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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 14

Question 130

A SKU contains a variable-length prefix and digits, and the desired field is just the numeric suffix. What helps?

  1. Extract the portion after the defined prefix delimiter before type conversion.
  2. Extract text before the comma delimiter.
  3. Extract the rightmost four characters from the text value.
  4. Split the ID column by its hyphen delimiter into separate fields.

Explanation

Correct Answer

A:

Extract the portion after the defined prefix delimiter before type conversion. Pattern-based extraction is safer than fixed-position guessing.

Incorrect Answers

B:

Text Before Delimiter can retain just the surname segment. That is a different operating condition from “the desired field is just the numeric suffix”: “'Last, First', and the report needs LastName only”.

C:

Text extraction can select a suffix without assuming fixed prefix length. It serves “the last four characters regardless of prefix length”, not “the desired field is just the numeric suffix”.

D:

Delimiter splitting turns one structured text value into multiple columns. The present requirement is “the desired field is just the numeric suffix”; “'Region-Store-Item', and the model needs each component separately” is a distinct situation.

Question 131

A source export combines OrderID, ProductName, CustomerCity and Amount on each line, and the model needs star schema tables. What should be done?

  1. Keep descriptive Product attributes in a dimension and use a foreign key in the fact.
  2. Add a common Date dimension connected to the date-level business key.
  3. Separate transaction-grain facts from unique product and customer dimensions.
  4. Preserve OrderLineID granularity when reports need line-level analysis.

Explanation

Correct Answer

C:

Separate transaction-grain facts from unique product and customer dimensions. Facts hold measures and foreign keys, dimensions hold descriptive attributes.

Incorrect Answers

A:

A star schema avoids duplicating long descriptive attributes across facts. The present requirement is “line, and the model needs star schema tables”; “store the same descriptions on every 100-million-row fact” is a distinct situation.

B:

A date table supports consistent calendar attributes and time intelligence. That matters for “timestamps, but reports group strictly by calendar date” rather than the condition “line, and the model needs star schema tables”.

D:

Aggregating too early destroys details that measures or drilldown may require. This solves “OrderID and loses line detail needed by reports”; the stated issue is “line, and the model needs star schema tables”.

Question 132

A customer table has one row per transaction rather than one row per CustomerID. What should be done before making it a dimension?

  1. Preserve OrderLineID granularity when reports need line-level analysis.
  2. Create a distinct CustomerID lookup with the appropriate customer attributes.
  3. Add a common Date dimension connected to the date-level business key.
  4. Separate transaction-grain facts from unique product and customer dimensions.

Explanation

Correct Answer

B:

Create a distinct CustomerID lookup with the appropriate customer attributes. What should be done before making it a dimension?.

Incorrect Answers

A:

Aggregating too early destroys details that measures or drilldown may require. This solves “OrderID and loses line detail needed by reports”; the stated issue is “per transaction rather than one row per CustomerID”.

C:

A date table supports consistent calendar attributes and time intelligence. Its application is “timestamps, but reports group strictly by calendar date”, not the need “per transaction rather than one row per CustomerID”.

D:

Facts hold measures and foreign keys, dimensions hold descriptive attributes. That is a different operating condition from “per transaction rather than one row per CustomerID”: “line, and the model needs star schema tables”.

Question 133

A fact table reports one row per OrderLineID, but the analyst groups to OrderID and loses line detail needed by reports. What is the modeling error?

  1. Create a distinct CustomerID lookup with the appropriate customer attributes.
  2. Separate transaction-grain facts from unique product and customer dimensions.
  3. Add a common Date dimension connected to the date-level business key.
  4. Preserve OrderLineID granularity when reports need line-level analysis.

Explanation

Correct Answer

D:

Preserve OrderLineID granularity when reports need line-level analysis. Aggregating too early destroys details that measures or drilldown may require.

Incorrect Answers

A:

What should be done before making it a dimension?. That is a different operating condition from “OrderID and loses line detail needed by reports”: “per transaction rather than one row per CustomerID”.

B:

Facts hold measures and foreign keys, dimensions hold descriptive attributes. It serves “line, and the model needs star schema tables”, not “OrderID and loses line detail needed by reports”.

C:

A date table supports consistent calendar attributes and time intelligence. The present requirement is “OrderID and loses line detail needed by reports”; “timestamps, but reports group strictly by calendar date” is a distinct situation.

Question 134

A retailer uses a Product dimension with category and brand but plans to store the same descriptions on every 100-million-row fact. What is more efficient?

  1. Add a common Date dimension connected to the date-level business key.
  2. Separate transaction-grain facts from unique product and customer dimensions.
  3. Keep descriptive Product attributes in a dimension and use a foreign key in the fact.
  4. Create a distinct CustomerID lookup with the appropriate customer attributes.

Explanation

Correct Answer

C:

Keep descriptive Product attributes in a dimension and use a foreign key in the fact. A star schema avoids duplicating long descriptive attributes across facts.

Incorrect Answers

A:

A date table supports consistent calendar attributes and time intelligence. That matters for “timestamps, but reports group strictly by calendar date” rather than the condition “store the same descriptions on every 100-million-row fact”.

B:

Facts hold measures and foreign keys, dimensions hold descriptive attributes. The boundary it addresses is “line, and the model needs star schema tables”, while this case concerns “store the same descriptions on every 100-million-row fact”.

D:

What should be done before making it a dimension?. The deciding difference is “per transaction rather than one row per CustomerID” versus “store the same descriptions on every 100-million-row fact”.

Question 135

A date field contains valid timestamps, but reports group strictly by calendar date. What shared lookup helps?

  1. Create a distinct CustomerID lookup with the appropriate customer attributes.
  2. Preserve OrderLineID granularity when reports need line-level analysis.
  3. Keep descriptive Product attributes in a dimension and use a foreign key in the fact.
  4. Add a common Date dimension connected to the date-level business key.

Explanation

Correct Answer

D:

Add a common Date dimension connected to the date-level business key. A date table supports consistent calendar attributes and time intelligence.

Incorrect Answers

A:

What should be done before making it a dimension?. The deciding difference is “per transaction rather than one row per CustomerID” versus “timestamps, but reports group strictly by calendar date”.

B:

Aggregating too early destroys details that measures or drilldown may require. Its application is “OrderID and loses line detail needed by reports”, not the need “timestamps, but reports group strictly by calendar date”.

C:

A star schema avoids duplicating long descriptive attributes across facts. That is a different operating condition from “timestamps, but reports group strictly by calendar date”: “store the same descriptions on every 100-million-row fact”.

Question 136

A source has CustomerKey as integer in Sales but Text in Customers, and relationships fail. What should be fixed?

  1. Use a composite business key or surrogate representing CustomerID plus SnapshotMonth.
  2. Convert both relationship key columns to compatible data types.
  3. Build a stable unique business or surrogate key for the dimension.
  4. Run an anti join to identify fact foreign keys with no dimension match.

Explanation

Correct Answer

B:

Convert both relationship key columns to compatible data types. Related key columns need compatible representations to match values.

Incorrect Answers

A:

Neither field alone uniquely identifies the snapshot row. It serves “has CustomerID and SnapshotMonth as its true grain”, not “Sales but Text in Customers, and relationships fail”.

C:

Names are nonunique and may change, making weak relationship keys. That matters for “no unique ID is present, resulting in collisions” rather than the condition “Sales but Text in Customers, and relationships fail”.

D:

Run an anti join to identify fact foreign keys with no dimension match. The boundary it addresses is “contains ProductID values missing from the Product dimension”, while this case concerns “Sales but Text in Customers, and relationships fail”.

Question 137

A fact table contains ProductID values missing from the Product dimension. What diagnostic is useful?

  1. Use the correct version-specific surrogate dimension key.
  2. Build a stable unique business or surrogate key for the dimension.
  3. Run an anti join to identify fact foreign keys with no dimension match.
  4. Convert both relationship key columns to compatible data types.

Explanation

Correct Answer

C:

Run an anti join to identify fact foreign keys with no dimension match. Anti joins expose orphaned dimension references.

Incorrect Answers

A:

Type-2 dimensions require keys identifying the historical attribute version. The boundary it addresses is “Customer dimension with multiple historical rows per customer”, while this case concerns “contains ProductID values missing from the Product dimension”.

B:

Names are nonunique and may change, making weak relationship keys. This solves “no unique ID is present, resulting in collisions”; the stated issue is “contains ProductID values missing from the Product dimension”.

D:

Related key columns need compatible representations to match values. Its application is “Sales but Text in Customers, and relationships fail”, not the need “contains ProductID values missing from the Product dimension”.

Question 138

An analyst joins on customer names because no unique ID is present, resulting in collisions. What is a safer design?

  1. Build a stable unique business or surrogate key for the dimension.
  2. Convert both relationship key columns to compatible data types.
  3. Use the correct version-specific surrogate dimension key.
  4. Use a composite business key or surrogate representing CustomerID plus SnapshotMonth.

Explanation

Correct Answer

A:

Build a stable unique business or surrogate key for the dimension. Names are nonunique and may change, making weak relationship keys.

Incorrect Answers

B:

Related key columns need compatible representations to match values. That is a different operating condition from “no unique ID is present, resulting in collisions”: “Sales but Text in Customers, and relationships fail”.

C:

Type-2 dimensions require keys identifying the historical attribute version. It serves “Customer dimension with multiple historical rows per customer”, not “no unique ID is present, resulting in collisions”.

D:

Neither field alone uniquely identifies the snapshot row. The present requirement is “no unique ID is present, resulting in collisions”; “has CustomerID and SnapshotMonth as its true grain” is a distinct situation.

Question 139

A monthly snapshot has CustomerID and SnapshotMonth as its true grain. Which key design prevents collisions?

  1. Use the correct version-specific surrogate dimension key.
  2. Use a composite business key or surrogate representing CustomerID plus SnapshotMonth.
  3. Run an anti join to identify fact foreign keys with no dimension match.
  4. Convert both relationship key columns to compatible data types.

Explanation

Correct Answer

B:

Use a composite business key or surrogate representing CustomerID plus SnapshotMonth. Neither field alone uniquely identifies the snapshot row.

Incorrect Answers

A:

Type-2 dimensions require keys identifying the historical attribute version. The present requirement is “has CustomerID and SnapshotMonth as its true grain”; “Customer dimension with multiple historical rows per customer” is a distinct situation.

C:

Run an anti join to identify fact foreign keys with no dimension match. The boundary it addresses is “contains ProductID values missing from the Product dimension”, while this case concerns “has CustomerID and SnapshotMonth as its true grain”.

D:

Related key columns need compatible representations to match values. This solves “Sales but Text in Customers, and relationships fail”; the stated issue is “has CustomerID and SnapshotMonth as its true grain”.

Question 140

A sales fact joins to a slowly changing Customer dimension with multiple historical rows per customer. What is needed?

  1. Run an anti join to identify fact foreign keys with no dimension match.
  2. Build a stable unique business or surrogate key for the dimension.
  3. Use the correct version-specific surrogate dimension key.
  4. Convert both relationship key columns to compatible data types.

Explanation

Correct Answer

C:

Use the correct version-specific surrogate dimension key. Type-2 dimensions require keys identifying the historical attribute version.

Incorrect Answers

A:

Run an anti join to identify fact foreign keys with no dimension match. This solves “contains ProductID values missing from the Product dimension”; the stated issue is “Customer dimension with multiple historical rows per customer”.

B:

Names are nonunique and may change, making weak relationship keys. The deciding difference is “no unique ID is present, resulting in collisions” versus “Customer dimension with multiple historical rows per customer”.

D:

Related key columns need compatible representations to match values. That is a different operating condition from “Customer dimension with multiple historical rows per customer”: “Sales but Text in Customers, and relationships fail”.