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Microsoft PL-300 Power BI Model Performance Performance Analyzer And DAX Query View Practice Test

Skills 2.3 • 25 original questions

This Microsoft PL-300 Power BI Data Analyst practice test focuses on power bi model performance performance analyzer and dax query view 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 sales analytics project at Wide World Importers, the self-service BI administrator must improve model efficiency by eliminating data that reports do not need. Which action most directly satisfies the requirement for the sales model, analysis cycle 1?

Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design
Use Quick measures to generate a supported calculation pattern and inspect or adapt the resulting DAX
Create a DAX calculated table or calculated column when the result must be materialized during model refresh
Remove unnecessary rows and columns before or during model load
Configure table and column properties such as names, descriptions, data categories, summarization, and visibility

Correct answer: D

Why: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned. This directly addresses the stated requirement: improve model efficiency by eliminating data that reports do not need.

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: improve model efficiency by eliminating data that reports do not need.

B: Quick measures create DAX for common analytical patterns and can accelerate authoring when the built-in pattern matches the need. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve model efficiency by eliminating data that reports do not need.

C: Calculated tables and columns are stored model objects created from DAX and are appropriate when row-level or table-level results must persist. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve model efficiency by eliminating data that reports do not need.

D: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned. This directly addresses the stated requirement: improve model efficiency by eliminating data that reports do not need.

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: improve model efficiency by eliminating data that reports do not need.

Learning point: Remove unnecessary rows and columns before or during model load

Question 2

Northwind Traders is revising its analytics solution during a finance dashboard refresh. The team needs to identify the source of poor report or DAX performance using built-in diagnostic views. Which Power BI action should the data analyst choose for the finance semantic model, analysis cycle 1?

Use basic statistical DAX functions to calculate values such as median, percentile, variance, or standard deviation when required
Use Performance Analyzer and DAX query view to investigate slow visuals, measures, relationships, or generated DAX queries
Create an explicit measure for a single aggregation such as SUM, COUNT, AVERAGE, MIN, or MAX
Build time-intelligence measures against a suitable date table
Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables

Correct answer: B

Why: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic model. This directly addresses the stated requirement: identify the source of poor report or DAX performance using built-in diagnostic views.

Option review:

A: DAX includes statistical functions that summarize distributions beyond simple totals and averages. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: identify the source of poor report or DAX performance using built-in diagnostic views.

B: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic model. This directly addresses the stated requirement: identify the source of poor report or DAX performance using built-in diagnostic views.

C: Measures calculate at query time in the current filter context and are the normal way to define reusable business aggregations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: identify the source of poor report or DAX performance using built-in diagnostic views.

D: Time-intelligence patterns compare or aggregate values across periods such as prior year, year to date, or rolling windows. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: identify the source of poor report or DAX performance using built-in diagnostic views.

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: identify the source of poor report or DAX performance using built-in diagnostic views.

Learning point: Use Performance Analyzer and DAX query view to investigate slow visuals, measures, relationships, or generated DAX queries

Question 3

A design review for the operations dashboard, analysis cycle 1 at Tailspin Toys identifies one required capability: improve performance by storing data at a coarser level of detail when business requirements permit. Which implementation is the strongest fit?

Use Performance Analyzer and DAX query view to investigate slow visuals, measures, relationships, or generated DAX queries
Build time-intelligence measures against a suitable date table
Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables
Reduce model granularity when detailed rows are not required for the intended analysis
Create and mark a common date table that supports consistent time analysis across facts

Correct answer: D

Why: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary. This directly addresses the stated requirement: improve performance by storing data at a coarser level of detail when business requirements permit.

Option review:

A: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic model. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve performance by storing data at a coarser level of detail when business requirements permit.

B: Time-intelligence patterns compare or aggregate values across periods such as prior year, year to date, or rolling windows. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve performance by storing data at a coarser level of detail when business requirements permit.

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: improve performance by storing data at a coarser level of detail when business requirements permit.

D: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary. This directly addresses the stated requirement: improve performance by storing data at a coarser level of detail when business requirements permit.

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: improve performance by storing data at a coarser level of detail when business requirements permit.

Learning point: Reduce model granularity when detailed rows are not required for the intended analysis

Question 4

For the customer report, analysis cycle 2, Alpine Ski House wants the least indirect way to improve model efficiency by eliminating data that reports do not need. Which Power BI feature or action should the analytics lead select?

Use Performance Analyzer and DAX query view to investigate slow visuals, measures, relationships, or generated DAX queries
Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Remove unnecessary rows and columns before or during model load
Reduce model granularity when detailed rows are not required for the intended analysis
Build time-intelligence measures against a suitable date table

Correct answer: C

Option review:

A: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic model. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve model efficiency by eliminating data that reports do not need.

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: improve model efficiency by eliminating data that reports do not need.

C: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned. This directly addresses the stated requirement: improve model efficiency by eliminating data that reports do not need.

D: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve model efficiency by eliminating data that reports do not need.

E: Time-intelligence patterns compare or aggregate values across periods such as prior year, year to date, or rolling windows. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve model efficiency by eliminating data that reports do not need.

Learning point: Remove unnecessary rows and columns before or during model load

Question 5

The BI developer at Wide World Importers is comparing several approaches for a monthly KPI review. The chosen approach must identify the source of poor report or DAX performance using built-in diagnostic views. Which option best meets that condition?

Create a DAX calculated table or calculated column when the result must be materialized during model refresh
Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design
Use CALCULATE to evaluate an expression under modified filter context
Use Performance Analyzer and DAX query view to investigate slow visuals, measures, relationships, or generated DAX queries
Reduce model granularity when detailed rows are not required for the intended analysis

Correct answer: D

Option review:

A: Calculated tables and columns are stored model objects created from DAX and are appropriate when row-level or table-level results must persist. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: identify the source of poor report or DAX performance using built-in diagnostic views.

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: identify the source of poor report or DAX performance using built-in diagnostic views.

C: CALCULATE changes the filter context for an expression and is central to many DAX business calculations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: identify the source of poor report or DAX performance using built-in diagnostic views.

D: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic model. This directly addresses the stated requirement: identify the source of poor report or DAX performance using built-in diagnostic views.

E: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: identify the source of poor report or DAX performance using built-in diagnostic views.

Learning point: Use Performance Analyzer and DAX query view to investigate slow visuals, measures, relationships, or generated DAX queries

Question 6

A support escalation at Northwind Traders has been narrowed to one requirement: improve performance by storing data at a coarser level of detail when business requirements permit. Which configuration should be investigated first for the inventory model, analysis cycle 2?

Create a calculation group to apply reusable calculation items to existing explicit measures
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
Reduce model granularity when detailed rows are not required for the intended analysis
Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required

Correct answer: D

Option review:

A: Calculation groups reduce repetitive measures by applying DAX calculation items, such as time transformations, to selected measures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve performance by storing data at a coarser level of detail when business requirements permit.

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: improve performance by storing data at a coarser level of detail when business requirements permit.

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: improve performance by storing data at a coarser level of detail when business requirements permit.

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: improve performance by storing data at a coarser level of detail when business requirements permit.

Learning point: Reduce model granularity when detailed rows are not required for the intended analysis

Question 7

An analytics governance review at Tailspin Toys asks the self-service BI administrator to improve model efficiency by eliminating data that reports do not need. Which action aligns most directly with that requirement?

Remove unnecessary rows and columns before or during model load
Create a DAX calculated table or calculated column when the result must be materialized during model refresh
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 Performance Analyzer and DAX query view to investigate slow visuals, measures, relationships, or generated DAX queries

Correct answer: A

Option review:

A: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned. This directly addresses the stated requirement: improve model efficiency by eliminating data that reports do not need.

B: Calculated tables and columns are stored model objects created from DAX and are appropriate when row-level or table-level results must persist. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve model efficiency by eliminating data that reports do not need.

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: improve model efficiency by eliminating data that reports do not need.

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: improve model efficiency by eliminating data that reports do not need.

E: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic model. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve model efficiency by eliminating data that reports do not need.

Learning point: Remove unnecessary rows and columns before or during model load

Question 8

Before the forecast report, analysis cycle 3 is released, the analytics team must identify the source of poor report or DAX performance using built-in diagnostic views. Which Power BI implementation should be added?

Reduce model granularity when detailed rows are not required for the intended analysis
Create a semi-additive measure that aggregates differently across time than across other dimensions
Use Performance Analyzer and DAX query view to investigate slow visuals, measures, relationships, or generated DAX queries
Use Quick measures to generate a supported calculation pattern and inspect or adapt the resulting DAX
Use basic statistical DAX functions to calculate values such as median, percentile, variance, or standard deviation when required

Correct answer: C

Option review:

A: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: identify the source of poor report or DAX performance using built-in diagnostic views.

B: Balances and snapshots often add across entities but require last-value, first-value, or another special rule across dates. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: identify the source of poor report or DAX performance using built-in diagnostic views.

C: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic model. This directly addresses the stated requirement: identify the source of poor report or DAX performance using built-in diagnostic views.

D: Quick measures create DAX for common analytical patterns and can accelerate authoring when the built-in pattern matches the need. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: identify the source of poor report or DAX performance using built-in diagnostic views.

E: DAX includes statistical functions that summarize distributions beyond simple totals and averages. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: identify the source of poor report or DAX performance using built-in diagnostic views.

Learning point: Use Performance Analyzer and DAX query view to investigate slow visuals, measures, relationships, or generated DAX queries

Question 9

Wide World Importers is replacing a manual analytics process. The replacement must reliably improve performance by storing data at a coarser level of detail when business requirements permit. Which choice should be implemented for the mobile report, analysis cycle 3?

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
Reduce model granularity when detailed rows are not required for the intended analysis
Create and mark a common date table that supports consistent time analysis across facts
Create a calculation group to apply reusable calculation items to existing explicit measures

Correct answer: C

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: improve performance by storing data at a coarser level of detail when business requirements permit.

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: improve performance by storing data at a coarser level of detail when business requirements permit.

C: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary. This directly addresses the stated requirement: improve performance by storing data at a coarser level of detail when business requirements permit.

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: improve performance by storing data at a coarser level of detail when business requirements permit.

E: Calculation groups reduce repetitive measures by applying DAX calculation items, such as time transformations, to selected measures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve performance by storing data at a coarser level of detail when business requirements permit.

Learning point: Reduce model granularity when detailed rows are not required for the intended analysis

Question 10

Which Power BI action best matches this technical purpose for the executive report, analysis cycle 4: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned.

Remove unnecessary rows and columns before or during model load
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
Create a calculation group to apply reusable calculation items to existing explicit measures
Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design

Correct answer: A

Why: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned. This directly addresses the stated requirement: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned..

Option review:

A: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned. This directly addresses the stated requirement: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned..

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: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned..

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: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned..

D: Calculation groups reduce repetitive measures by applying DAX calculation items, such as time transformations, to selected measures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned..

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: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned..

Learning point: Remove unnecessary rows and columns before or during model load

Question 11

A runbook for the sales model, analysis cycle 4 contains this description: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic model. Which Power BI feature or action belongs in the runbook?

Use CALCULATE to evaluate an expression under modified filter context
Use Performance Analyzer and DAX query view to investigate slow visuals, measures, relationships, or generated DAX queries
Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Build time-intelligence measures against a suitable date table
Create and mark a common date table that supports consistent time analysis across facts

Correct answer: B

Why: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic model. This directly addresses the stated requirement: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic model..

Option review:

A: CALCULATE changes the filter context for an expression and is central to many DAX business calculations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic model..

B: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic model. This directly addresses the stated requirement: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic model..

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: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic model..

D: Time-intelligence patterns compare or aggregate values across periods such as prior year, year to date, or rolling windows. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic model..

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: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic model..

Learning point: Use Performance Analyzer and DAX query view to investigate slow visuals, measures, relationships, or generated DAX queries

Question 12

During validation of the finance semantic model, analysis cycle 4, the Power BI data analyst needs a capability that behaves as follows: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary. Which choice is correct?

Create a DAX calculated table or calculated column when the result must be materialized during model refresh
Create a semi-additive measure that aggregates differently across time than across other dimensions
Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Reduce model granularity when detailed rows are not required for the intended analysis
Create and mark a common date table that supports consistent time analysis across facts

Correct answer: D

Why: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary. This directly addresses the stated requirement: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary..

Option review:

A: Calculated tables and columns are stored model objects created from DAX and are appropriate when row-level or table-level results must persist. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary..

B: Balances and snapshots often add across entities but require last-value, first-value, or another special rule across dates. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary..

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: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary..

D: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary. This directly addresses the stated requirement: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary..

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: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary..

Learning point: Reduce model granularity when detailed rows are not required for the intended analysis

Question 13

A stakeholder asks why a particular Power BI feature should be used for the operations dashboard, analysis cycle 5. The required behavior is: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned. Which action provides that behavior?

Create a calculation group to apply reusable calculation items to existing explicit measures
Configure table and column properties such as names, descriptions, data categories, summarization, and visibility
Remove unnecessary rows and columns before or during model load
Use CALCULATE to evaluate an expression under modified filter context
Reduce model granularity when detailed rows are not required for the intended analysis

Correct answer: C

Why: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned. This directly addresses the stated requirement: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned..

Option review:

A: Calculation groups reduce repetitive measures by applying DAX calculation items, such as time transformations, to selected measures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned..

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: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned..

C: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned. This directly addresses the stated requirement: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned..

D: CALCULATE changes the filter context for an expression and is central to many DAX business calculations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned..

E: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned..

Learning point: Remove unnecessary rows and columns before or during model load

Question 14

The customer report, analysis cycle 5 is moving to production at Northwind Traders. Which action should be approved when the goal is to identify the source of poor report or DAX performance using built-in diagnostic views?

Set relationship cardinality and cross-filter direction to match the grain and filtering behavior of the tables
Create an explicit measure for a single aggregation such as SUM, COUNT, AVERAGE, MIN, or MAX
Create a semi-additive measure that aggregates differently across time than across other dimensions
Create a calculation group to apply reusable calculation items to existing explicit measures
Use Performance Analyzer and DAX query view to investigate slow visuals, measures, relationships, or generated DAX queries

Correct answer: E

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: identify the source of poor report or DAX performance using built-in diagnostic views.

B: Measures calculate at query time in the current filter context and are the normal way to define reusable business aggregations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: identify the source of poor report or DAX performance using built-in diagnostic views.

C: Balances and snapshots often add across entities but require last-value, first-value, or another special rule across dates. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: identify the source of poor report or DAX performance using built-in diagnostic views.

D: Calculation groups reduce repetitive measures by applying DAX calculation items, such as time transformations, to selected measures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: identify the source of poor report or DAX performance using built-in diagnostic views.

E: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic model. This directly addresses the stated requirement: identify the source of poor report or DAX performance using built-in diagnostic views.

Learning point: Use Performance Analyzer and DAX query view to investigate slow visuals, measures, relationships, or generated DAX queries

Question 15

A data analyst at Tailspin Toys must satisfy this acceptance criterion for the regional workspace, analysis cycle 5: improve performance by storing data at a coarser level of detail when business requirements permit. Which implementation is most appropriate?

Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design
Reduce model granularity when detailed rows are not required for the intended analysis
Use basic statistical DAX functions to calculate values such as median, percentile, variance, or standard deviation when required
Create and mark a common date table that supports consistent time analysis across facts
Remove unnecessary rows and columns before or during model load

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: improve performance by storing data at a coarser level of detail when business requirements permit.

B: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary. This directly addresses the stated requirement: improve performance by storing data at a coarser level of detail when business requirements permit.

C: DAX includes statistical functions that summarize distributions beyond simple totals and averages. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve performance by storing data at a coarser level of detail when business requirements permit.

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: improve performance by storing data at a coarser level of detail when business requirements permit.

E: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve performance by storing data at a coarser level of detail when business requirements permit.

Learning point: Reduce model granularity when detailed rows are not required for the intended analysis

Question 16

A self-service analytics rollout at Alpine Ski House can proceed only after the team can improve model efficiency by eliminating data that reports do not need. What should the analytics lead configure?

Remove unnecessary rows and columns before or during model load
Build time-intelligence measures against a suitable date table
Create a DAX calculated table or calculated column when the result must be materialized during model refresh
Use Quick measures to generate a supported calculation pattern and inspect or adapt the resulting DAX
Configure table and column properties such as names, descriptions, data categories, summarization, and visibility

Correct answer: A

Option review:

B: Time-intelligence patterns compare or aggregate values across periods such as prior year, year to date, or rolling windows. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve model efficiency by eliminating data that reports do not need.

D: Quick measures create DAX for common analytical patterns and can accelerate authoring when the built-in pattern matches the need. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve model efficiency by eliminating data that reports do not need.

Learning point: Remove unnecessary rows and columns before or during model load

Question 17

The analytics team at Wide World Importers has ruled out unrelated redesign work. Which action directly enables the team to identify the source of poor report or DAX performance using built-in diagnostic views for the service-level dashboard, analysis cycle 6?

Use basic statistical DAX functions to calculate values such as median, percentile, variance, or standard deviation when required
Create a calculation group to apply reusable calculation items to existing explicit measures
Create a semi-additive measure that aggregates differently across time than across other dimensions
Use Performance Analyzer and DAX query view to investigate slow visuals, measures, relationships, or generated DAX queries
Build time-intelligence measures against a suitable date table

Correct answer: D

Option review:

B: Calculation groups reduce repetitive measures by applying DAX calculation items, such as time transformations, to selected measures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: identify the source of poor report or DAX performance using built-in diagnostic views.

E: Time-intelligence patterns compare or aggregate values across periods such as prior year, year to date, or rolling windows. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: identify the source of poor report or DAX performance using built-in diagnostic views.

Learning point: Use Performance Analyzer and DAX query view to investigate slow visuals, measures, relationships, or generated DAX queries

Question 18

An audit finding for the forecast report, analysis cycle 6 says the current design cannot improve performance by storing data at a coarser level of detail when business requirements permit. Which Power BI action most directly closes the gap?

Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Reduce model granularity when detailed rows are not required for the intended analysis
Use CALCULATE to evaluate an expression under modified filter context
Remove unnecessary rows and columns before or during model load
Create a DAX calculated table or calculated column when the result must be materialized during model refresh

Correct answer: B

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: improve performance by storing data at a coarser level of detail when business requirements permit.

C: CALCULATE changes the filter context for an expression and is central to many DAX business calculations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve performance by storing data at a coarser level of detail when business requirements permit.

D: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve performance by storing data at a coarser level of detail when business requirements permit.

E: Calculated tables and columns are stored model objects created from DAX and are appropriate when row-level or table-level results must persist. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve performance by storing data at a coarser level of detail when business requirements permit.

Learning point: Reduce model granularity when detailed rows are not required for the intended analysis

Question 19

For the mobile report, analysis cycle 7, the self-service BI administrator needs a repeatable solution that will improve model efficiency by eliminating data that reports do not need. Which option should replace the current ad hoc process?

Create a calculation group to apply reusable calculation items to existing explicit measures
Use CALCULATE to evaluate an expression under modified filter context
Create a DAX calculated table or calculated column when the result must be materialized during model refresh
Remove unnecessary rows and columns before or during model load
Reduce model granularity when detailed rows are not required for the intended analysis

Correct answer: D

Option review:

A: Calculation groups reduce repetitive measures by applying DAX calculation items, such as time transformations, to selected measures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve model efficiency by eliminating data that reports do not need.

B: CALCULATE changes the filter context for an expression and is central to many DAX business calculations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve model efficiency by eliminating data that reports do not need.

E: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve model efficiency by eliminating data that reports do not need.

Learning point: Remove unnecessary rows and columns before or during model load

Question 20

During a finance dashboard refresh, Alpine Ski House defines the desired outcome as follows: identify the source of poor report or DAX performance using built-in diagnostic views. Which Power BI capability should the team use?

Use CALCULATE to evaluate an expression under modified filter context
Remove unnecessary rows and columns before or during model load
Create a calculation group to apply reusable calculation items to existing explicit measures
Use basic statistical DAX functions to calculate values such as median, percentile, variance, or standard deviation when required
Use Performance Analyzer and DAX query view to investigate slow visuals, measures, relationships, or generated DAX queries

Correct answer: E

Option review:

A: CALCULATE changes the filter context for an expression and is central to many DAX business calculations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: identify the source of poor report or DAX performance using built-in diagnostic views.

B: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: identify the source of poor report or DAX performance using built-in diagnostic views.

C: Calculation groups reduce repetitive measures by applying DAX calculation items, such as time transformations, to selected measures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: identify the source of poor report or DAX performance using built-in diagnostic views.

D: DAX includes statistical functions that summarize distributions beyond simple totals and averages. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: identify the source of poor report or DAX performance using built-in diagnostic views.

Learning point: Use Performance Analyzer and DAX query view to investigate slow visuals, measures, relationships, or generated DAX queries

Question 21

A new requirement is added to the sales model, analysis cycle 7: improve performance by storing data at a coarser level of detail when business requirements permit. Which action should the report author take?

Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Reduce model granularity when detailed rows are not required for the intended analysis
Create a calculation group to apply reusable calculation items to existing explicit measures
Create and mark a common date table that supports consistent time analysis across facts
Use basic statistical DAX functions to calculate values such as median, percentile, variance, or standard deviation when required

Correct answer: B

Option review:

C: Calculation groups reduce repetitive measures by applying DAX calculation items, such as time transformations, to selected measures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve performance by storing data at a coarser level of detail when business requirements permit.

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: improve performance by storing data at a coarser level of detail when business requirements permit.

E: DAX includes statistical functions that summarize distributions beyond simple totals and averages. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve performance by storing data at a coarser level of detail when business requirements permit.

Learning point: Reduce model granularity when detailed rows are not required for the intended analysis

Question 22

Northwind Traders is troubleshooting an unexpected reporting result. The decisive requirement is to improve model efficiency by eliminating data that reports do not need. Which feature or configuration is most relevant?

Build time-intelligence measures against a suitable date table
Remove unnecessary rows and columns before or during model load
Use Performance Analyzer and DAX query view to investigate slow visuals, measures, relationships, or generated DAX queries
Create and mark a common date table that supports consistent time analysis across facts
Create an explicit measure for a single aggregation such as SUM, COUNT, AVERAGE, MIN, or MAX

Correct answer: B

Option review:

A: Time-intelligence patterns compare or aggregate values across periods such as prior year, year to date, or rolling windows. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve model efficiency by eliminating data that reports do not need.

B: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned. This directly addresses the stated requirement: improve model efficiency by eliminating data that reports do not need.

C: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic model. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve model efficiency by eliminating data that reports do not need.

E: Measures calculate at query time in the current filter context and are the normal way to define reusable business aggregations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve model efficiency by eliminating data that reports do not need.

Learning point: Remove unnecessary rows and columns before or during model load

Question 23

A technical workshop for the operations dashboard, analysis cycle 8 documents this behavior: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic model. Which Power BI choice is being described?

Create a calculation group to apply reusable calculation items to existing explicit measures
Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Use Performance Analyzer and DAX query view to investigate slow visuals, measures, relationships, or generated DAX queries
Create an explicit measure for a single aggregation such as SUM, COUNT, AVERAGE, MIN, or MAX
Reduce model granularity when detailed rows are not required for the intended analysis

Correct answer: C

Why: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic model. This directly addresses the stated requirement: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic model..

Option review:

A: Calculation groups reduce repetitive measures by applying DAX calculation items, such as time transformations, to selected measures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic 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: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic model..

C: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic model. This directly addresses the stated requirement: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic model..

D: Measures calculate at query time in the current filter context and are the normal way to define reusable business aggregations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic model..

E: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Performance Analyzer exposes visual timings and queries, while DAX query view helps inspect and test DAX behavior against the semantic model..

Learning point: Use Performance Analyzer and DAX query view to investigate slow visuals, measures, relationships, or generated DAX queries

Question 24

The Power BI data analyst must identify the Power BI capability that provides this function: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary. Which answer is correct for the customer report, analysis cycle 8?

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
Use CALCULATE to evaluate an expression under modified filter context
Reuse one dimension in multiple relationship roles, such as order date and ship date, with an appropriate role-playing design
Reduce model granularity when detailed rows are not required for the intended analysis

Correct answer: E

Why: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary. This directly addresses the stated requirement: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary..

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: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary..

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: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary..

C: CALCULATE changes the filter context for an expression and is central to many DAX business calculations. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary..

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: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary..

E: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary. This directly addresses the stated requirement: Aggregating to a coarser grain can dramatically reduce model size and query work when detailed transaction-level data is unnecessary..

Learning point: Reduce model granularity when detailed rows are not required for the intended analysis

Question 25

A modernization plan for the regional workspace, analysis cycle 9 requires the team to improve model efficiency by eliminating data that reports do not need. Which Power BI action is the clearest fit?

Build time-intelligence measures against a suitable date table
Create a semi-additive measure that aggregates differently across time than across other dimensions
Use calculated columns for row-level stored results and calculated tables when a DAX-generated table is required
Create a calculation group to apply reusable calculation items to existing explicit measures
Remove unnecessary rows and columns before or during model load

Correct answer: E

Option review:

B: Balances and snapshots often add across entities but require last-value, first-value, or another special rule across dates. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve model efficiency by eliminating data that reports do not need.

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: improve model efficiency by eliminating data that reports do not need.

D: Calculation groups reduce repetitive measures by applying DAX calculation items, such as time transformations, to selected measures. It is useful in another Power BI scenario, but it does not most directly satisfy this requirement: improve model efficiency by eliminating data that reports do not need.

E: Smaller semantic models use less memory and generally refresh and query more efficiently because irrelevant data is not stored or scanned. This directly addresses the stated requirement: improve model efficiency by eliminating data that reports do not need.

Learning point: Remove unnecessary rows and columns before or during model load

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