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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 9

Question 80

A user sees a column of values containing the literal word 'Error' as normal text. What should be checked?

  1. Replace or handle 'N/A' before the integer conversion step.
  2. Distinguish textual 'Error' values from actual Power Query error cells.
  3. Repair the Applied Step that references the obsolete column name.
  4. Validate expected schema and handle empty input files explicitly.

Explanation

Correct Answer

B:

Distinguish textual 'Error' values from actual Power Query error cells. Literal strings are data, whereas error values arise from failed evaluations.

Incorrect Answers

A:

Explicit exception handling can prevent unexpected type conversion failures. It serves “values like 'N/A' are converted to integers”, not “containing the literal word 'Error' as normal text”.

C:

M transformation steps depend on the columns created by earlier steps. That matters for “an upstream query renamed CustomerKey” rather than the condition “containing the literal word 'Error' as normal text”.

D:

Defensive source checks prevent brittle step failures on unusual extracts. The boundary it addresses is “occasionally returns an empty file, producing missing-column errors”, while this case concerns “containing the literal word 'Error' as normal text”.

Question 81

A store's DailySales measure jumps from typical hundreds to several million because of a load error. What should the analyst do?

  1. Reconcile metric population and denominator definitions before reporting.
  2. Validate extreme values against source records before removal or capping.
  3. Check whether the merge multiplied fact rows through nonunique matches.
  4. Apply documented valid-range tests and flag impossible ages for review.

Explanation

Correct Answer

B:

Validate extreme values against source records before removal or capping. Outlier remediation should reflect business truth rather than arbitrary deletion.

Incorrect Answers

A:

A surprising percentage may indicate a definition error rather than a transform bug. The boundary it addresses is “rate due to mixed numerator and denominator populations”, while this case concerns “of a load error”.

C:

A one-to-many join applied in the wrong direction can duplicate amounts. The deciding difference is “a join to a dimension with duplicate keys” versus “of a load error”.

D:

Range checks identify suspect entries without assuming all unusual values are bad. Its application is “age column contains values 0, 25 and 450”, not the need “of a load error”.

Question 82

A quantity column has negative returns that business users expect to include in net sales. What is the correct cleaning decision?

  1. Validate extreme values against source records before removal or capping.
  2. Reconcile metric population and denominator definitions before reporting.
  3. Apply documented valid-range tests and flag impossible ages for review.
  4. Preserve legitimate negative return rows instead of dropping all negatives.

Explanation

Correct Answer

D:

Preserve legitimate negative return rows instead of dropping all negatives. Negative quantities can encode valid returns and adjustments.

Incorrect Answers

A:

Outlier remediation should reflect business truth rather than arbitrary deletion. Its application is “of a load error”, not the need “business users expect to include in net sales”.

B:

A surprising percentage may indicate a definition error rather than a transform bug. That is a different operating condition from “business users expect to include in net sales”: “rate due to mixed numerator and denominator populations”.

C:

Range checks identify suspect entries without assuming all unusual values are bad. It serves “age column contains values 0, 25 and 450”, not “business users expect to include in net sales”.

Question 83

A marketing table reports a 140 percent conversion rate due to mixed numerator and denominator populations. What is needed?

  1. Reconcile metric population and denominator definitions before reporting.
  2. Validate extreme values against source records before removal or capping.
  3. Preserve legitimate negative return rows instead of dropping all negatives.
  4. Check whether the merge multiplied fact rows through nonunique matches.

Explanation

Correct Answer

A:

Reconcile metric population and denominator definitions before reporting. A surprising percentage may indicate a definition error rather than a transform bug.

Incorrect Answers

B:

Outlier remediation should reflect business truth rather than arbitrary deletion. That matters for “of a load error” rather than the condition “rate due to mixed numerator and denominator populations”.

C:

Negative quantities can encode valid returns and adjustments. The boundary it addresses is “business users expect to include in net sales”, while this case concerns “rate due to mixed numerator and denominator populations”.

D:

A one-to-many join applied in the wrong direction can duplicate amounts. This solves “a join to a dimension with duplicate keys”; the stated issue is “rate due to mixed numerator and denominator populations”.

Question 84

A customer age column contains values 0, 25 and 450. Which initial quality control is appropriate?

  1. Preserve legitimate negative return rows instead of dropping all negatives.
  2. Apply documented valid-range tests and flag impossible ages for review.
  3. Check whether the merge multiplied fact rows through nonunique matches.
  4. Validate extreme values against source records before removal or capping.

Explanation

Correct Answer

B:

Apply documented valid-range tests and flag impossible ages for review. Range checks identify suspect entries without assuming all unusual values are bad.

Incorrect Answers

A:

Negative quantities can encode valid returns and adjustments. This solves “business users expect to include in net sales”; the stated issue is “age column contains values 0, 25 and 450”.

C:

A one-to-many join applied in the wrong direction can duplicate amounts. Its application is “a join to a dimension with duplicate keys”, not the need “age column contains values 0, 25 and 450”.

D:

Outlier remediation should reflect business truth rather than arbitrary deletion. That is a different operating condition from “age column contains values 0, 25 and 450”: “of a load error”.

Question 85

A sales model's total grows unexpectedly after a join to a dimension with duplicate keys. What should be investigated?

  1. Check whether the merge multiplied fact rows through nonunique matches.
  2. Validate extreme values against source records before removal or capping.
  3. Preserve legitimate negative return rows instead of dropping all negatives.
  4. Apply documented valid-range tests and flag impossible ages for review.

Explanation

Correct Answer

A:

Check whether the merge multiplied fact rows through nonunique matches. A one-to-many join applied in the wrong direction can duplicate amounts.

Incorrect Answers

B:

Outlier remediation should reflect business truth rather than arbitrary deletion. It serves “of a load error”, not “a join to a dimension with duplicate keys”.

C:

Negative quantities can encode valid returns and adjustments. The present requirement is “a join to a dimension with duplicate keys”; “business users expect to include in net sales” is a distinct situation.

D:

Range checks identify suspect entries without assuming all unusual values are bad. That matters for “age column contains values 0, 25 and 450” rather than the condition “a join to a dimension with duplicate keys”.

Question 86

A scheduled refresh breaks when a source removes a field referenced by Power Query. What caused the failure?

  1. Identify schema drift and update the dependent transformation step.
  2. Enforce a documented type contract with validation before model loading.
  3. Update the delimiter setting in the source parsing step.
  4. Select and type a stable required field set rather than ingest every changing column.

Explanation

Correct Answer

A:

Identify schema drift and update the dependent transformation step. Removed source columns invalidate downstream references.

Incorrect Answers

B:

Schema contracts catch incompatible types before they silently alter results. The boundary it addresses is “a decimal one month and string the next”, while this case concerns “source removes a field referenced by Power Query”.

C:

Update the delimiter setting in the source parsing step. This solves “semicolons and the import collapses into one column”; the stated issue is “source removes a field referenced by Power Query”.

D:

Explicit schemas insulate models from irrelevant source columns. The deciding difference is “source field should not change report measure definitions” versus “source removes a field referenced by Power Query”.

Question 87

A newly added optional source field should not change report measure definitions. Which design helps?

  1. Update the delimiter setting in the source parsing step.
  2. Normalize source header names to stable canonical names before later transforms.
  3. Select and type a stable required field set rather than ingest every changing column.
  4. Identify schema drift and update the dependent transformation step.

Explanation

Correct Answer

C:

Select and type a stable required field set rather than ingest every changing column. Explicit schemas insulate models from irrelevant source columns.

Incorrect Answers

A:

Update the delimiter setting in the source parsing step. The deciding difference is “semicolons and the import collapses into one column” versus “source field should not change report measure definitions”.

B:

Canonical names reduce failures from localized source schemas. Its application is “multilingual source changes column headers based on localization”, not the need “source field should not change report measure definitions”.

D:

Identify schema drift and update the dependent transformation step. It serves “source removes a field referenced by Power Query”, not “source field should not change report measure definitions”.

Question 88

A source sends TotalSales as a decimal one month and string the next. What safeguard helps?

  1. Enforce a documented type contract with validation before model loading.
  2. Update the delimiter setting in the source parsing step.
  3. Identify schema drift and update the dependent transformation step.
  4. Normalize source header names to stable canonical names before later transforms.

Explanation

Correct Answer

A:

Enforce a documented type contract with validation before model loading. Schema contracts catch incompatible types before they silently alter results.

Incorrect Answers

B:

Update the delimiter setting in the source parsing step. The present requirement is “a decimal one month and string the next”; “semicolons and the import collapses into one column” is a distinct situation.

C:

Identify schema drift and update the dependent transformation step. That matters for “source removes a field referenced by Power Query” rather than the condition “a decimal one month and string the next”.

D:

Canonical names reduce failures from localized source schemas. The boundary it addresses is “multilingual source changes column headers based on localization”, while this case concerns “a decimal one month and string the next”.

Question 89

A CSV producer changes from commas to semicolons and the import collapses into one column. What should be fixed?

  1. Normalize source header names to stable canonical names before later transforms.
  2. Select and type a stable required field set rather than ingest every changing column.
  3. Update the delimiter setting in the source parsing step.
  4. Identify schema drift and update the dependent transformation step.

Explanation

Correct Answer

C:

Update the delimiter setting in the source parsing step. Incorrect delimiters prevent correct column separation.

Incorrect Answers

A:

Canonical names reduce failures from localized source schemas. The boundary it addresses is “multilingual source changes column headers based on localization”, while this case concerns “semicolons and the import collapses into one column”.

B:

Explicit schemas insulate models from irrelevant source columns. This solves “source field should not change report measure definitions”; the stated issue is “semicolons and the import collapses into one column”.

D:

Identify schema drift and update the dependent transformation step. Its application is “source removes a field referenced by Power Query”, not the need “semicolons and the import collapses into one column”.

Question 90

A multilingual source changes column headers based on localization. What pipeline design is prudent?

  1. Identify schema drift and update the dependent transformation step.
  2. Enforce a documented type contract with validation before model loading.
  3. Select and type a stable required field set rather than ingest every changing column.
  4. Normalize source header names to stable canonical names before later transforms.

Explanation

Correct Answer

D:

Normalize source header names to stable canonical names before later transforms. Canonical names reduce failures from localized source schemas.

Incorrect Answers

A:

Identify schema drift and update the dependent transformation step. Its application is “source removes a field referenced by Power Query”, not the need “multilingual source changes column headers based on localization”.

B:

Schema contracts catch incompatible types before they silently alter results. That is a different operating condition from “multilingual source changes column headers based on localization”: “a decimal one month and string the next”.

C:

Explicit schemas insulate models from irrelevant source columns. It serves “source field should not change report measure definitions”, not “multilingual source changes column headers based on localization”.