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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 11

Question 100

A contract stores durations as text like '3 hours' and '45 minutes'. Which cleaning is required?

  1. Convert monetary amounts into a consistent reporting currency before summing.
  2. Parse durations into a consistent numeric unit or duration type.
  3. Standardize accounting definitions and currency treatment by documented rules.
  4. Preserve valid rows when only optional descriptive fields are absent.

Explanation

Correct Answer

B:

Parse durations into a consistent numeric unit or duration type. Consistent units enable valid comparisons and aggregation.

Incorrect Answers

A:

Convert monetary amounts into a consistent reporting currency before summing. This solves “denominated in EUR and USD without exchange conversion”; the stated issue is “as text like '3 hours' and '45 minutes'”.

C:

Combining incompatible financial measures without normalization distorts totals. Its application is “one region and tax exclusive amounts in another”, not the need “as text like '3 hours' and '45 minutes'”.

D:

Optional null text does not necessarily invalidate a transaction record. That is a different operating condition from “as text like '3 hours' and '45 minutes'”: “optional comments even though numeric fields are valid”.

Question 101

A budget workbook has columns Jan, Feb and Mar, and the report needs one Month field with corresponding Amount values. Which transformation fits?

  1. Expand the record column into named fields.
  2. Expand the nested table column into multiple detail rows.
  3. Unpivot the month columns into attribute and value rows.
  4. Pivot the Month column using an explicit Amount aggregation.

Explanation

Correct Answer

C:

Unpivot the month columns into attribute and value rows. Unpivot converts wide period columns into a normalized long layout.

Incorrect Answers

A:

Expand the record column into named fields. That is a different operating condition from “needs one Month field with corresponding Amount values”: “row, and the analyst needs its City field”.

B:

Table expansion flattens nested collections rather than keeping a single cell per parent. It serves “and the report needs one row per line”, not “needs one Month field with corresponding Amount values”.

D:

Pivot converts category values into columns while resolving duplicate combinations with aggregation. That matters for “output needs a separate column for each Month” rather than the condition “needs one Month field with corresponding Amount values”.

Question 102

A sales table has row values Year, Month and Amount but the output needs a separate column for each Month. Which transformation fits?

  1. Pivot the Month column using an explicit Amount aggregation.
  2. Expand the nested table column into multiple detail rows.
  3. Expand the record column into named fields.
  4. Unpivot the month columns into attribute and value rows.

Explanation

Correct Answer

A:

Pivot the Month column using an explicit Amount aggregation. Pivot converts category values into columns while resolving duplicate combinations with aggregation.

Incorrect Answers

B:

Table expansion flattens nested collections rather than keeping a single cell per parent. The boundary it addresses is “and the report needs one row per line”, while this case concerns “output needs a separate column for each Month”.

C:

Expand the record column into named fields. This solves “row, and the analyst needs its City field”; the stated issue is “output needs a separate column for each Month”.

D:

Unpivot converts wide period columns into a normalized long layout. The deciding difference is “needs one Month field with corresponding Amount values” versus “output needs a separate column for each Month”.

Question 103

A two-column matrix has attributes stored as rows and observations as columns, requiring orientation reversal. What command should be evaluated?

  1. Transpose the matrix to exchange rows and columns.
  2. Pivot the Month column using an explicit Amount aggregation.
  3. Unpivot the month columns into attribute and value rows.
  4. Expand the record column into named fields.

Explanation

Correct Answer

A:

Transpose the matrix to exchange rows and columns. Transpose rotates the table axes without applying aggregation.

Incorrect Answers

B:

Pivot converts category values into columns while resolving duplicate combinations with aggregation. Its application is “output needs a separate column for each Month”, not the need “rows and observations as columns, requiring orientation reversal”.

C:

Unpivot converts wide period columns into a normalized long layout. That is a different operating condition from “rows and observations as columns, requiring orientation reversal”: “needs one Month field with corresponding Amount values”.

D:

Expand the record column into named fields. It serves “row, and the analyst needs its City field”, not “rows and observations as columns, requiring orientation reversal”.

Question 104

A Power Query table contains a nested record object in each row, and the analyst needs its City field. Which step fits?

  1. Pivot the Month column using an explicit Amount aggregation.
  2. Expand the record column into named fields.
  3. Unpivot the month columns into attribute and value rows.
  4. Transpose the matrix to exchange rows and columns.

Explanation

Correct Answer

B:

Expand the record column into named fields. Record expansion exposes nested attributes as columns.

Incorrect Answers

A:

Pivot converts category values into columns while resolving duplicate combinations with aggregation. It serves “output needs a separate column for each Month”, not “row, and the analyst needs its City field”.

C:

Unpivot converts wide period columns into a normalized long layout. That matters for “needs one Month field with corresponding Amount values” rather than the condition “row, and the analyst needs its City field”.

D:

Transpose rotates the table axes without applying aggregation. The boundary it addresses is “rows and observations as columns, requiring orientation reversal”, while this case concerns “row, and the analyst needs its City field”.

Question 105

A column contains a nested table of order lines, and the report needs one row per line. What should happen?

  1. Pivot the Month column using an explicit Amount aggregation.
  2. Expand the record column into named fields.
  3. Unpivot the month columns into attribute and value rows.
  4. Expand the nested table column into multiple detail rows.

Explanation

Correct Answer

D:

Expand the nested table column into multiple detail rows. Table expansion flattens nested collections rather than keeping a single cell per parent.

Incorrect Answers

A:

Pivot converts category values into columns while resolving duplicate combinations with aggregation. The boundary it addresses is “output needs a separate column for each Month”, while this case concerns “and the report needs one row per line”.

B:

Expand the record column into named fields. This solves “row, and the analyst needs its City field”; the stated issue is “and the report needs one row per line”.

C:

Unpivot converts wide period columns into a normalized long layout. The deciding difference is “needs one Month field with corresponding Amount values” versus “and the report needs one row per line”.

Question 106

A shipment extract has one row per package, but operations needs a count of packages by carrier. Which transformation fits?

  1. Group by Market and count distinct CustomerID values.
  2. Group by Department and calculate Minimum HireDate.
  3. Group by InvoiceID and sum Amount.
  4. Group by Carrier and aggregate Count Rows.

Explanation

Correct Answer

D:

Group by Carrier and aggregate Count Rows. Group By summarizes packages for each carrier value.

Incorrect Answers

A:

Distinct-count aggregation avoids counting repeat appearances of the same customer. Its application is “needs the number of distinct CustomerIDs per market”, not the need “operations needs a count of packages by carrier”.

B:

Minimum selects the earliest date within each department. That is a different operating condition from “operations needs a count of packages by carrier”: “file needs the oldest HireDate for each Department”.

C:

That recommendation applies when a transaction table needs the total Amount for each InvoiceID before joining to a summary model. It serves “each InvoiceID before joining to a summary model”, not “operations needs a count of packages by carrier”.

Question 107

A transaction table needs the total Amount for each InvoiceID before joining to a summary model. Which step fits?

  1. Group by Market and count distinct CustomerID values.
  2. Group by InvoiceID and sum Amount.
  3. Group by Carrier and aggregate Count Rows.
  4. Group by Department and calculate Minimum HireDate.

Explanation

Correct Answer

B:

Group by InvoiceID and sum Amount. Grouping with Sum produces an invoice-grain aggregate.

Incorrect Answers

A:

Distinct-count aggregation avoids counting repeat appearances of the same customer. The present requirement is “each InvoiceID before joining to a summary model”; “needs the number of distinct CustomerIDs per market” is a distinct situation.

C:

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 “each InvoiceID before joining to a summary model”.

D:

Minimum selects the earliest date within each department. This solves “file needs the oldest HireDate for each Department”; the stated issue is “each InvoiceID before joining to a summary model”.

Question 108

A workforce file needs the oldest HireDate for each Department. Which aggregation fits?

  1. Group by InvoiceID and sum Amount.
  2. Group by Market and count distinct CustomerID values.
  3. Group by ProductCategory and calculate Average UnitCost.
  4. Group by Department and calculate Minimum HireDate.

Explanation

Correct Answer

D:

Group by Department and calculate Minimum HireDate. Minimum selects the earliest date within each department.

Incorrect Answers

A:

That recommendation applies when a transaction table needs the total Amount for each InvoiceID before joining to a summary model. This solves “each InvoiceID before joining to a summary model”; the stated issue is “file needs the oldest HireDate for each Department”.

B:

Distinct-count aggregation avoids counting repeat appearances of the same customer. The deciding difference is “needs the number of distinct CustomerIDs per market” versus “file needs the oldest HireDate for each Department”.

C:

Average returns the arithmetic mean for each category group. Its application is “A SKU table requires average UnitCost by ProductCategory”, not the need “file needs the oldest HireDate for each Department”.

Question 109

A SKU table requires average UnitCost by ProductCategory. What Power Query operation fits?

  1. Group by ProductCategory and calculate Average UnitCost.
  2. Group by Carrier and aggregate Count Rows.
  3. Group by InvoiceID and sum Amount.
  4. Group by Market and count distinct CustomerID values.

Explanation

Correct Answer

A:

Group by ProductCategory and calculate Average UnitCost. Average returns the arithmetic mean for each category group.

Incorrect Answers

B:

Group By summarizes packages for each carrier value. It serves “operations needs a count of packages by carrier”, not “A SKU table requires average UnitCost by ProductCategory”.

C:

The relevant alternative case would be: A transaction table needs the total Amount for each InvoiceID before joining to a summary model. The present requirement is “A SKU table requires average UnitCost by ProductCategory”; “each InvoiceID before joining to a summary model” is a distinct situation.

D:

Distinct-count aggregation avoids counting repeat appearances of the same customer. That matters for “needs the number of distinct CustomerIDs per market” rather than the condition “A SKU table requires average UnitCost by ProductCategory”.

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”.