Practice Exams:

View All

Microsoft PL-300 Model Properties Role Playing Dimensions And Relationships Practice Test

Skills 2.1 • 25 original questions

This Microsoft PL-300 Power BI Data Analyst practice test focuses on model properties role playing dimensions and relationships 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 self-service analytics rollout at Woodgrove Bank, the Power BI data analyst must configure model metadata so fields behave and appear correctly to report authors. Which action most directly satisfies the requirement for the inventory model, analysis cycle 1?

Configure table and column properties such as names, descriptions, data categories, summarization, and visibility
Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design
Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Create and mark a common date table that supports consistent time analysis across facts
Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables

Correct answer: A

Why: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience. This directly addresses the stated requirement: configure model metadata so fields behave and appear correctly to report authors.

Option review:

A: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience. This directly addresses the stated requirement: configure model metadata so fields behave and appear correctly to report authors.

B: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure model metadata so fields behave and appear correctly to report authors.

C: Calculated columns and tables are computed during refresh and stored in the model, unlike measures that evaluate at query time. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure model metadata so fields behave and appear correctly to report authors.

D: A dedicated date dimension provides complete calendar attributes and a consistent basis for DAX time intelligence and report filtering. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure model metadata so fields behave and appear correctly to report authors.

E: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure model metadata so fields behave and appear correctly to report authors.

Learning point: Configure table and column properties such as names, descriptions, data categories, summarization, and visibility

Question 2

Blue Yonder Airlines is revising its analytics solution during a monthly KPI review. The team needs to model one business dimension when it must serve multiple analytical roles. Which Power BI action should the self-service BI administrator choose for the service-level dashboard, analysis cycle 1?

Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design
Create and mark a common date table that supports consistent time analysis across facts
Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables
Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Configure table and column properties such as names, descriptions, data categories, summarization, and visibility

Correct answer: A

Why: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous. This directly addresses the stated requirement: model one business dimension when it must serve multiple analytical roles.

Option review:

A: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous. This directly addresses the stated requirement: model one business dimension when it must serve multiple analytical roles.

B: A dedicated date dimension provides complete calendar attributes and a consistent basis for DAX time intelligence and report filtering. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: model one business dimension when it must serve multiple analytical roles.

C: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: model one business dimension when it must serve multiple analytical roles.

D: Calculated columns and tables are computed during refresh and stored in the model, unlike measures that evaluate at query time. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: model one business dimension when it must serve multiple analytical roles.

E: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: model one business dimension when it must serve multiple analytical roles.

Learning point: Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design

Question 3

A design review for the forecast report, analysis cycle 1 at Contoso Retail identifies one required capability: configure relationship uniqueness and filter propagation correctly. Which implementation is the strongest fit?

Create and mark a common date table that supports consistent time analysis across facts
Configure table and column properties such as names, descriptions, data categories, summarization, and visibility
Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design
Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables

Correct answer: E

Why: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model. This directly addresses the stated requirement: configure relationship uniqueness and filter propagation correctly.

Option review:

A: A dedicated date dimension provides complete calendar attributes and a consistent basis for DAX time intelligence and report filtering. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure relationship uniqueness and filter propagation correctly.

B: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure relationship uniqueness and filter propagation correctly.

C: Calculated columns and tables are computed during refresh and stored in the model, unlike measures that evaluate at query time. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure relationship uniqueness and filter propagation correctly.

D: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure relationship uniqueness and filter propagation correctly.

E: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model. This directly addresses the stated requirement: configure relationship uniqueness and filter propagation correctly.

Learning point: Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables

Question 4

For the mobile report, analysis cycle 2, Litware Finance wants the least indirect way to configure model metadata so fields behave and appear correctly to report authors. Which Power BI feature or action should the report author select?

Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design
Configure table and column properties such as names, descriptions, data categories, summarization, and visibility
Create and mark a common date table that supports consistent time analysis across facts
Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables

Correct answer: C

Option review:

A: Calculated columns and tables are computed during refresh and stored in the model, unlike measures that evaluate at query time. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure model metadata so fields behave and appear correctly to report authors.

C: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience. This directly addresses the stated requirement: configure model metadata so fields behave and appear correctly to report authors.

Learning point: Configure table and column properties such as names, descriptions, data categories, summarization, and visibility

Question 5

The analytics lead at Woodgrove Bank is comparing several approaches for a finance dashboard refresh. The chosen approach must model one business dimension when it must serve multiple analytical roles. Which option best meets that condition?

Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables
Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design
Create and mark a common date table that supports consistent time analysis across facts
Configure table and column properties such as names, descriptions, data categories, summarization, and visibility

Correct answer: C

Option review:

A: Calculated columns and tables are computed during refresh and stored in the model, unlike measures that evaluate at query time. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: model one business dimension when it must serve multiple analytical roles.

B: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: model one business dimension when it must serve multiple analytical roles.

C: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous. This directly addresses the stated requirement: model one business dimension when it must serve multiple analytical roles.

D: A dedicated date dimension provides complete calendar attributes and a consistent basis for DAX time intelligence and report filtering. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: model one business dimension when it must serve multiple analytical roles.

Learning point: Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design

Question 6

A support escalation at Blue Yonder Airlines has been narrowed to one requirement: configure relationship uniqueness and filter propagation correctly. Which configuration should be investigated first for the sales model, analysis cycle 2?

Create and mark a common date table that supports consistent time analysis across facts
Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design
Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables
Configure table and column properties such as names, descriptions, data categories, summarization, and visibility
Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required

Correct answer: C

Option review:

B: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure relationship uniqueness and filter propagation correctly.

C: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model. This directly addresses the stated requirement: configure relationship uniqueness and filter propagation correctly.

D: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure relationship uniqueness and filter propagation correctly.

E: Calculated columns and tables are computed during refresh and stored in the model, unlike measures that evaluate at query time. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure relationship uniqueness and filter propagation correctly.

Learning point: Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables

Question 7

An analytics governance review at Contoso Retail asks the Power BI data analyst to configure model metadata so fields behave and appear correctly to report authors. Which action aligns most directly with that requirement?

Create and mark a common date table that supports consistent time analysis across facts
Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables
Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design
Configure table and column properties such as names, descriptions, data categories, summarization, and visibility

Correct answer: E

Option review:

A: A dedicated date dimension provides complete calendar attributes and a consistent basis for DAX time intelligence and report filtering. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure model metadata so fields behave and appear correctly to report authors.

B: Calculated columns and tables are computed during refresh and stored in the model, unlike measures that evaluate at query time. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure model metadata so fields behave and appear correctly to report authors.

C: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure model metadata so fields behave and appear correctly to report authors.

D: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure model metadata so fields behave and appear correctly to report authors.

E: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience. This directly addresses the stated requirement: configure model metadata so fields behave and appear correctly to report authors.

Learning point: Configure table and column properties such as names, descriptions, data categories, summarization, and visibility

Question 8

Before the operations dashboard, analysis cycle 3 is released, the analytics team must model one business dimension when it must serve multiple analytical roles. Which Power BI implementation should be added?

Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables
Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design
Create and mark a common date table that supports consistent time analysis across facts
Configure table and column properties such as names, descriptions, data categories, summarization, and visibility

Correct answer: C

Option review:

A: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: model one business dimension when it must serve multiple analytical roles.

B: Calculated columns and tables are computed during refresh and stored in the model, unlike measures that evaluate at query time. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: model one business dimension when it must serve multiple analytical roles.

D: A dedicated date dimension provides complete calendar attributes and a consistent basis for DAX time intelligence and report filtering. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: model one business dimension when it must serve multiple analytical roles.

Learning point: Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design

Question 9

Woodgrove Bank is replacing a manual analytics process. The replacement must reliably configure relationship uniqueness and filter propagation correctly. Which choice should be implemented for the customer report, analysis cycle 3?

Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Configure table and column properties such as names, descriptions, data categories, summarization, and visibility
Create and mark a common date table that supports consistent time analysis across facts
Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables
Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design

Correct answer: D

Option review:

A: Calculated columns and tables are computed during refresh and stored in the model, unlike measures that evaluate at query time. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure relationship uniqueness and filter propagation correctly.

C: A dedicated date dimension provides complete calendar attributes and a consistent basis for DAX time intelligence and report filtering. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure relationship uniqueness and filter propagation correctly.

D: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model. This directly addresses the stated requirement: configure relationship uniqueness and filter propagation correctly.

E: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure relationship uniqueness and filter propagation correctly.

Learning point: Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables

Question 10

Which Power BI action best matches this technical purpose for the regional workspace, analysis cycle 4: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience.

Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Configure table and column properties such as names, descriptions, data categories, summarization, and visibility
Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design
Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables
Create and mark a common date table that supports consistent time analysis across facts

Correct answer: B

Why: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience. This directly addresses the stated requirement: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience..

Option review:

A: Calculated columns and tables are computed during refresh and stored in the model, unlike measures that evaluate at query time. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience..

B: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience. This directly addresses the stated requirement: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience..

C: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience..

D: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience..

E: A dedicated date dimension provides complete calendar attributes and a consistent basis for DAX time intelligence and report filtering. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience..

Learning point: Configure table and column properties such as names, descriptions, data categories, summarization, and visibility

Question 11

A runbook for the inventory model, analysis cycle 4 contains this description: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous. Which Power BI feature or action belongs in the runbook?

Configure table and column properties such as names, descriptions, data categories, summarization, and visibility
Create and mark a common date table that supports consistent time analysis across facts
Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design
Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables

Correct answer: C

Why: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous. This directly addresses the stated requirement: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous..

Option review:

A: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous..

B: A dedicated date dimension provides complete calendar attributes and a consistent basis for DAX time intelligence and report filtering. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous..

C: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous. This directly addresses the stated requirement: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous..

D: Calculated columns and tables are computed during refresh and stored in the model, unlike measures that evaluate at query time. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous..

E: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous..

Learning point: Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design

Question 12

During validation of the service-level dashboard, analysis cycle 4, the BI developer needs a capability that behaves as follows: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model. Which choice is correct?

Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables
Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design
Create and mark a common date table that supports consistent time analysis across facts
Configure table and column properties such as names, descriptions, data categories, summarization, and visibility

Correct answer: A

Why: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model. This directly addresses the stated requirement: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model..

Option review:

A: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model. This directly addresses the stated requirement: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model..

B: Calculated columns and tables are computed during refresh and stored in the model, unlike measures that evaluate at query time. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model..

C: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model..

D: A dedicated date dimension provides complete calendar attributes and a consistent basis for DAX time intelligence and report filtering. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model..

E: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model..

Learning point: Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables

Question 13

A stakeholder asks why a particular Power BI feature should be used for the forecast report, analysis cycle 5. The required behavior is: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience. Which action provides that behavior?

Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Create and mark a common date table that supports consistent time analysis across facts
Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design
Configure table and column properties such as names, descriptions, data categories, summarization, and visibility
Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables

Correct answer: D

Why: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience. This directly addresses the stated requirement: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience..

Option review:

A: Calculated columns and tables are computed during refresh and stored in the model, unlike measures that evaluate at query time. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience..

B: A dedicated date dimension provides complete calendar attributes and a consistent basis for DAX time intelligence and report filtering. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience..

D: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience. This directly addresses the stated requirement: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience..

E: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience..

Learning point: Configure table and column properties such as names, descriptions, data categories, summarization, and visibility

Question 14

The mobile report, analysis cycle 5 is moving to production at Blue Yonder Airlines. Which action should be approved when the goal is to model one business dimension when it must serve multiple analytical roles?

Configure table and column properties such as names, descriptions, data categories, summarization, and visibility
Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables
Create and mark a common date table that supports consistent time analysis across facts
Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design

Correct answer: E

Option review:

A: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: model one business dimension when it must serve multiple analytical roles.

B: Calculated columns and tables are computed during refresh and stored in the model, unlike measures that evaluate at query time. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: model one business dimension when it must serve multiple analytical roles.

D: A dedicated date dimension provides complete calendar attributes and a consistent basis for DAX time intelligence and report filtering. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: model one business dimension when it must serve multiple analytical roles.

E: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous. This directly addresses the stated requirement: model one business dimension when it must serve multiple analytical roles.

Learning point: Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design

Question 15

A data analyst at Contoso Retail must satisfy this acceptance criterion for the executive report, analysis cycle 5: configure relationship uniqueness and filter propagation correctly. Which implementation is most appropriate?

Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design
Create and mark a common date table that supports consistent time analysis across facts
Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables
Configure table and column properties such as names, descriptions, data categories, summarization, and visibility

Correct answer: D

Option review:

A: Calculated columns and tables are computed during refresh and stored in the model, unlike measures that evaluate at query time. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure relationship uniqueness and filter propagation correctly.

B: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure relationship uniqueness and filter propagation correctly.

E: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure relationship uniqueness and filter propagation correctly.

Learning point: Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables

Question 16

A sales analytics project at Litware Finance can proceed only after the team can configure model metadata so fields behave and appear correctly to report authors. What should the report author configure?

Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design
Configure table and column properties such as names, descriptions, data categories, summarization, and visibility
Create and mark a common date table that supports consistent time analysis across facts
Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables

Correct answer: B

Option review:

A: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure model metadata so fields behave and appear correctly to report authors.

B: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience. This directly addresses the stated requirement: configure model metadata so fields behave and appear correctly to report authors.

C: A dedicated date dimension provides complete calendar attributes and a consistent basis for DAX time intelligence and report filtering. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure model metadata so fields behave and appear correctly to report authors.

D: Calculated columns and tables are computed during refresh and stored in the model, unlike measures that evaluate at query time. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure model metadata so fields behave and appear correctly to report authors.

Learning point: Configure table and column properties such as names, descriptions, data categories, summarization, and visibility

Question 17

The analytics team at Woodgrove Bank has ruled out unrelated redesign work. Which action directly enables the team to model one business dimension when it must serve multiple analytical roles for the finance semantic model, analysis cycle 6?

Create and mark a common date table that supports consistent time analysis across facts
Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables
Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design
Configure table and column properties such as names, descriptions, data categories, summarization, and visibility

Correct answer: D

Option review:

A: A dedicated date dimension provides complete calendar attributes and a consistent basis for DAX time intelligence and report filtering. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: model one business dimension when it must serve multiple analytical roles.

C: Calculated columns and tables are computed during refresh and stored in the model, unlike measures that evaluate at query time. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: model one business dimension when it must serve multiple analytical roles.

D: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous. This directly addresses the stated requirement: model one business dimension when it must serve multiple analytical roles.

Learning point: Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design

Question 18

An audit finding for the operations dashboard, analysis cycle 6 says the current design cannot configure relationship uniqueness and filter propagation correctly. Which Power BI action most directly closes the gap?

Create and mark a common date table that supports consistent time analysis across facts
Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables
Configure table and column properties such as names, descriptions, data categories, summarization, and visibility
Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design

Correct answer: B

Option review:

B: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model. This directly addresses the stated requirement: configure relationship uniqueness and filter propagation correctly.

C: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure relationship uniqueness and filter propagation correctly.

D: Calculated columns and tables are computed during refresh and stored in the model, unlike measures that evaluate at query time. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure relationship uniqueness and filter propagation correctly.

Learning point: Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables

Question 19

For the customer report, analysis cycle 7, the Power BI data analyst needs a repeatable solution that will configure model metadata so fields behave and appear correctly to report authors. Which option should replace the current ad hoc process?

Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables
Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Create and mark a common date table that supports consistent time analysis across facts
Configure table and column properties such as names, descriptions, data categories, summarization, and visibility
Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design

Correct answer: D

Option review:

A: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure model metadata so fields behave and appear correctly to report authors.

C: A dedicated date dimension provides complete calendar attributes and a consistent basis for DAX time intelligence and report filtering. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure model metadata so fields behave and appear correctly to report authors.

D: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience. This directly addresses the stated requirement: configure model metadata so fields behave and appear correctly to report authors.

E: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure model metadata so fields behave and appear correctly to report authors.

Learning point: Configure table and column properties such as names, descriptions, data categories, summarization, and visibility

Question 20

During a monthly KPI review, Litware Finance defines the desired outcome as follows: model one business dimension when it must serve multiple analytical roles. Which Power BI capability should the team use?

Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables
Create and mark a common date table that supports consistent time analysis across facts
Configure table and column properties such as names, descriptions, data categories, summarization, and visibility
Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design

Correct answer: E

Option review:

C: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: model one business dimension when it must serve multiple analytical roles.

Learning point: Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design

Question 21

A new requirement is added to the inventory model, analysis cycle 7: configure relationship uniqueness and filter propagation correctly. Which action should the data analyst take?

Create and mark a common date table that supports consistent time analysis across facts
Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables
Configure table and column properties such as names, descriptions, data categories, summarization, and visibility
Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design

Correct answer: B

Option review:

D: Calculated columns and tables are computed during refresh and stored in the model, unlike measures that evaluate at query time. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure relationship uniqueness and filter propagation correctly.

Learning point: Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables

Question 22

Blue Yonder Airlines is troubleshooting an unexpected reporting result. The decisive requirement is to configure model metadata so fields behave and appear correctly to report authors. Which feature or configuration is most relevant?

Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Configure table and column properties such as names, descriptions, data categories, summarization, and visibility
Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables
Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design
Create and mark a common date table that supports consistent time analysis across facts

Correct answer: B

Option review:

B: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience. This directly addresses the stated requirement: configure model metadata so fields behave and appear correctly to report authors.

C: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure model metadata so fields behave and appear correctly to report authors.

D: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure model metadata so fields behave and appear correctly to report authors.

E: A dedicated date dimension provides complete calendar attributes and a consistent basis for DAX time intelligence and report filtering. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure model metadata so fields behave and appear correctly to report authors.

Learning point: Configure table and column properties such as names, descriptions, data categories, summarization, and visibility

Question 23

A technical workshop for the forecast report, analysis cycle 8 documents this behavior: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous. Which Power BI choice is being described?

Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables
Create and mark a common date table that supports consistent time analysis across facts
Configure table and column properties such as names, descriptions, data categories, summarization, and visibility
Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design

Correct answer: E

Why: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous. This directly addresses the stated requirement: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous..

Option review:

A: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous..

B: A dedicated date dimension provides complete calendar attributes and a consistent basis for DAX time intelligence and report filtering. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous..

C: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous..

D: Calculated columns and tables are computed during refresh and stored in the model, unlike measures that evaluate at query time. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous..

E: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous. This directly addresses the stated requirement: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous..

Learning point: Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design

Question 24

The BI developer must identify the Power BI capability that provides this function: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model. Which answer is correct for the mobile report, analysis cycle 8?

Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables
Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Create and mark a common date table that supports consistent time analysis across facts
Configure table and column properties such as names, descriptions, data categories, summarization, and visibility
Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design

Correct answer: A

Why: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model. This directly addresses the stated requirement: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model..

Option review:

B: Calculated columns and tables are computed during refresh and stored in the model, unlike measures that evaluate at query time. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model..

C: A dedicated date dimension provides complete calendar attributes and a consistent basis for DAX time intelligence and report filtering. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model..

D: Well-configured model metadata improves report authoring, default aggregation behavior, discoverability, and the consumer experience. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model..

E: A role-playing dimension represents the same business dimension in more than one analytical role and must be modeled so each role is unambiguous. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model..

Learning point: Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables

Question 25

A modernization plan for the executive report, analysis cycle 9 requires the team to configure model metadata so fields behave and appear correctly to report authors. Which Power BI action is the clearest fit?

Configure table and column properties such as names, descriptions, data categories, summarization, and visibility
Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design
Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables
Create and mark a common date table that supports consistent time analysis across facts
Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required

Correct answer: A

Option review:

C: Cardinality describes uniqueness on each side of a relationship, while cross-filter direction controls how filters propagate through the model. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure model metadata so fields behave and appear correctly to report authors.

E: Calculated columns and tables are computed during refresh and stored in the model, unlike measures that evaluate at query time. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: configure model metadata so fields behave and appear correctly to report authors.

Learning point: Configure table and column properties such as names, descriptions, data categories, summarization, and visibility

Question 1

Question 2

Question 3

Question 4

Question 5

Question 6

Question 7

Question 8

Question 9

Question 10

Question 11

Question 12

Question 13

Question 14

Question 15

Question 16

Question 17

Question 18

Question 19

Question 20

Question 21

Question 22

Question 23

Question 24

Question 25

Related posts: