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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 17

Question 160

A Power BI team has selected this approach: Retain currency context and convert through a defined exchange-rate methodology before combined totals. The original business context is: An order table reports sales in different currencies and an analyst plans to sum raw amounts across currencies. What validation would be most relevant before accepting this change?

  1. Reconciled totals use a common reporting currency and state the rate date.
  2. A slicer can select one priority and filter its numeric count consistently.
  3. The fact holds numeric events and foreign keys while dimensions contain descriptive attributes.
  4. The output contains the expected named attribute columns for each entity.

Explanation

Correct Answer

A:

Reconciled totals use a common reporting currency and state the rate date. It provides the required observation for the chosen implementation: Retain currency context and convert through a defined exchange-rate methodology before combined totals. This test addresses the stated business constraint directly.

Incorrect Answers

B:

A slicer can select one priority and filter its numeric count consistently. That check would be meaningful for the separate case: A help desk export has ticket priorities distributed as columns rather than values, preventing a single priority slicer. It would not directly verify this scenario's selected implementation: Retain currency context and convert through a defined exchange-rate methodology before combined totals.

C:

The fact holds numeric events and foreign keys while dimensions contain descriptive attributes. That check would be meaningful for the separate case: A warehouse table combines ProductKey, CustomerKey, DateKey and SalesAmount events with repeated descriptions. It would not directly verify this scenario's selected implementation: Retain currency context and convert through a defined exchange-rate methodology before combined totals.

D:

The output contains the expected named attribute columns for each entity. That check would be meaningful for the separate case: A key-value table lists attributes in rows but the model needs each attribute as a field. It would not directly verify this scenario's selected implementation: Retain currency context and convert through a defined exchange-rate methodology before combined totals.

Question 161

A query's duplicate stage adds no new rows but creates two loaded copies of a large fact table. What should the Power BI analyst configure to meet the stated need?

  1. Group by CostCenter and FinancialYear with Sum over Amount.
  2. Disable load for the unnecessary duplicate or remove it after dependency review.
  3. Build a dimension with unique region keys and keep descriptive hierarchy attributes.
  4. Identify or create a stable surrogate key in an upstream or Power Query-supported process.

Explanation

Correct Answer

B:

Disable load for the unnecessary duplicate or remove it after dependency review. This targets the specific requirement described for this Power BI scenario. The corresponding acceptance check is: Only the intended fact table loads and dependent queries still work.

Incorrect Answers

A:

'Group by CostCenter and FinancialYear with Sum over Amount' is validated when 'Each group has one row with the independently checked sum of its expenses'. It does not demonstrate 'Only the intended fact table loads and dependent queries still work', which requires 'Disable load for the unnecessary duplicate or remove it after dependency review'.

C:

A successful 'Build a dimension with unique region keys and keep descriptive hierarchy attributes' would show 'The fact-to-region relationship uses one key and hierarchy labels do not cause key collisions'. This case needs 'Disable load for the unnecessary duplicate or remove it after dependency review' and evidence that 'Only the intended fact table loads and dependent queries still work', not the other outcome.

D:

The check for 'Identify or create a stable surrogate key in an upstream or Power Query-supported process' is 'Relationships use the stable key and product descriptions can change without merging unrelated entities'. That does not establish 'Only the intended fact table loads and dependent queries still work'; the stated requirement calls for 'Disable load for the unnecessary duplicate or remove it after dependency review'.

Question 162

A Power BI team has selected this approach: Disable load for the unnecessary duplicate or remove it after dependency review. The original business context is: A query's duplicate stage adds no new rows but creates two loaded copies of a large fact table. Which observation best indicates the requirement was met?

  1. Relationships use the stable key and product descriptions can change without merging unrelated entities.
  2. Only the intended fact table loads and dependent queries still work.
  3. Each group has one row with the independently checked sum of its expenses.
  4. The fact-to-region relationship uses one key and hierarchy labels do not cause key collisions.

Explanation

Correct Answer

B:

Only the intended fact table loads and dependent queries still work. It provides the required observation for the chosen implementation: Disable load for the unnecessary duplicate or remove it after dependency review. This test addresses the stated business constraint directly.

Incorrect Answers

A:

Relationships use the stable key and product descriptions can change without merging unrelated entities. That check would be meaningful for the separate case: A reporting source lacks a single stable key for each product because SKU values change on rebranding. It would not directly verify this scenario's selected implementation: Disable load for the unnecessary duplicate or remove it after dependency review.

C:

Each group has one row with the independently checked sum of its expenses. That check would be meaningful for the separate case: An expense detail table needs total amount grouped by CostCenter and FinancialYear before modeling. It would not directly verify this scenario's selected implementation: Disable load for the unnecessary duplicate or remove it after dependency review.

D:

The fact-to-region relationship uses one key and hierarchy labels do not cause key collisions. That check would be meaningful for the separate case: A product export includes region names with hierarchy levels in separate columns; the analyst needs a stable region key for joins. It would not directly verify this scenario's selected implementation: Disable load for the unnecessary duplicate or remove it after dependency review.

Question 163

A semantic model's fact table uses date-time stamps but needs to relate to a day-grain Date dimension. Which modeling or report-design decision most directly resolves this situation?

  1. Merge queries on CustomerID with a join that retains all required shipments.
  2. Derive a normalized Date-only key from the timestamps for the relationship.
  3. Keep the original DateTime relationship without normalizing the day grain or providing a compatible date-only key.
  4. Create a referenced query based on the cleaned base query.

Explanation

Correct Answer

B:

Derive a normalized Date-only key from the timestamps for the relationship. This targets the specific requirement described for this Power BI scenario. The corresponding acceptance check is: Every intended transaction matches the corresponding calendar date without time mismatches.

Incorrect Answers

A:

'Merge queries on CustomerID with a join that retains all required shipments' addresses the mechanism measured by 'Matched shipments receive segment values and unmatched shipments remain visible as intended'. The current outcome is 'Every intended transaction matches the corresponding calendar date without time mismatches' after 'Derive a normalized Date-only key from the timestamps for the relationship', which the distractor leaves unverified.

C:

Keeping a raw date-time relationship when the dimension contains only day-grain dates can prevent the intended match. A compatible day-grain key is needed.

D:

'Editing a transformation in the base query also updates the dependent regional view' would demonstrate 'Create a referenced query based on the cleaned base query', but not 'Every intended transaction matches the corresponding calendar date without time mismatches'. The latter requires 'Derive a normalized Date-only key from the timestamps for the relationship' in this scenario.

Question 164

A Power BI team has selected this approach: Derive a normalized Date-only key from the timestamps for the relationship. The original business context is: A semantic model's fact table uses date-time stamps but needs to relate to a day-grain Date dimension. Which observed result would best confirm the change worked as intended?

  1. Editing a transformation in the base query also updates the dependent regional view.
  2. The date field behaves as a date and chronological filters use the intended days.
  3. Every intended transaction matches the corresponding calendar date without time mismatches.
  4. Matched shipments receive segment values and unmatched shipments remain visible as intended.

Explanation

Correct Answer

C:

Every intended transaction matches the corresponding calendar date without time mismatches. It provides the required observation for the chosen implementation: Derive a normalized Date-only key from the timestamps for the relationship. This test addresses the stated business constraint directly.

Incorrect Answers

A:

Editing a transformation in the base query also updates the dependent regional view. That check would be meaningful for the separate case: An analyst copies a query for a filtered regional view but wants upstream cleanup changes to flow through automatically. It would not directly verify this scenario's selected implementation: Derive a normalized Date-only key from the timestamps for the relationship.

B:

The date field behaves as a date and chronological filters use the intended days. That check would be meaningful for the separate case: A source exports order dates as text but the model must support a date hierarchy and time intelligence. It would not directly verify this scenario's selected implementation: Derive a normalized Date-only key from the timestamps for the relationship.

D:

Matched shipments receive segment values and unmatched shipments remain visible as intended. That check would be meaningful for the separate case: A shipments query must enrich each shipment with a customer segment from a second table keyed by CustomerID. It would not directly verify this scenario's selected implementation: Derive a normalized Date-only key from the timestamps for the relationship.

Question 165

A help desk export has ticket priorities distributed as columns rather than values, preventing a single priority slicer. Which solution should the analyst prioritize for this scenario?

  1. Unpivot the month columns into Month and Value fields.
  2. Duplicate the query to create a separate transformation chain.
  3. Unpivot the priority count columns to Priority and Count rows.
  4. Append the regional queries after aligning their column names and types.

Explanation

Correct Answer

C:

Unpivot the priority count columns to Priority and Count rows. This targets the specific requirement described for this Power BI scenario. The corresponding acceptance check is: A slicer can select one priority and filter its numeric count consistently.

Incorrect Answers

A:

Using 'Unpivot the month columns into Month and Value fields' supports the result 'The result has month-value rows rather than twelve separate month measures'. It does not implement 'Unpivot the priority count columns to Priority and Count rows' or prove 'A slicer can select one priority and filter its numeric count consistently' for this question.

B:

'Duplicate the query to create a separate transformation chain' is validated when 'Changes to one query's later steps do not automatically rewrite the duplicated chain'. It does not demonstrate 'A slicer can select one priority and filter its numeric count consistently', which requires 'Unpivot the priority count columns to Priority and Count rows'.

D:

A successful 'Append the regional queries after aligning their column names and types' would show 'The resulting table has the combined regional orders without a side-by-side join'. This case needs 'Unpivot the priority count columns to Priority and Count rows' and evidence that 'A slicer can select one priority and filter its numeric count consistently', not the other outcome.

Question 166

A Power BI team has selected this approach: Unpivot the priority count columns to Priority and Count rows. The original business context is: A help desk export has ticket priorities distributed as columns rather than values, preventing a single priority slicer. Which acceptance test best demonstrates the intended outcome?

  1. The resulting table has the combined regional orders without a side-by-side join.
  2. A slicer can select one priority and filter its numeric count consistently.
  3. The result has month-value rows rather than twelve separate month measures.
  4. Changes to one query's later steps do not automatically rewrite the duplicated chain.

Explanation

Correct Answer

B:

A slicer can select one priority and filter its numeric count consistently. It provides the required observation for the chosen implementation: Unpivot the priority count columns to Priority and Count rows. This test addresses the stated business constraint directly.

Incorrect Answers

A:

The resulting table has the combined regional orders without a side-by-side join. That check would be meaningful for the separate case: Two branches export identical Orders columns for different regions and rows should stack. It would not directly verify this scenario's selected implementation: Unpivot the priority count columns to Priority and Count rows.

C:

The result has month-value rows rather than twelve separate month measures. That check would be meaningful for the separate case: A wide quarterly table has one column per month but report designers need month as a slicer category. It would not directly verify this scenario's selected implementation: Unpivot the priority count columns to Priority and Count rows.

D:

Changes to one query's later steps do not automatically rewrite the duplicated chain. That check would be meaningful for the separate case: A sandbox calculation should remain independent after the author changes the original query steps. It would not directly verify this scenario's selected implementation: Unpivot the priority count columns to Priority and Count rows.

Question 167

A product export includes region names with hierarchy levels in separate columns; the analyst needs a stable region key for joins. What should the Power BI analyst configure to meet the stated need?

  1. Build a dimension with unique region keys and keep descriptive hierarchy attributes.
  2. Expand the list and each record into a tabular Power Query structure.
  3. Disable Enable load for the staging query while keeping referenced outputs enabled.
  4. Pivot the attribute-name column using an appropriate value aggregation.

Explanation

Correct Answer

A:

Build a dimension with unique region keys and keep descriptive hierarchy attributes. This targets the specific requirement described for this Power BI scenario. The corresponding acceptance check is: The fact-to-region relationship uses one key and hierarchy labels do not cause key collisions.

Incorrect Answers

B:

The check for 'Expand the list and each record into a tabular Power Query structure' is 'The loaded table contains one row per API record and explicitly typed columns'. That does not establish 'The fact-to-region relationship uses one key and hierarchy labels do not cause key collisions'; the stated requirement calls for 'Build a dimension with unique region keys and keep descriptive hierarchy attributes'.

C:

'Disable Enable load for the staging query while keeping referenced outputs enabled' addresses the mechanism measured by 'The three output tables load and the staging query is absent from the model Fields list'. The current outcome is 'The fact-to-region relationship uses one key and hierarchy labels do not cause key collisions' after 'Build a dimension with unique region keys and keep descriptive hierarchy attributes', which the distractor leaves unverified.

D:

'The output contains the expected named attribute columns for each entity' would demonstrate 'Pivot the attribute-name column using an appropriate value aggregation', but not 'The fact-to-region relationship uses one key and hierarchy labels do not cause key collisions'. The latter requires 'Build a dimension with unique region keys and keep descriptive hierarchy attributes' in this scenario.

Question 168

A Power BI team has selected this approach: Build a dimension with unique region keys and keep descriptive hierarchy attributes. The original business context is: A product export includes region names with hierarchy levels in separate columns; the analyst needs a stable region key for joins. Which check would show that the solution meets its stated goal?

  1. The fact-to-region relationship uses one key and hierarchy labels do not cause key collisions.
  2. The output contains the expected named attribute columns for each entity.
  3. The three output tables load and the staging query is absent from the model Fields list.
  4. The loaded table contains one row per API record and explicitly typed columns.

Explanation

Correct Answer

A:

The fact-to-region relationship uses one key and hierarchy labels do not cause key collisions. It provides the required observation for the chosen implementation: Build a dimension with unique region keys and keep descriptive hierarchy attributes. This test addresses the stated business constraint directly.

Incorrect Answers

B:

The output contains the expected named attribute columns for each entity. That check would be meaningful for the separate case: A key-value table lists attributes in rows but the model needs each attribute as a field. It would not directly verify this scenario's selected implementation: Build a dimension with unique region keys and keep descriptive hierarchy attributes.

C:

The three output tables load and the staging query is absent from the model Fields list. That check would be meaningful for the separate case: An intermediate staging query feeds three downstream fact tables but should not appear as a visible model table. It would not directly verify this scenario's selected implementation: Build a dimension with unique region keys and keep descriptive hierarchy attributes.

D:

The loaded table contains one row per API record and explicitly typed columns. That check would be meaningful for the separate case: An API returns a list of records inside a top-level JSON object rather than a flat table. It would not directly verify this scenario's selected implementation: Build a dimension with unique region keys and keep descriptive hierarchy attributes.

Question 169

A sales fact has repeated ProductKey values while a product dimension contains one row per ProductKey. Which implementation choice most directly meets the business requirement?

  1. Create role-playing date dimensions for Invoice and Ship analysis.
  2. Set the column's data type and default summarization to suit an identifier.
  3. Create a date-grain matching key and relate it to the Date table's unique date column.
  4. Create a one-to-many relationship from unique Product to repeating Sales keys.

Explanation

Correct Answer

D:

Create a one-to-many relationship from unique Product to repeating Sales keys. This targets the specific requirement described for this Power BI scenario. The corresponding acceptance check is: A product slicer filters the correct matching sales rows without duplicate-dimension-key errors.

Incorrect Answers

A:

Using 'Create role-playing date dimensions for Invoice and Ship analysis' supports the result 'InvoiceDate and ShipDate slicers independently narrow the intended date roles in one visual' for Sales has InvoiceDate and ShipDate but the business requires separate simultaneous slicers for both roles. It does not implement 'Create a one-to-many relationship from unique Product to repeating Sales keys' or prove 'A product slicer filters the correct matching sales rows without duplicate-dimension-key errors' for this question.

B:

'Set the column's data type and default summarization to suit an identifier' is validated when 'Visuals display SKU values without automatic sum or average aggregation' for A dimension has a column of SKU labels for grouping but report authors should not aggregate it numerically. It does not demonstrate 'A product slicer filters the correct matching sales rows without duplicate-dimension-key errors', which requires 'Create a one-to-many relationship from unique Product to repeating Sales keys'.

C:

A successful 'Create a date-grain matching key and relate it to the Date table's unique date column' would show 'Sales transactions across different times on the same day appear under the correct calendar date' for A date dimension is accidentally related to Sales[TimeStamp] containing nonzero times. This case needs 'Create a one-to-many relationship from unique Product to repeating Sales keys' and evidence that 'A product slicer filters the correct matching sales rows without duplicate-dimension-key errors', not the other outcome.

Question 170

A Power BI team has selected this approach: Create a one-to-many relationship from unique Product to repeating Sales keys. The original business context is: A sales fact has repeated ProductKey values while a product dimension contains one row per ProductKey. Which acceptance test best demonstrates the intended outcome?

  1. Visuals display SKU values without automatic sum or average aggregation.
  2. A product slicer filters the correct matching sales rows without duplicate-dimension-key errors.
  3. Sales transactions across different times on the same day appear under the correct calendar date.
  4. InvoiceDate and ShipDate slicers independently narrow the intended date roles in one visual.

Explanation

Correct Answer

B:

A product slicer filters the correct matching sales rows without duplicate-dimension-key errors. It provides the required observation for the chosen implementation: Create a one-to-many relationship from unique Product to repeating Sales keys. This test addresses the stated business constraint directly.

Incorrect Answers

A:

Visuals display SKU values without automatic sum or average aggregation. That check would be meaningful for the separate case: A dimension has a column of SKU labels for grouping but report authors should not aggregate it numerically. It would not directly verify this scenario's selected implementation: Create a one-to-many relationship from unique Product to repeating Sales keys.

C:

Sales transactions across different times on the same day appear under the correct calendar date. That check would be meaningful for the separate case: A date dimension is accidentally related to Sales[TimeStamp] containing nonzero times. It would not directly verify this scenario's selected implementation: Create a one-to-many relationship from unique Product to repeating Sales keys.

D:

InvoiceDate and ShipDate slicers independently narrow the intended date roles in one visual. That check would be meaningful for the separate case: Sales has InvoiceDate and ShipDate but the business requires separate simultaneous slicers for both roles. It would not directly verify this scenario's selected implementation: Create a one-to-many relationship from unique Product to repeating Sales keys.