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Microsoft PL-300 Practice Test 16

Question 150

An analyst must retain every sale even when a matching product master row is missing. A colleague proposes: Use an Inner join and assume unmatched sales are kept. Which explanation BEST identifies the defect in that proposal?

  1. A join may duplicate or drop rows instead of adding region transactions
  2. A left outer join preserves all rows from the left fact query
  3. The suggested approach is safe merely because its product name is familiar.
  4. Inner joins remove fact rows lacking a matching dimension key

Explanation

Correct Answer

D:

Inner joins remove fact rows lacking a matching dimension key. This directly explains why the proposal 'Use an Inner join and assume unmatched sales are kept' should not be used.

Incorrect Answers

A:

This addresses a different case: An analyst combines two tables that have the same columns but distinct store-region rows. It is not the specific defect in 'Use an Inner join and assume unmatched sales are kept'.

B:

This is reasoning supporting the preferred solution 'Use a Left Outer merge from Sales to Products', but it does not directly name the defect in the proposed shortcut.

C:

Product familiarity is not evidence of suitability. The actual defect in this proposal is: Inner joins remove fact rows lacking a matching dimension key.

Question 151

An analyst must retain every sale even when a matching product master row is missing. After implementing Use a Left Outer merge from Sales to Products, which observation BEST demonstrates that the intended result is actually working?

  1. The report owner approves Use a Left Outer merge from Sales to Products without testing the resulting behavior.
  2. Output row count is the sum of the valid input rows
  3. Fact row counts remain unchanged and unmatched products are identifiable
  4. The team schedules a future check of whether Fact row counts remain unchanged and unmatched products are identifiable, but has not observed a result.

Explanation

Correct Answer

C:

The observation 'Fact row counts remain unchanged and unmatched products are identifiable' directly tests the intended result. A left outer join preserves all rows from the left fact query.

Incorrect Answers

A:

Approval of a proposed design is not evidence of working behavior. The required observation is: Fact row counts remain unchanged and unmatched products are identifiable.

B:

This belongs to a different case: An analyst combines two tables that have the same columns but distinct store-region rows. It does not validate the current change.

D:

A future test is not yet observed operational evidence. The verification must establish: Fact row counts remain unchanged and unmatched products are identifiable.

Question 152

The original scenario was: An analyst must retain every sale even when a matching product master row is missing. The initial solution is already in place. Now this new requirement arises: Business rules later require discarding unrecognized products for an export. Which ADDITIONAL action BEST meets the changed condition?

  1. Use an Inner join and assume unmatched sales are kept
  2. Apply an explicitly approved Inner join to the export-specific query
  3. Use a Left Outer merge from Sales to Products
  4. Fact row counts remain unchanged and unmatched products are identifiable

Explanation

Correct Answer

B:

The changed requirement is 'Business rules later require discarding unrecognized products for an export'. The additional response is 'Apply an explicitly approved Inner join to the export-specific query'.

Incorrect Answers

A:

This remains unsuitable for the original situation: Inner joins remove fact rows lacking a matching dimension key. It also does not meet the changed need.

C:

This is the already implemented original solution: Use a Left Outer merge from Sales to Products. A left outer join preserves all rows from the left fact query. It does not itself solve the new requirement.

D:

This observes the original approach but is not an additional action: Fact row counts remain unchanged and unmatched products are identifiable.

Question 153

A web API returns JSON with nested Customer and Addresses records that must become flat columns. Which approach BEST meets the stated Microsoft Power BI requirement?

  1. Expand address lists into related child rows with a stable parent key
  2. Convert JSON lists and records to tables and expand the required fields
  3. Rename the nested JSON field without expanding it
  4. Add a conditional column implementing the accepted price rule

Explanation

Correct Answer

B:

Power Query supports structured expansion of nested semi-structured data. For the stated case, Convert JSON lists and records to tables and expand the required fields.

Incorrect Answers

A:

This might be appropriate after a different requirement arises: One customer can have multiple shipping addresses. It is not the best initial response to the stated case.

C:

Renaming alone leaves the record data nested. The proposed shortcut 'Rename the nested JSON field without expanding it' fails the documented need.

D:

This addresses a separate source situation: A product list needs an IsPremium text flag based on Price greater than 500. It does not directly satisfy this case's priority.

Question 154

A web API returns JSON with nested Customer and Addresses records that must become flat columns. A colleague proposes: Rename the nested JSON field without expanding it. Which explanation BEST identifies the defect in that proposal?

  1. Renaming alone leaves the record data nested
  2. The suggested approach is safe merely because its product name is familiar.
  3. Sorting changes display order rather than creating rule-based flags
  4. Power Query supports structured expansion of nested semi-structured data

Explanation

Correct Answer

A:

Renaming alone leaves the record data nested. This directly explains why the proposal 'Rename the nested JSON field without expanding it' should not be used.

Incorrect Answers

B:

Product familiarity is not evidence of suitability. The actual defect in this proposal is: Renaming alone leaves the record data nested.

C:

This addresses a different case: A product list needs an IsPremium text flag based on Price greater than 500. It is not the specific defect in 'Rename the nested JSON field without expanding it'.

D:

This is reasoning supporting the preferred solution 'Convert JSON lists and records to tables and expand the required fields', but it does not directly name the defect in the proposed shortcut.

Question 155

A web API returns JSON with nested Customer and Addresses records that must become flat columns. After implementing Convert JSON lists and records to tables and expand the required fields, which observation BEST demonstrates that the intended result is actually working?

  1. Rows over 500 are marked premium and other rows follow the rule
  2. Customer and address properties appear as typed columns after expansion
  3. The team schedules a future check of whether Customer and address properties appear as typed columns after expansion, but has not observed a result.
  4. The report owner approves Convert JSON lists and records to tables and expand the required fields without testing the resulting behavior.

Explanation

Correct Answer

B:

The observation 'Customer and address properties appear as typed columns after expansion' directly tests the intended result. Power Query supports structured expansion of nested semi-structured data.

Incorrect Answers

A:

This belongs to a different case: A product list needs an IsPremium text flag based on Price greater than 500. It does not validate the current change.

C:

A future test is not yet observed operational evidence. The verification must establish: Customer and address properties appear as typed columns after expansion.

D:

Approval of a proposed design is not evidence of working behavior. The required observation is: Customer and address properties appear as typed columns after expansion.

Question 156

The original scenario was: A web API returns JSON with nested Customer and Addresses records that must become flat columns. The initial solution is already in place. Now this new requirement arises: One customer can have multiple shipping addresses. Which ADDITIONAL action BEST meets the changed condition?

  1. Customer and address properties appear as typed columns after expansion
  2. Convert JSON lists and records to tables and expand the required fields
  3. Expand address lists into related child rows with a stable parent key
  4. Rename the nested JSON field without expanding it

Explanation

Correct Answer

C:

The changed requirement is 'One customer can have multiple shipping addresses'. The additional response is 'Expand address lists into related child rows with a stable parent key'.

Incorrect Answers

A:

This observes the original approach but is not an additional action: Customer and address properties appear as typed columns after expansion.

B:

This is the already implemented original solution: Convert JSON lists and records to tables and expand the required fields. Power Query supports structured expansion of nested semi-structured data. It does not itself solve the new requirement.

D:

This remains unsuitable for the original situation: Renaming alone leaves the record data nested. It also does not meet the changed need.

Question 157

A location file has City and State concatenated as Austin, TX in one column. Which TWO entries together identify the most appropriate initial action and direct observed evidence of its success? (Select TWO.)

  1. City and State load separately with no leading spaces
  2. Transpose the entire table to split every location string
  3. Use proper CSV quoting or a robust parsing rule instead of naive splitting
  4. Split the column by its documented comma delimiter and trim results
  5. Unpivot the monthly columns to Month and Value rows

Explanation

Correct Answers

A:

Correct direct operational evidence. City and State load separately with no leading spaces. This is a check of the original requirement, not an unperformed plan.

D:

Splitting a delimited field creates separate analyzable attributes. For the stated case, Split the column by its documented comma delimiter and trim results.

Incorrect Answers

B:

Transposition changes orientation but does not parse delimiters. The proposed shortcut 'Transpose the entire table to split every location string' fails the documented need.

C:

This might be appropriate after a different requirement arises: Some locations now include commas inside quoted city names. It is not the best initial response to the stated case.

E:

This addresses a separate source situation: A spreadsheet contains January, February, and March as separate columns, and users need a Month field for plotting. It does not directly satisfy this case's priority.

Question 158

A location file has City and State concatenated as Austin, TX in one column. A colleague proposes: Transpose the entire table to split every location string. Which explanation BEST identifies the defect in that proposal?

  1. Pivot would create the opposite shape from the requested tidy fact table
  2. Splitting a delimited field creates separate analyzable attributes
  3. The suggested approach is safe merely because its product name is familiar.
  4. Transposition changes orientation but does not parse delimiters

Explanation

Correct Answer

D:

Transposition changes orientation but does not parse delimiters. This directly explains why the proposal 'Transpose the entire table to split every location string' should not be used.

Incorrect Answers

A:

This addresses a different case: A spreadsheet contains January, February, and March as separate columns, and users need a Month field for plotting. It is not the specific defect in 'Transpose the entire table to split every location string'.

B:

This is reasoning supporting the preferred solution 'Split the column by its documented comma delimiter and trim results', but it does not directly name the defect in the proposed shortcut.

C:

Product familiarity is not evidence of suitability. The actual defect in this proposal is: Transposition changes orientation but does not parse delimiters.

Question 159

A location file has City and State concatenated as Austin, TX in one column. After implementing Split the column by its documented comma delimiter and trim results, which observation BEST demonstrates that the intended result is actually working?

  1. City and State load separately with no leading spaces
  2. The output has one Month column and one numeric Value column
  3. The team schedules a future check of whether City and State load separately with no leading spaces, but has not observed a result.
  4. The report owner approves Split the column by its documented comma delimiter and trim results without testing the resulting behavior.

Explanation

Correct Answer

A:

The observation 'City and State load separately with no leading spaces' directly tests the intended result. Splitting a delimited field creates separate analyzable attributes.

Incorrect Answers

B:

This belongs to a different case: A spreadsheet contains January, February, and March as separate columns, and users need a Month field for plotting. It does not validate the current change.

C:

A future test is not yet observed operational evidence. The verification must establish: City and State load separately with no leading spaces.

D:

Approval of a proposed design is not evidence of working behavior. The required observation is: City and State load separately with no leading spaces.

Question 160

The original scenario was: A location file has City and State concatenated as Austin, TX in one column. The initial solution is already in place. Now this new requirement arises: Some locations now include commas inside quoted city names. Which ADDITIONAL action BEST meets the changed condition?

  1. City and State load separately with no leading spaces
  2. Transpose the entire table to split every location string
  3. Split the column by its documented comma delimiter and trim results
  4. Use proper CSV quoting or a robust parsing rule instead of naive splitting

Explanation

Correct Answer

D:

The changed requirement is 'Some locations now include commas inside quoted city names'. The additional response is 'Use proper CSV quoting or a robust parsing rule instead of naive splitting'.

Incorrect Answers

A:

This observes the original approach but is not an additional action: City and State load separately with no leading spaces.

B:

This remains unsuitable for the original situation: Transposition changes orientation but does not parse delimiters. It also does not meet the changed need.

C:

This is the already implemented original solution: Split the column by its documented comma delimiter and trim results. Splitting a delimited field creates separate analyzable attributes. It does not itself solve the new requirement.

Microsoft Power BI Data Analyst (PL-300) Practice Tests