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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 16

Question 150

A source returns nested JSON arrays; the analyst wants each array element to become a separate record. What should be done?

  1. Write a Power Query M conditional expression using if then else.
  2. Preserve the dependency order of Power Query transformation steps.
  3. Expand the list to new rows and then expand its records.
  4. Filter rows in Power Query before loading the table.

Explanation

Correct Answer

C:

Expand the list to new rows and then expand its records. List expansion creates one row per nested element before its fields are expanded.

Incorrect Answers

A:

M expressions define row-level transformations at data preparation time. That matters for “must branch according to a null-safe status field” rather than the condition “each array element to become a separate record”.

B:

M evaluation composes named steps whose downstream references require upstream results. The boundary it addresses is “Steps in which later steps reference earlier names”, while this case concerns “each array element to become a separate record”.

D:

A row filter can reduce the imported dataset and may fold to the source. The deciding difference is “keeps rows with Quantity > 0” versus “each array element to become a separate record”.

Question 151

A finance extract has dates from multiple time zones and needs grouping by a designated reporting timezone. What should the author do?

  1. Derive fiscal year and quarter attributes from the approved fiscal calendar.
  2. Convert numeric columns using the source locale during Power Query transformation.
  3. Convert timestamps to the reporting timezone before deriving calendar dates.
  4. Extract a Date value for the relationship key while retaining the original time where needed.

Explanation

Correct Answer

C:

Convert timestamps to the reporting timezone before deriving calendar dates. Grouping raw timestamps by date without timezone alignment can shift transactions across days.

Incorrect Answers

A:

Fiscal periods are business-specific rather than automatically matching Gregorian quarters. The deciding difference is “reports need fiscal quarters distinct from calendar quarters” versus “and needs grouping by a designated reporting timezone”.

B:

Locale-based parsing prevents numeric values being misinterpreted as text. Its application is “as 12,5 in a locale expecting decimal points”, not the need “and needs grouping by a designated reporting timezone”.

D:

A date-only key aligns to the calendar dimension grain. It serves “time, but a date-level dimension uses date-only keys”, not “and needs grouping by a designated reporting timezone”.

Question 152

A report compares values in decimal kilometers and miles sourced from separate systems. What transformation fits?

  1. Normalize distance measurements to a common unit before combining.
  2. Convert timestamps to the reporting timezone before deriving calendar dates.
  3. Convert numeric columns using the source locale during Power Query transformation.
  4. Extract a Date value for the relationship key while retaining the original time where needed.

Explanation

Correct Answer

A:

Normalize distance measurements to a common unit before combining. Consistent measurement units are necessary for valid totals and comparisons.

Incorrect Answers

B:

Grouping raw timestamps by date without timezone alignment can shift transactions across days. The present requirement is “decimal kilometers and miles sourced from separate systems”; “and needs grouping by a designated reporting timezone” is a distinct situation.

C:

Locale-based parsing prevents numeric values being misinterpreted as text. That matters for “as 12,5 in a locale expecting decimal points” rather than the condition “decimal kilometers and miles sourced from separate systems”.

D:

A date-only key aligns to the calendar dimension grain. The boundary it addresses is “time, but a date-level dimension uses date-only keys”, while this case concerns “decimal kilometers and miles sourced from separate systems”.

Question 153

A fiscal calendar starts in July and reports need fiscal quarters distinct from calendar quarters. What design helps?

  1. Convert timestamps to the reporting timezone before deriving calendar dates.
  2. Convert numeric columns using the source locale during Power Query transformation.
  3. Extract a Date value for the relationship key while retaining the original time where needed.
  4. Derive fiscal year and quarter attributes from the approved fiscal calendar.

Explanation

Correct Answer

D:

Derive fiscal year and quarter attributes from the approved fiscal calendar. Fiscal periods are business-specific rather than automatically matching Gregorian quarters.

Incorrect Answers

A:

Grouping raw timestamps by date without timezone alignment can shift transactions across days. The boundary it addresses is “and needs grouping by a designated reporting timezone”, while this case concerns “reports need fiscal quarters distinct from calendar quarters”.

B:

Locale-based parsing prevents numeric values being misinterpreted as text. This solves “as 12,5 in a locale expecting decimal points”; the stated issue is “reports need fiscal quarters distinct from calendar quarters”.

C:

A date-only key aligns to the calendar dimension grain. The deciding difference is “time, but a date-level dimension uses date-only keys” versus “reports need fiscal quarters distinct from calendar quarters”.

Question 154

A DateTime value includes a time, but a date-level dimension uses date-only keys. Which preparation step fits?

  1. Convert numeric columns using the source locale during Power Query transformation.
  2. Extract a Date value for the relationship key while retaining the original time where needed.
  3. Convert timestamps to the reporting timezone before deriving calendar dates.
  4. Normalize distance measurements to a common unit before combining.

Explanation

Correct Answer

B:

Extract a Date value for the relationship key while retaining the original time where needed. A date-only key aligns to the calendar dimension grain.

Incorrect Answers

A:

Locale-based parsing prevents numeric values being misinterpreted as text. Its application is “as 12,5 in a locale expecting decimal points”, not the need “time, but a date-level dimension uses date-only keys”.

C:

Grouping raw timestamps by date without timezone alignment can shift transactions across days. It serves “and needs grouping by a designated reporting timezone”, not “time, but a date-level dimension uses date-only keys”.

D:

Consistent measurement units are necessary for valid totals and comparisons. The present requirement is “time, but a date-level dimension uses date-only keys”; “decimal kilometers and miles sourced from separate systems” is a distinct situation.

Question 155

A source CSV contains decimal commas such as 12,5 in a locale expecting decimal points. How should it be loaded?

  1. Derive fiscal year and quarter attributes from the approved fiscal calendar.
  2. Convert timestamps to the reporting timezone before deriving calendar dates.
  3. Convert numeric columns using the source locale during Power Query transformation.
  4. Extract a Date value for the relationship key while retaining the original time where needed.

Explanation

Correct Answer

C:

Convert numeric columns using the source locale during Power Query transformation. Locale-based parsing prevents numeric values being misinterpreted as text.

Incorrect Answers

A:

Fiscal periods are business-specific rather than automatically matching Gregorian quarters. The present requirement is “as 12,5 in a locale expecting decimal points”; “reports need fiscal quarters distinct from calendar quarters” is a distinct situation.

B:

Grouping raw timestamps by date without timezone alignment can shift transactions across days. That matters for “and needs grouping by a designated reporting timezone” rather than the condition “as 12,5 in a locale expecting decimal points”.

D:

A date-only key aligns to the calendar dimension grain. This solves “time, but a date-level dimension uses date-only keys”; the stated issue is “as 12,5 in a locale expecting decimal points”.

Question 156

A model needs sales at the transaction grain and an additional aggregate summary for performance. Which design is safer?

  1. Keep the detailed fact and create a separate aggregate table as needed.
  2. Preserve semantic differences between missing and genuine zero values.
  3. Rework the row-wise external call pattern to avoid excessive network operations.
  4. Fold source-side filters and projections before expensive non-foldable steps.

Explanation

Correct Answer

A:

Keep the detailed fact and create a separate aggregate table as needed. Supplementary aggregates should not erase detail required by other analyses.

Incorrect Answers

B:

Preserve semantic differences between missing and genuine zero values. The deciding difference is “but a custom transformation converts both to zero” versus “grain and an additional aggregate summary for performance”.

C:

Repeated remote calls can dominate refresh latency and violate rate limits. Its application is “row-by-row custom web request for 1 million rows”, not the need “grain and an additional aggregate summary for performance”.

D:

Delegating selective operations reduces refresh data transfer and processing. That is a different operating condition from “grain and an additional aggregate summary for performance”: “required rows and columns. Where should filtering occur”.

Question 157

A query references a remote source and performs a row-by-row custom web request for 1 million rows. What concern matters?

  1. Keep the detailed fact and create a separate aggregate table as needed.
  2. Consume the governed transformed dataflow output where appropriate.
  3. Rework the row-wise external call pattern to avoid excessive network operations.
  4. Fold source-side filters and projections before expensive non-foldable steps.

Explanation

Correct Answer

C:

Rework the row-wise external call pattern to avoid excessive network operations. Repeated remote calls can dominate refresh latency and violate rate limits.

Incorrect Answers

A:

Supplementary aggregates should not erase detail required by other analyses. That is a different operating condition from “row-by-row custom web request for 1 million rows”: “grain and an additional aggregate summary for performance”.

B:

Consume the governed transformed dataflow output where appropriate. It serves “A dataflow already standardizes CustomerIDs for several reports”, not “row-by-row custom web request for 1 million rows”.

D:

Delegating selective operations reduces refresh data transfer and processing. That matters for “required rows and columns. Where should filtering occur” rather than the condition “row-by-row custom web request for 1 million rows”.

Question 158

A dataflow already standardizes CustomerIDs for several reports. What reuse pattern helps?

  1. Preserve semantic differences between missing and genuine zero values.
  2. Keep the detailed fact and create a separate aggregate table as needed.
  3. Consume the governed transformed dataflow output where appropriate.
  4. Fold source-side filters and projections before expensive non-foldable steps.

Explanation

Correct Answer

C:

Consume the governed transformed dataflow output where appropriate. Shared upstream transformations reduce duplicated ETL logic.

Incorrect Answers

A:

Preserve semantic differences between missing and genuine zero values. That matters for “but a custom transformation converts both to zero” rather than the condition “A dataflow already standardizes CustomerIDs for several reports”.

B:

Supplementary aggregates should not erase detail required by other analyses. The boundary it addresses is “grain and an additional aggregate summary for performance”, while this case concerns “A dataflow already standardizes CustomerIDs for several reports”.

D:

Delegating selective operations reduces refresh data transfer and processing. The deciding difference is “required rows and columns. Where should filtering occur” versus “A dataflow already standardizes CustomerIDs for several reports”.

Question 159

A numeric column contains both blank and zero entries but a custom transformation converts both to zero. What should change?

  1. Fold source-side filters and projections before expensive non-foldable steps.
  2. Keep the detailed fact and create a separate aggregate table as needed.
  3. Preserve semantic differences between missing and genuine zero values.
  4. Consume the governed transformed dataflow output where appropriate.

Explanation

Correct Answer

C:

Preserve semantic differences between missing and genuine zero values. Blank/zero conflation can alter counts and aggregations.

Incorrect Answers

A:

Delegating selective operations reduces refresh data transfer and processing. The deciding difference is “required rows and columns. Where should filtering occur” versus “but a custom transformation converts both to zero”.

B:

Supplementary aggregates should not erase detail required by other analyses. Its application is “grain and an additional aggregate summary for performance”, not the need “but a custom transformation converts both to zero”.

D:

Consume the governed transformed dataflow output where appropriate. It serves “A dataflow already standardizes CustomerIDs for several reports”, not “but a custom transformation converts both to zero”.

Question 160

A database query has a foldable selection of only required rows and columns. Where should filtering occur?

  1. Preserve semantic differences between missing and genuine zero values.
  2. Fold source-side filters and projections before expensive non-foldable steps.
  3. Keep the detailed fact and create a separate aggregate table as needed.
  4. Consume the governed transformed dataflow output where appropriate.

Explanation

Correct Answer

B:

Fold source-side filters and projections before expensive non-foldable steps. Delegating selective operations reduces refresh data transfer and processing.

Incorrect Answers

A:

Preserve semantic differences between missing and genuine zero values. It serves “but a custom transformation converts both to zero”, not “required rows and columns. Where should filtering occur”.

C:

Supplementary aggregates should not erase detail required by other analyses. That matters for “grain and an additional aggregate summary for performance” rather than the condition “required rows and columns. Where should filtering occur”.

D:

Consume the governed transformed dataflow output where appropriate. The boundary it addresses is “A dataflow already standardizes CustomerIDs for several reports”, while this case concerns “required rows and columns. Where should filtering occur”.