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Microsoft PL-300 Fact Dimensions Query Dependencies Merge Append Keys And Loading Practice Test

Skills 1.3 • 30 original questions

This Microsoft PL-300 Power BI Data Analyst practice test focuses on fact dimensions query dependencies merge append keys and loading through original scenario-based questions aligned to the skills measured as of April 20, 2026. Use the full ExamSnap PL-300 collection for broader practice across all current skill areas. For broader exam preparation, review the Microsoft PL-300 Exam Dumps page.

Instructions: Select the best answer for each question. Review the explanation after answering; each distractor includes a reason it is not the best choice for that scenario.

Question 1

During a semantic model modernization at Tailspin Toys, the self-service BI administrator must shape source data into a star schema with fact and dimension tables. Which action most directly satisfies the requirement for the regional workspace, analysis cycle 1?

Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required
Merge queries to join columns from related tables or append queries to stack compatible rows
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables
Create or select a stable key that uniquely identifies the appropriate side of a relationship
Enable or disable load and configure query loading so only required tables enter the model

Correct answer: C

Why: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. This directly addresses the stated requirement: shape source data into a star schema with fact and dimension tables.

Option review:

A: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: shape source data into a star schema with fact and dimension tables.

B: Merge performs a join by key, whereas append combines rows from tables with compatible structures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: shape source data into a star schema with fact and dimension tables.

C: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. This directly addresses the stated requirement: shape source data into a star schema with fact and dimension tables.

D: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: shape source data into a star schema with fact and dimension tables.

E: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: shape source data into a star schema with fact and dimension tables.

Learning point: Separate transactional facts from descriptive dimensions and load them as fact and dimension tables

Question 2

Alpine Ski House is revising its analytics solution during a self-service analytics rollout. The team needs to choose between a dependent reference query and an independent duplicate query. Which Power BI action should the data analyst choose for the inventory model, analysis cycle 1?

Enable or disable load and configure query loading so only required tables enter the model
Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required
Merge queries to join columns from related tables or append queries to stack compatible rows
Create or select a stable key that uniquely identifies the appropriate side of a relationship
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables

Correct answer: B

Why: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. This directly addresses the stated requirement: choose between a dependent reference query and an independent duplicate query.

Option review:

A: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose between a dependent reference query and an independent duplicate query.

B: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. This directly addresses the stated requirement: choose between a dependent reference query and an independent duplicate query.

C: Merge performs a join by key, whereas append combines rows from tables with compatible structures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose between a dependent reference query and an independent duplicate query.

D: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose between a dependent reference query and an independent duplicate query.

E: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose between a dependent reference query and an independent duplicate query.

Learning point: Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required

Question 3

A design review for the service-level dashboard, analysis cycle 1 at Wide World Importers identifies one required capability: combine tables correctly by joining related columns or stacking similar rows. Which implementation is the strongest fit?

Merge queries to join columns from related tables or append queries to stack compatible rows
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables
Create or select a stable key that uniquely identifies the appropriate side of a relationship
Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required
Enable or disable load and configure query loading so only required tables enter the model

Correct answer: A

Why: Merge performs a join by key, whereas append combines rows from tables with compatible structures. This directly addresses the stated requirement: combine tables correctly by joining related columns or stacking similar rows.

Option review:

A: Merge performs a join by key, whereas append combines rows from tables with compatible structures. This directly addresses the stated requirement: combine tables correctly by joining related columns or stacking similar rows.

B: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: combine tables correctly by joining related columns or stacking similar rows.

C: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: combine tables correctly by joining related columns or stacking similar rows.

D: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: combine tables correctly by joining related columns or stacking similar rows.

E: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: combine tables correctly by joining related columns or stacking similar rows.

Learning point: Merge queries to join columns from related tables or append queries to stack compatible rows

Question 4

For the forecast report, analysis cycle 1, Northwind Traders wants the least indirect way to choose a reliable key that supports the intended table relationship. Which Power BI feature or action should the analytics lead select?

Create or select a stable key that uniquely identifies the appropriate side of a relationship
Merge queries to join columns from related tables or append queries to stack compatible rows
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables
Enable or disable load and configure query loading so only required tables enter the model
Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required

Correct answer: A

Why: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. This directly addresses the stated requirement: choose a reliable key that supports the intended table relationship.

Option review:

A: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. This directly addresses the stated requirement: choose a reliable key that supports the intended table relationship.

B: Merge performs a join by key, whereas append combines rows from tables with compatible structures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose a reliable key that supports the intended table relationship.

C: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose a reliable key that supports the intended table relationship.

D: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose a reliable key that supports the intended table relationship.

E: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose a reliable key that supports the intended table relationship.

Learning point: Create or select a stable key that uniquely identifies the appropriate side of a relationship

Question 5

The BI developer at Tailspin Toys is comparing several approaches for a data-quality remediation. The chosen approach must control which prepared queries are actually loaded into the semantic model. Which option best meets that condition?

Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required
Create or select a stable key that uniquely identifies the appropriate side of a relationship
Merge queries to join columns from related tables or append queries to stack compatible rows
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables
Enable or disable load and configure query loading so only required tables enter the model

Correct answer: E

Why: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. This directly addresses the stated requirement: control which prepared queries are actually loaded into the semantic model.

Option review:

A: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: control which prepared queries are actually loaded into the semantic model.

B: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: control which prepared queries are actually loaded into the semantic model.

C: Merge performs a join by key, whereas append combines rows from tables with compatible structures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: control which prepared queries are actually loaded into the semantic model.

D: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: control which prepared queries are actually loaded into the semantic model.

E: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. This directly addresses the stated requirement: control which prepared queries are actually loaded into the semantic model.

Learning point: Enable or disable load and configure query loading so only required tables enter the model

Question 6

A support escalation at Alpine Ski House has been narrowed to one requirement: shape source data into a star schema with fact and dimension tables. Which configuration should be investigated first for the executive report, analysis cycle 2?

Create or select a stable key that uniquely identifies the appropriate side of a relationship
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables
Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required
Enable or disable load and configure query loading so only required tables enter the model
Merge queries to join columns from related tables or append queries to stack compatible rows

Correct answer: B

Option review:

A: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: shape source data into a star schema with fact and dimension tables.

B: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. This directly addresses the stated requirement: shape source data into a star schema with fact and dimension tables.

C: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: shape source data into a star schema with fact and dimension tables.

D: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: shape source data into a star schema with fact and dimension tables.

E: Merge performs a join by key, whereas append combines rows from tables with compatible structures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: shape source data into a star schema with fact and dimension tables.

Learning point: Separate transactional facts from descriptive dimensions and load them as fact and dimension tables

Question 7

An analytics governance review at Wide World Importers asks the self-service BI administrator to choose between a dependent reference query and an independent duplicate query. Which action aligns most directly with that requirement?

Enable or disable load and configure query loading so only required tables enter the model
Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables
Create or select a stable key that uniquely identifies the appropriate side of a relationship
Merge queries to join columns from related tables or append queries to stack compatible rows

Correct answer: B

Option review:

C: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose between a dependent reference query and an independent duplicate query.

D: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose between a dependent reference query and an independent duplicate query.

E: Merge performs a join by key, whereas append combines rows from tables with compatible structures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose between a dependent reference query and an independent duplicate query.

Learning point: Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required

Question 8

Before the finance semantic model, analysis cycle 2 is released, the analytics team must combine tables correctly by joining related columns or stacking similar rows. Which Power BI implementation should be added?

Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required
Merge queries to join columns from related tables or append queries to stack compatible rows
Create or select a stable key that uniquely identifies the appropriate side of a relationship
Enable or disable load and configure query loading so only required tables enter the model
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables

Correct answer: B

Option review:

A: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: combine tables correctly by joining related columns or stacking similar rows.

B: Merge performs a join by key, whereas append combines rows from tables with compatible structures. This directly addresses the stated requirement: combine tables correctly by joining related columns or stacking similar rows.

D: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: combine tables correctly by joining related columns or stacking similar rows.

E: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: combine tables correctly by joining related columns or stacking similar rows.

Learning point: Merge queries to join columns from related tables or append queries to stack compatible rows

Question 9

Tailspin Toys is replacing a manual analytics process. The replacement must reliably choose a reliable key that supports the intended table relationship. Which choice should be implemented for the operations dashboard, analysis cycle 2?

Merge queries to join columns from related tables or append queries to stack compatible rows
Create or select a stable key that uniquely identifies the appropriate side of a relationship
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables
Enable or disable load and configure query loading so only required tables enter the model
Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required

Correct answer: B

Option review:

A: Merge performs a join by key, whereas append combines rows from tables with compatible structures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose a reliable key that supports the intended table relationship.

B: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. This directly addresses the stated requirement: choose a reliable key that supports the intended table relationship.

Learning point: Create or select a stable key that uniquely identifies the appropriate side of a relationship

Question 10

Which Power BI action best matches this technical purpose for the customer report, analysis cycle 2: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations.

Create or select a stable key that uniquely identifies the appropriate side of a relationship
Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables
Merge queries to join columns from related tables or append queries to stack compatible rows
Enable or disable load and configure query loading so only required tables enter the model

Correct answer: E

Why: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. This directly addresses the stated requirement: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations..

Option review:

A: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations..

B: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations..

C: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations..

D: Merge performs a join by key, whereas append combines rows from tables with compatible structures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations..

E: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. This directly addresses the stated requirement: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations..

Learning point: Enable or disable load and configure query loading so only required tables enter the model

Question 11

A runbook for the regional workspace, analysis cycle 3 contains this description: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. Which Power BI feature or action belongs in the runbook?

Enable or disable load and configure query loading so only required tables enter the model
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables
Merge queries to join columns from related tables or append queries to stack compatible rows
Create or select a stable key that uniquely identifies the appropriate side of a relationship
Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required

Correct answer: B

Why: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. This directly addresses the stated requirement: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior..

Option review:

A: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior..

B: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. This directly addresses the stated requirement: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior..

C: Merge performs a join by key, whereas append combines rows from tables with compatible structures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior..

D: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior..

E: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior..

Learning point: Separate transactional facts from descriptive dimensions and load them as fact and dimension tables

Question 12

During validation of the inventory model, analysis cycle 3, the Power BI data analyst needs a capability that behaves as follows: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. Which choice is correct?

Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required
Enable or disable load and configure query loading so only required tables enter the model
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables
Merge queries to join columns from related tables or append queries to stack compatible rows
Create or select a stable key that uniquely identifies the appropriate side of a relationship

Correct answer: A

Why: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. This directly addresses the stated requirement: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently..

Option review:

A: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. This directly addresses the stated requirement: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently..

B: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently..

C: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently..

D: Merge performs a join by key, whereas append combines rows from tables with compatible structures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently..

E: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently..

Learning point: Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required

Question 13

A stakeholder asks why a particular Power BI feature should be used for the service-level dashboard, analysis cycle 3. The required behavior is: Merge performs a join by key, whereas append combines rows from tables with compatible structures. Which action provides that behavior?

Enable or disable load and configure query loading so only required tables enter the model
Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required
Create or select a stable key that uniquely identifies the appropriate side of a relationship
Merge queries to join columns from related tables or append queries to stack compatible rows
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables

Correct answer: D

Why: Merge performs a join by key, whereas append combines rows from tables with compatible structures. This directly addresses the stated requirement: Merge performs a join by key, whereas append combines rows from tables with compatible structures..

Option review:

A: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Merge performs a join by key, whereas append combines rows from tables with compatible structures..

B: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Merge performs a join by key, whereas append combines rows from tables with compatible structures..

C: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Merge performs a join by key, whereas append combines rows from tables with compatible structures..

D: Merge performs a join by key, whereas append combines rows from tables with compatible structures. This directly addresses the stated requirement: Merge performs a join by key, whereas append combines rows from tables with compatible structures..

E: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Merge performs a join by key, whereas append combines rows from tables with compatible structures..

Learning point: Merge queries to join columns from related tables or append queries to stack compatible rows

Question 14

The forecast report, analysis cycle 3 is moving to production at Alpine Ski House. Which action should be approved when the goal is to choose a reliable key that supports the intended table relationship?

Create or select a stable key that uniquely identifies the appropriate side of a relationship
Enable or disable load and configure query loading so only required tables enter the model
Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables
Merge queries to join columns from related tables or append queries to stack compatible rows

Correct answer: A

Option review:

B: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose a reliable key that supports the intended table relationship.

C: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose a reliable key that supports the intended table relationship.

D: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose a reliable key that supports the intended table relationship.

E: Merge performs a join by key, whereas append combines rows from tables with compatible structures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose a reliable key that supports the intended table relationship.

Learning point: Create or select a stable key that uniquely identifies the appropriate side of a relationship

Question 15

A data analyst at Wide World Importers must satisfy this acceptance criterion for the mobile report, analysis cycle 3: control which prepared queries are actually loaded into the semantic model. Which implementation is most appropriate?

Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required
Create or select a stable key that uniquely identifies the appropriate side of a relationship
Enable or disable load and configure query loading so only required tables enter the model
Merge queries to join columns from related tables or append queries to stack compatible rows
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables

Correct answer: C

Option review:

A: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: control which prepared queries are actually loaded into the semantic model.

C: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. This directly addresses the stated requirement: control which prepared queries are actually loaded into the semantic model.

D: Merge performs a join by key, whereas append combines rows from tables with compatible structures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: control which prepared queries are actually loaded into the semantic model.

E: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: control which prepared queries are actually loaded into the semantic model.

Learning point: Enable or disable load and configure query loading so only required tables enter the model

Question 16

A self-service analytics rollout at Northwind Traders can proceed only after the team can shape source data into a star schema with fact and dimension tables. What should the analytics lead configure?

Merge queries to join columns from related tables or append queries to stack compatible rows
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables
Create or select a stable key that uniquely identifies the appropriate side of a relationship
Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required
Enable or disable load and configure query loading so only required tables enter the model

Correct answer: B

Option review:

A: Merge performs a join by key, whereas append combines rows from tables with compatible structures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: shape source data into a star schema with fact and dimension tables.

C: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: shape source data into a star schema with fact and dimension tables.

D: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: shape source data into a star schema with fact and dimension tables.

Learning point: Separate transactional facts from descriptive dimensions and load them as fact and dimension tables

Question 17

The analytics team at Tailspin Toys has ruled out unrelated redesign work. Which action directly enables the team to choose between a dependent reference query and an independent duplicate query for the sales model, analysis cycle 4?

Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables
Enable or disable load and configure query loading so only required tables enter the model
Merge queries to join columns from related tables or append queries to stack compatible rows
Create or select a stable key that uniquely identifies the appropriate side of a relationship

Correct answer: A

Option review:

A: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. This directly addresses the stated requirement: choose between a dependent reference query and an independent duplicate query.

B: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose between a dependent reference query and an independent duplicate query.

C: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose between a dependent reference query and an independent duplicate query.

D: Merge performs a join by key, whereas append combines rows from tables with compatible structures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose between a dependent reference query and an independent duplicate query.

E: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose between a dependent reference query and an independent duplicate query.

Learning point: Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required

Question 18

An audit finding for the finance semantic model, analysis cycle 4 says the current design cannot combine tables correctly by joining related columns or stacking similar rows. Which Power BI action most directly closes the gap?

Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required
Create or select a stable key that uniquely identifies the appropriate side of a relationship
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables
Enable or disable load and configure query loading so only required tables enter the model
Merge queries to join columns from related tables or append queries to stack compatible rows

Correct answer: E

Option review:

A: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: combine tables correctly by joining related columns or stacking similar rows.

B: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: combine tables correctly by joining related columns or stacking similar rows.

C: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: combine tables correctly by joining related columns or stacking similar rows.

D: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: combine tables correctly by joining related columns or stacking similar rows.

E: Merge performs a join by key, whereas append combines rows from tables with compatible structures. This directly addresses the stated requirement: combine tables correctly by joining related columns or stacking similar rows.

Learning point: Merge queries to join columns from related tables or append queries to stack compatible rows

Question 19

For the operations dashboard, analysis cycle 4, the self-service BI administrator needs a repeatable solution that will choose a reliable key that supports the intended table relationship. Which option should replace the current ad hoc process?

Separate transactional facts from descriptive dimensions and load them as fact and dimension tables
Merge queries to join columns from related tables or append queries to stack compatible rows
Enable or disable load and configure query loading so only required tables enter the model
Create or select a stable key that uniquely identifies the appropriate side of a relationship
Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required

Correct answer: D

Option review:

A: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose a reliable key that supports the intended table relationship.

B: Merge performs a join by key, whereas append combines rows from tables with compatible structures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose a reliable key that supports the intended table relationship.

C: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose a reliable key that supports the intended table relationship.

D: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. This directly addresses the stated requirement: choose a reliable key that supports the intended table relationship.

Learning point: Create or select a stable key that uniquely identifies the appropriate side of a relationship

Question 20

During a executive reporting rollout, Northwind Traders defines the desired outcome as follows: control which prepared queries are actually loaded into the semantic model. Which Power BI capability should the team use?

Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables
Create or select a stable key that uniquely identifies the appropriate side of a relationship
Enable or disable load and configure query loading so only required tables enter the model
Merge queries to join columns from related tables or append queries to stack compatible rows

Correct answer: D

Option review:

A: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: control which prepared queries are actually loaded into the semantic model.

B: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: control which prepared queries are actually loaded into the semantic model.

C: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: control which prepared queries are actually loaded into the semantic model.

D: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. This directly addresses the stated requirement: control which prepared queries are actually loaded into the semantic model.

E: Merge performs a join by key, whereas append combines rows from tables with compatible structures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: control which prepared queries are actually loaded into the semantic model.

Learning point: Enable or disable load and configure query loading so only required tables enter the model

Question 21

A new requirement is added to the regional workspace, analysis cycle 5: shape source data into a star schema with fact and dimension tables. Which action should the report author take?

Enable or disable load and configure query loading so only required tables enter the model
Create or select a stable key that uniquely identifies the appropriate side of a relationship
Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required
Merge queries to join columns from related tables or append queries to stack compatible rows
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables

Correct answer: E

Option review:

A: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: shape source data into a star schema with fact and dimension tables.

B: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: shape source data into a star schema with fact and dimension tables.

D: Merge performs a join by key, whereas append combines rows from tables with compatible structures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: shape source data into a star schema with fact and dimension tables.

E: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. This directly addresses the stated requirement: shape source data into a star schema with fact and dimension tables.

Learning point: Separate transactional facts from descriptive dimensions and load them as fact and dimension tables

Question 22

Alpine Ski House is troubleshooting an unexpected reporting result. The decisive requirement is to choose between a dependent reference query and an independent duplicate query. Which feature or configuration is most relevant?

Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required
Merge queries to join columns from related tables or append queries to stack compatible rows
Create or select a stable key that uniquely identifies the appropriate side of a relationship
Enable or disable load and configure query loading so only required tables enter the model
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables

Correct answer: A

Option review:

A: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. This directly addresses the stated requirement: choose between a dependent reference query and an independent duplicate query.

B: Merge performs a join by key, whereas append combines rows from tables with compatible structures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose between a dependent reference query and an independent duplicate query.

C: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose between a dependent reference query and an independent duplicate query.

D: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose between a dependent reference query and an independent duplicate query.

Learning point: Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required

Question 23

A technical workshop for the service-level dashboard, analysis cycle 5 documents this behavior: Merge performs a join by key, whereas append combines rows from tables with compatible structures. Which Power BI choice is being described?

Enable or disable load and configure query loading so only required tables enter the model
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables
Merge queries to join columns from related tables or append queries to stack compatible rows
Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required
Create or select a stable key that uniquely identifies the appropriate side of a relationship

Correct answer: C

Option review:

A: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Merge performs a join by key, whereas append combines rows from tables with compatible structures..

B: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Merge performs a join by key, whereas append combines rows from tables with compatible structures..

C: Merge performs a join by key, whereas append combines rows from tables with compatible structures. This directly addresses the stated requirement: Merge performs a join by key, whereas append combines rows from tables with compatible structures..

D: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Merge performs a join by key, whereas append combines rows from tables with compatible structures..

E: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Merge performs a join by key, whereas append combines rows from tables with compatible structures..

Learning point: Merge queries to join columns from related tables or append queries to stack compatible rows

Question 24

The Power BI data analyst must identify the Power BI capability that provides this function: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. Which answer is correct for the forecast report, analysis cycle 5?

Create or select a stable key that uniquely identifies the appropriate side of a relationship
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables
Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required
Enable or disable load and configure query loading so only required tables enter the model
Merge queries to join columns from related tables or append queries to stack compatible rows

Correct answer: A

Why: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. This directly addresses the stated requirement: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key..

Option review:

A: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. This directly addresses the stated requirement: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key..

B: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key..

C: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key..

D: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key..

E: Merge performs a join by key, whereas append combines rows from tables with compatible structures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key..

Learning point: Create or select a stable key that uniquely identifies the appropriate side of a relationship

Question 25

A modernization plan for the mobile report, analysis cycle 5 requires the team to control which prepared queries are actually loaded into the semantic model. Which Power BI action is the clearest fit?

Merge queries to join columns from related tables or append queries to stack compatible rows
Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required
Enable or disable load and configure query loading so only required tables enter the model
Create or select a stable key that uniquely identifies the appropriate side of a relationship
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables

Correct answer: C

Option review:

A: Merge performs a join by key, whereas append combines rows from tables with compatible structures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: control which prepared queries are actually loaded into the semantic model.

B: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: control which prepared queries are actually loaded into the semantic model.

D: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: control which prepared queries are actually loaded into the semantic model.

E: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: control which prepared queries are actually loaded into the semantic model.

Learning point: Enable or disable load and configure query loading so only required tables enter the model

Question 26

The analytics lead at Alpine Ski House is creating a standard for the executive report, analysis cycle 6. The standard must shape source data into a star schema with fact and dimension tables. Which feature should be documented?

Enable or disable load and configure query loading so only required tables enter the model
Merge queries to join columns from related tables or append queries to stack compatible rows
Create or select a stable key that uniquely identifies the appropriate side of a relationship
Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables

Correct answer: E

Option review:

A: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: shape source data into a star schema with fact and dimension tables.

C: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: shape source data into a star schema with fact and dimension tables.

D: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: shape source data into a star schema with fact and dimension tables.

Learning point: Separate transactional facts from descriptive dimensions and load them as fact and dimension tables

Question 27

Which implementation should Wide World Importers use for the sales model, analysis cycle 6 when the business requirement is to choose between a dependent reference query and an independent duplicate query?

Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables
Create or select a stable key that uniquely identifies the appropriate side of a relationship
Enable or disable load and configure query loading so only required tables enter the model
Merge queries to join columns from related tables or append queries to stack compatible rows

Correct answer: A

Option review:

A: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. This directly addresses the stated requirement: choose between a dependent reference query and an independent duplicate query.

B: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose between a dependent reference query and an independent duplicate query.

C: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose between a dependent reference query and an independent duplicate query.

D: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose between a dependent reference query and an independent duplicate query.

E: Merge performs a join by key, whereas append combines rows from tables with compatible structures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: choose between a dependent reference query and an independent duplicate query.

Learning point: Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required

Question 28

A pilot review at Northwind Traders finds that users still cannot combine tables correctly by joining related columns or stacking similar rows. Which action should be completed before the finance semantic model, analysis cycle 6 is expanded?

Create or select a stable key that uniquely identifies the appropriate side of a relationship
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables
Enable or disable load and configure query loading so only required tables enter the model
Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required
Merge queries to join columns from related tables or append queries to stack compatible rows

Correct answer: E

Option review:

A: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: combine tables correctly by joining related columns or stacking similar rows.

C: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: combine tables correctly by joining related columns or stacking similar rows.

Learning point: Merge queries to join columns from related tables or append queries to stack compatible rows

Question 29

The BI developer needs to justify a Power BI design decision for the operations dashboard, analysis cycle 6. The feature must provide this behavior: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. Which selection is most defensible?

Create or select a stable key that uniquely identifies the appropriate side of a relationship
Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required
Merge queries to join columns from related tables or append queries to stack compatible rows
Enable or disable load and configure query loading so only required tables enter the model
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables

Correct answer: A

Why: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. This directly addresses the stated requirement: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key..

Option review:

A: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key. This directly addresses the stated requirement: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key..

B: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key..

C: Merge performs a join by key, whereas append combines rows from tables with compatible structures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key..

D: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key..

E: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Relationships depend on reliable keys; a dimension-side key should normally be unique and compatible with the matching foreign key..

Learning point: Create or select a stable key that uniquely identifies the appropriate side of a relationship

Question 30

For a customer analytics initiative, the customer report, analysis cycle 6 must support the ability to control which prepared queries are actually loaded into the semantic model. Which option is technically aligned with that goal?

Enable or disable load and configure query loading so only required tables enter the model
Create or select a stable key that uniquely identifies the appropriate side of a relationship
Separate transactional facts from descriptive dimensions and load them as fact and dimension tables
Use a reference query when a dependent query should inherit upstream steps, and duplicate when an independent copy is required
Merge queries to join columns from related tables or append queries to stack compatible rows

Correct answer: A

Option review:

A: Query load settings prevent staging or helper queries from unnecessarily becoming model tables while still allowing them to support downstream transformations. This directly addresses the stated requirement: control which prepared queries are actually loaded into the semantic model.

C: A star schema places measurable events in fact tables and descriptive attributes in dimensions, improving usability and model behavior. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: control which prepared queries are actually loaded into the semantic model.

D: Reference queries depend on another query result, while duplicate copies the existing steps and then evolves independently. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: control which prepared queries are actually loaded into the semantic model.

E: Merge performs a join by key, whereas append combines rows from tables with compatible structures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: control which prepared queries are actually loaded into the semantic model.

Learning point: Enable or disable load and configure query loading so only required tables enter the model

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