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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 12

Question 110

A data quality team needs the number of distinct CustomerIDs per market. What aggregation is appropriate?

  1. Group by Department and calculate Minimum HireDate.
  2. Group by Carrier and aggregate Count Rows.
  3. Group by Market and count distinct CustomerID values.
  4. Group by ProductCategory and calculate Average UnitCost.

Explanation

Correct Answer

C:

Group by Market and count distinct CustomerID values. Distinct-count aggregation avoids counting repeat appearances of the same customer.

Incorrect Answers

A:

Minimum selects the earliest date within each department. That matters for “file needs the oldest HireDate for each Department” rather than the condition “needs the number of distinct CustomerIDs per market”.

B:

Group By summarizes packages for each carrier value. The boundary it addresses is “operations needs a count of packages by carrier”, while this case concerns “needs the number of distinct CustomerIDs per market”.

D:

Average returns the arithmetic mean for each category group. The deciding difference is “A SKU table requires average UnitCost by ProductCategory” versus “needs the number of distinct CustomerIDs per market”.

Question 111

A Sales fact must retain every transaction while optionally bringing in a matching Product name. Which join is best?

  1. Use a Left Outer merge from Sales to Product.
  2. Use Table.Join with JoinKind.LeftSemi to retain CRM rows with a match.
  3. Use an Inner merge on the normalized customer key.
  4. Use a Left Anti merge from CRM to ERP.

Explanation

Correct Answer

A:

Use a Left Outer merge from Sales to Product. Left Outer preserves every left fact row even without a product match.

Incorrect Answers

B:

A semi join tests whether a right-side match exists without multiplying left rows by multiple matches. Its application is “Use Table.Join with JoinKind.LeftSemi to retain CRM rows”, not the need “while optionally bringing in a matching Product name”.

C:

Inner joins keep matched keys from both tables. That is a different operating condition from “while optionally bringing in a matching Product name”: “appear in both two source lists”.

D:

Left Anti returns left records without right-side matches. It serves “customers appearing in CRM but absent from ERP”, not “while optionally bringing in a matching Product name”.

Question 112

A reconciliation report should show only customers that appear in both two source lists. Which join fits?

  1. Use a Full Outer merge of both lists.
  2. Use Table.Join with JoinKind.LeftSemi to retain CRM rows with a match.
  3. Use a Left Anti merge from CRM to ERP.
  4. Use an Inner merge on the normalized customer key.

Explanation

Correct Answer

D:

Use an Inner merge on the normalized customer key. Inner joins keep matched keys from both tables.

Incorrect Answers

A:

Full Outer retains all records from either side with nulls where unmatched. It serves “every unmatched and matched key from both systems”, not “appear in both two source lists”.

B:

A semi join tests whether a right-side match exists without multiplying left rows by multiple matches. The present requirement is “appear in both two source lists”; “Use Table.Join with JoinKind.LeftSemi to retain CRM rows” is a distinct situation.

C:

Left Anti returns left records without right-side matches. That matters for “customers appearing in CRM but absent from ERP” rather than the condition “appear in both two source lists”.

Question 113

A quality audit needs customers appearing in CRM but absent from ERP. Which join fits?

  1. Use a Left Anti merge from CRM to ERP.
  2. Use a Left Outer merge from Sales to Product.
  3. Use Table.Join with JoinKind.LeftSemi to retain CRM rows with a match.
  4. Use a Full Outer merge of both lists.

Explanation

Correct Answer

A:

Use a Left Anti merge from CRM to ERP. Left Anti returns left records without right-side matches.

Incorrect Answers

B:

Left Outer preserves every left fact row even without a product match. This solves “while optionally bringing in a matching Product name”; the stated issue is “customers appearing in CRM but absent from ERP”.

C:

A semi join tests whether a right-side match exists without multiplying left rows by multiple matches. The deciding difference is “Use Table.Join with JoinKind.LeftSemi to retain CRM rows” versus “customers appearing in CRM but absent from ERP”.

D:

Full Outer retains all records from either side with nulls where unmatched. Its application is “every unmatched and matched key from both systems”, not the need “customers appearing in CRM but absent from ERP”.

Question 114

A full reconciliation must show every unmatched and matched key from both systems. Which join fits?

  1. Use Table.Join with JoinKind.LeftSemi to retain CRM rows with a match.
  2. Use a Left Anti merge from CRM to ERP.
  3. Use a Full Outer merge of both lists.
  4. Use a Left Outer merge from Sales to Product.

Explanation

Correct Answer

C:

Use a Full Outer merge of both lists. Full Outer retains all records from either side with nulls where unmatched.

Incorrect Answers

A:

A semi join tests whether a right-side match exists without multiplying left rows by multiple matches. Its application is “Use Table.Join with JoinKind.LeftSemi to retain CRM rows”, not the need “every unmatched and matched key from both systems”.

B:

Left Anti returns left records without right-side matches. That is a different operating condition from “every unmatched and matched key from both systems”: “customers appearing in CRM but absent from ERP”.

D:

Left Outer preserves every left fact row even without a product match. The present requirement is “every unmatched and matched key from both systems”; “while optionally bringing in a matching Product name” is a distinct situation.

Question 115

A marketer must retain CRM contacts with a matching opt-in entry but must not add duplicate rows when opt-in events repeat. Which Power Query M pattern fits?

  1. Use a Full Outer merge of both lists.
  2. Use a Left Outer merge from Sales to Product.
  3. Use Table.Join with JoinKind.LeftSemi to retain CRM rows with a match.
  4. Use an Inner merge on the normalized customer key.

Explanation

Correct Answer

C:

Use Table.Join with JoinKind.LeftSemi to retain CRM rows with a match. A semi join tests whether a right-side match exists without multiplying left rows by multiple matches.

Incorrect Answers

A:

Full Outer retains all records from either side with nulls where unmatched. The present requirement is “Use Table.Join with JoinKind.LeftSemi to retain CRM rows”; “every unmatched and matched key from both systems” is a distinct situation.

B:

Left Outer preserves every left fact row even without a product match. That matters for “while optionally bringing in a matching Product name” rather than the condition “Use Table.Join with JoinKind.LeftSemi to retain CRM rows”.

D:

Inner joins keep matched keys from both tables. This solves “appear in both two source lists”; the stated issue is “Use Table.Join with JoinKind.LeftSemi to retain CRM rows”.

Question 116

Quarter-one and quarter-two CSV files share identical columns, and the analyst wants one longer transaction table. Which operation fits?

  1. Merge Orders with Customer on CustomerID and expand City.
  2. Append them and expect nulls for columns absent in a source.
  3. Deduplicate or remodel the customer lookup before expanding the merge.
  4. Append the quarterly tables to stack their rows.

Explanation

Correct Answer

D:

Append the quarterly tables to stack their rows. Append unions rows and aligns columns by name.

Incorrect Answers

A:

A merge joins columns using a key, unlike appending additional transactions. This solves “the report needs City alongside each Order”; the stated issue is “and the analyst wants one longer transaction table”.

B:

Power Query aligns appended columns by name and fills missing cells with null. The deciding difference is “different column sets, with one optional SourceChannel column” versus “and the analyst wants one longer transaction table”.

C:

Deduplicate or remodel the customer lookup before expanding the merge. Its application is “and a fact merge unexpectedly doubles some rows”, not the need “and the analyst wants one longer transaction table”.

Question 117

An Orders table contains CustomerID, and the Customer table contains City; the report needs City alongside each Order. What fits?

  1. Append them and expect nulls for columns absent in a source.
  2. Merge Orders with Customer on CustomerID and expand City.
  3. Deduplicate or remodel the customer lookup before expanding the merge.
  4. Replace that merge with Append Queries for same-schema row concatenation.

Explanation

Correct Answer

B:

Merge Orders with Customer on CustomerID and expand City. A merge joins columns using a key, unlike appending additional transactions.

Incorrect Answers

A:

Power Query aligns appended columns by name and fills missing cells with null. That is a different operating condition from “the report needs City alongside each Order”: “different column sets, with one optional SourceChannel column”.

C:

Deduplicate or remodel the customer lookup before expanding the merge. The present requirement is “the report needs City alongside each Order”; “and a fact merge unexpectedly doubles some rows” is a distinct situation.

D:

Merge joins across keys; append adds vertical records. That matters for “gets many null columns instead of stacked records” rather than the condition “the report needs City alongside each Order”.

Question 118

Two daily files have different column sets, with one optional SourceChannel column. What can Append produce?

  1. Replace that merge with Append Queries for same-schema row concatenation.
  2. Deduplicate or remodel the customer lookup before expanding the merge.
  3. Append them and expect nulls for columns absent in a source.
  4. Append the quarterly tables to stack their rows.

Explanation

Correct Answer

C:

Append them and expect nulls for columns absent in a source. Power Query aligns appended columns by name and fills missing cells with null.

Incorrect Answers

A:

Merge joins across keys; append adds vertical records. That matters for “gets many null columns instead of stacked records” rather than the condition “different column sets, with one optional SourceChannel column”.

B:

Deduplicate or remodel the customer lookup before expanding the merge. The boundary it addresses is “and a fact merge unexpectedly doubles some rows”, while this case concerns “different column sets, with one optional SourceChannel column”.

D:

Append unions rows and aligns columns by name. The deciding difference is “and the analyst wants one longer transaction table” versus “different column sets, with one optional SourceChannel column”.

Question 119

A customer lookup has multiple matches per CustomerID, and a fact merge unexpectedly doubles some rows. What should be fixed?

  1. Replace that merge with Append Queries for same-schema row concatenation.
  2. Merge Orders with Customer on CustomerID and expand City.
  3. Deduplicate or remodel the customer lookup before expanding the merge.
  4. Append them and expect nulls for columns absent in a source.

Explanation

Correct Answer

C:

Deduplicate or remodel the customer lookup before expanding the merge. Nonunique right-side keys can multiply merged fact rows.

Incorrect Answers

A:

Merge joins across keys; append adds vertical records. The deciding difference is “gets many null columns instead of stacked records” versus “and a fact merge unexpectedly doubles some rows”.

B:

A merge joins columns using a key, unlike appending additional transactions. Its application is “the report needs City alongside each Order”, not the need “and a fact merge unexpectedly doubles some rows”.

D:

Power Query aligns appended columns by name and fills missing cells with null. It serves “different column sets, with one optional SourceChannel column”, not “and a fact merge unexpectedly doubles some rows”.

Question 120

A team uses Merge to add same-schema monthly sales and gets many null columns instead of stacked records. What should change?

  1. Append the quarterly tables to stack their rows.
  2. Replace that merge with Append Queries for same-schema row concatenation.
  3. Append them and expect nulls for columns absent in a source.
  4. Deduplicate or remodel the customer lookup before expanding the merge.

Explanation

Correct Answer

B:

Replace that merge with Append Queries for same-schema row concatenation. Merge joins across keys; append adds vertical records.

Incorrect Answers

A:

Append unions rows and aligns columns by name. It serves “and the analyst wants one longer transaction table”, not “gets many null columns instead of stacked records”.

C:

Power Query aligns appended columns by name and fills missing cells with null. That matters for “different column sets, with one optional SourceChannel column” rather than the condition “gets many null columns instead of stacked records”.

D:

Deduplicate or remodel the customer lookup before expanding the merge. The boundary it addresses is “and a fact merge unexpectedly doubles some rows”, while this case concerns “gets many null columns instead of stacked records”.