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Microsoft PL-300 Bundle

Exam Code: PL-300

Exam Name Microsoft Power BI Data Analyst

Certification Provider: Microsoft

Corresponding Certification: Microsoft Certified: Power BI Data Analyst Associate

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Microsoft PL-300 Practice Test 22

Question 210

A high-cardinality text field is used to relate unrelated dimension tables with accidental collisions. How should it be redesigned?

  1. Use a disconnected parameter table read by SELECTEDVALUE in a measure.
  2. Treat the Tag-Document table as a factless bridge.
  3. Use a Customer-Segment membership bridge with appropriate unique dimensions.
  4. Use stable identity keys and explicit bridge relationships rather than ambiguous free text.

Explanation

Correct Answer

D:

Use stable identity keys and explicit bridge relationships rather than ambiguous free text. Text matching on nonunique descriptions can create incorrect propagation paths.

Incorrect Answers

A:

Disconnected tables can pass choices to DAX without filtering through a relationship. Its application is “rate but is disconnected from the fact table”, not the need “to relate unrelated dimension tables with accidental collisions”.

B:

Factless tables represent associations or events without an additive numeric measure. That is a different operating condition from “to relate unrelated dimension tables with accidental collisions”: “and must not sum any numerical business amount”.

C:

A bridge represents many-to-many membership without duplicating customer facts. It serves “marketing segments and each segment contains many customers”, not “to relate unrelated dimension tables with accidental collisions”.

Question 211

A Sales fact contains one row per InvoiceLine; a report needs line discounts and net totals. What grain should the model retain?

  1. Keep the invoice-line grain in the Sales fact.
  2. Treat account balance as semi-additive across time.
  3. Model inventory as a periodic snapshot fact.
  4. Model department history with effective-dated or type-2 dimension records as required.

Explanation

Correct Answer

A:

Keep the invoice-line grain in the Sales fact. Line-level events support accurate discount measures and drillthrough.

Incorrect Answers

B:

Summing daily point-in-time balances overstates a period-end balance. That matters for “across a month as if they were transactions” rather than the condition “a report needs line discounts and net totals”.

C:

Snapshot facts record balances at defined reporting points rather than additive transactions. The boundary it addresses is “per Product per Warehouse at each month end”, while this case concerns “a report needs line discounts and net totals”.

D:

Time-variant assignments must preserve the version valid for each event. This solves “each worker's current department plus historical department assignments”; the stated issue is “a report needs line discounts and net totals”.

Question 212

An inventory table holds one balance per Product per Warehouse at each month end. What fact type is it?

  1. Model department history with effective-dated or type-2 dimension records as required.
  2. Keep the invoice-line grain in the Sales fact.
  3. Model PageViews as an event-grain transaction fact.
  4. Model inventory as a periodic snapshot fact.

Explanation

Correct Answer

D:

Model inventory as a periodic snapshot fact. Snapshot facts record balances at defined reporting points rather than additive transactions.

Incorrect Answers

A:

Time-variant assignments must preserve the version valid for each event. This solves “each worker's current department plus historical department assignments”; the stated issue is “per Product per Warehouse at each month end”.

B:

Line-level events support accurate discount measures and drillthrough. The deciding difference is “a report needs line discounts and net totals” versus “per Product per Warehouse at each month end”.

C:

Each pageview is a measurable observation with dimensional keys. Its application is “one row per pageview with VisitorID and PageID”, not the need “per Product per Warehouse at each month end”.

Question 213

A bank stores account balance by day, and a visual sums balances across a month as if they were transactions. What is the problem?

  1. Model department history with effective-dated or type-2 dimension records as required.
  2. Model PageViews as an event-grain transaction fact.
  3. Treat account balance as semi-additive across time.
  4. Keep the invoice-line grain in the Sales fact.

Explanation

Correct Answer

C:

Treat account balance as semi-additive across time. Summing daily point-in-time balances overstates a period-end balance.

Incorrect Answers

A:

Time-variant assignments must preserve the version valid for each event. That is a different operating condition from “across a month as if they were transactions”: “each worker's current department plus historical department assignments”.

B:

Each pageview is a measurable observation with dimensional keys. It serves “one row per pageview with VisitorID and PageID”, not “across a month as if they were transactions”.

D:

Line-level events support accurate discount measures and drillthrough. That matters for “a report needs line discounts and net totals” rather than the condition “across a month as if they were transactions”.

Question 214

A website logs one row per pageview with VisitorID and PageID. What kind of fact fits?

  1. Model department history with effective-dated or type-2 dimension records as required.
  2. Treat account balance as semi-additive across time.
  3. Model inventory as a periodic snapshot fact.
  4. Model PageViews as an event-grain transaction fact.

Explanation

Correct Answer

D:

Model PageViews as an event-grain transaction fact. Each pageview is a measurable observation with dimensional keys.

Incorrect Answers

A:

Time-variant assignments must preserve the version valid for each event. That matters for “each worker's current department plus historical department assignments” rather than the condition “one row per pageview with VisitorID and PageID”.

B:

Summing daily point-in-time balances overstates a period-end balance. The boundary it addresses is “across a month as if they were transactions”, while this case concerns “one row per pageview with VisitorID and PageID”.

C:

Snapshot facts record balances at defined reporting points rather than additive transactions. This solves “per Product per Warehouse at each month end”; the stated issue is “one row per pageview with VisitorID and PageID”.

Question 215

An employee table stores each worker's current department plus historical department assignments. What modeling strategy supports history?

  1. Treat account balance as semi-additive across time.
  2. Keep the invoice-line grain in the Sales fact.
  3. Model department history with effective-dated or type-2 dimension records as required.
  4. Model inventory as a periodic snapshot fact.

Explanation

Correct Answer

C:

Model department history with effective-dated or type-2 dimension records as required. Time-variant assignments must preserve the version valid for each event.

Incorrect Answers

A:

Summing daily point-in-time balances overstates a period-end balance. The deciding difference is “across a month as if they were transactions” versus “each worker's current department plus historical department assignments”.

B:

Line-level events support accurate discount measures and drillthrough. Its application is “a report needs line discounts and net totals”, not the need “each worker's current department plus historical department assignments”.

D:

Snapshot facts record balances at defined reporting points rather than additive transactions. It serves “per Product per Warehouse at each month end”, not “each worker's current department plus historical department assignments”.

Question 216

A report's sales values are blank for several products despite sales records, because ProductKey types do not match. What should be corrected?

  1. Introduce a unique conformed lookup dimension instead of a repeated-key direct link.
  2. Remove unnecessary bidirectional relationships and retain a single clear filter path.
  3. Repair Customer dimension uniqueness before declaring one-to-many cardinality.
  4. Align relationship key data types and values between Product and Sales.

Explanation

Correct Answer

D:

Align relationship key data types and values between Product and Sales. Relationship filters cannot match incompatible representations.

Incorrect Answers

A:

Clear one-to-many relationships usually improve predictability in a star model. It serves “relationship where a conformed dimension could be used”, not “ProductKey types do not match”.

B:

Ambiguous paths can create incorrect or prohibited model filtering. The present requirement is “ProductKey types do not match”; “between Region and Sales and gets ambiguity errors” is a distinct situation.

C:

A lookup's grain must be one record per relationship key. That matters for “lookup has duplicates so the relationship auto-detects many-to-many” rather than the condition “ProductKey types do not match”.

Question 217

A Customer lookup has duplicates so the relationship auto-detects many-to-many. What should happen?

  1. Introduce a unique conformed lookup dimension instead of a repeated-key direct link.
  2. Repair Customer dimension uniqueness before declaring one-to-many cardinality.
  3. Align relationship key data types and values between Product and Sales.
  4. Check which Sales-to-Date relationship is active.

Explanation

Correct Answer

B:

Repair Customer dimension uniqueness before declaring one-to-many cardinality. A lookup's grain must be one record per relationship key.

Incorrect Answers

A:

Clear one-to-many relationships usually improve predictability in a star model. The boundary it addresses is “relationship where a conformed dimension could be used”, while this case concerns “lookup has duplicates so the relationship auto-detects many-to-many”.

C:

Align relationship key data types and values between Product and Sales. The deciding difference is “ProductKey types do not match” versus “lookup has duplicates so the relationship auto-detects many-to-many”.

D:

That recommendation applies when a Date slicer unexpectedly filters orders through ShipDate rather than OrderDate. Its application is “unexpectedly filters orders through ShipDate rather than OrderDate”, not the need “lookup has duplicates so the relationship auto-detects many-to-many”.

Question 218

A Date slicer unexpectedly filters orders through ShipDate rather than OrderDate. What should be examined?

  1. Remove unnecessary bidirectional relationships and retain a single clear filter path.
  2. Introduce a unique conformed lookup dimension instead of a repeated-key direct link.
  3. Check which Sales-to-Date relationship is active.
  4. Repair Customer dimension uniqueness before declaring one-to-many cardinality.

Explanation

Correct Answer

C:

Check which Sales-to-Date relationship is active. Active relationships define default date filter propagation.

Incorrect Answers

A:

Ambiguous paths can create incorrect or prohibited model filtering. Its application is “between Region and Sales and gets ambiguity errors”, not the need “unexpectedly filters orders through ShipDate rather than OrderDate”.

B:

Clear one-to-many relationships usually improve predictability in a star model. That is a different operating condition from “unexpectedly filters orders through ShipDate rather than OrderDate”: “relationship where a conformed dimension could be used”.

D:

A lookup's grain must be one record per relationship key. The present requirement is “unexpectedly filters orders through ShipDate rather than OrderDate”; “lookup has duplicates so the relationship auto-detects many-to-many” is a distinct situation.

Question 219

A report uses two bidirectional paths between Region and Sales and gets ambiguity errors. What should be simplified?

  1. Check which Sales-to-Date relationship is active.
  2. Repair Customer dimension uniqueness before declaring one-to-many cardinality.
  3. Remove unnecessary bidirectional relationships and retain a single clear filter path.
  4. Introduce a unique conformed lookup dimension instead of a repeated-key direct link.

Explanation

Correct Answer

C:

Remove unnecessary bidirectional relationships and retain a single clear filter path. Ambiguous paths can create incorrect or prohibited model filtering.

Incorrect Answers

A:

The relevant alternative case would be: A Date slicer unexpectedly filters orders through ShipDate rather than OrderDate. The present requirement is “between Region and Sales and gets ambiguity errors”; “unexpectedly filters orders through ShipDate rather than OrderDate” is a distinct situation.

B:

A lookup's grain must be one record per relationship key. That matters for “lookup has duplicates so the relationship auto-detects many-to-many” rather than the condition “between Region and Sales and gets ambiguity errors”.

D:

Clear one-to-many relationships usually improve predictability in a star model. This solves “relationship where a conformed dimension could be used”; the stated issue is “between Region and Sales and gets ambiguity errors”.

Question 220

A performance review finds a many-to-many relationship where a conformed dimension could be used. What redesign may help?

  1. Check which Sales-to-Date relationship is active.
  2. Repair Customer dimension uniqueness before declaring one-to-many cardinality.
  3. Introduce a unique conformed lookup dimension instead of a repeated-key direct link.
  4. Remove unnecessary bidirectional relationships and retain a single clear filter path.

Explanation

Correct Answer

C:

Introduce a unique conformed lookup dimension instead of a repeated-key direct link. Clear one-to-many relationships usually improve predictability in a star model.

Incorrect Answers

A:

That recommendation applies when a Date slicer unexpectedly filters orders through ShipDate rather than OrderDate. This solves “unexpectedly filters orders through ShipDate rather than OrderDate”; the stated issue is “relationship where a conformed dimension could be used”.

B:

A lookup's grain must be one record per relationship key. The deciding difference is “lookup has duplicates so the relationship auto-detects many-to-many” versus “relationship where a conformed dimension could be used”.

D:

Ambiguous paths can create incorrect or prohibited model filtering. That is a different operating condition from “relationship where a conformed dimension could be used”: “between Region and Sales and gets ambiguity errors”.