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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 10

Question 90

A multilingual source changes column headers based on localization. What pipeline design is prudent?

  1. Identify schema drift and update the dependent transformation step.
  2. Enforce a documented type contract with validation before model loading.
  3. Select and type a stable required field set rather than ingest every changing column.
  4. Normalize source header names to stable canonical names before later transforms.

Explanation

Correct Answer

D:

Normalize source header names to stable canonical names before later transforms. Canonical names reduce failures from localized source schemas.

Incorrect Answers

A:

Identify schema drift and update the dependent transformation step. Its application is “source removes a field referenced by Power Query”, not the need “multilingual source changes column headers based on localization”.

B:

Schema contracts catch incompatible types before they silently alter results. That is a different operating condition from “multilingual source changes column headers based on localization”: “a decimal one month and string the next”.

C:

Explicit schemas insulate models from irrelevant source columns. It serves “source field should not change report measure definitions”, not “multilingual source changes column headers based on localization”.

Question 91

Power Query's Column Profile shows only the first 1,000 rows, but rare invalid country codes appear later. What is appropriate?

  1. Use Power Query's column profiling views as diagnostics.
  2. Group by source system and count rows with null CustomerID.
  3. Enable full-dataset profiling where feasible and run explicit validation checks.
  4. Apply Trim to join-key text columns before comparison.

Explanation

Correct Answer

C:

Enable full-dataset profiling where feasible and run explicit validation checks. Sampling can miss data-quality issues outside the sampled subset.

Incorrect Answers

A:

Profiling helps assess data patterns before authoring destructive transformations. The present requirement is “rows, but rare invalid country codes appear later”; “per-column value distribution without modifying the underlying dataset” is a distinct situation.

B:

Grouped counts locate missing-key quality problems by origin. That matters for “compare frequency of null CustomerIDs by source system” rather than the condition “rows, but rare invalid country codes appear later”.

D:

Trimming removes leading or trailing whitespace that changes equality. This solves “making a join fail despite visually equal IDs”; the stated issue is “rows, but rare invalid country codes appear later”.

Question 92

A Power BI analyst must compare frequency of null CustomerIDs by source system. Which operation helps?

  1. Apply Trim to join-key text columns before comparison.
  2. Group by source system and count rows with null CustomerID.
  3. Enable full-dataset profiling where feasible and run explicit validation checks.
  4. Recognize that many product values repeat while ten occur exactly once.

Explanation

Correct Answer

B:

Group by source system and count rows with null CustomerID. Grouped counts locate missing-key quality problems by origin.

Incorrect Answers

A:

Trimming removes leading or trailing whitespace that changes equality. This solves “making a join fail despite visually equal IDs”; the stated issue is “compare frequency of null CustomerIDs by source system”.

C:

Sampling can miss data-quality issues outside the sampled subset. Its application is “rows, but rare invalid country codes appear later”, not the need “compare frequency of null CustomerIDs by source system”.

D:

Distinct counts categories; unique counts values whose frequency is one. That is a different operating condition from “compare frequency of null CustomerIDs by source system”: “distinct products but only 10 unique product values”.

Question 93

A column distribution displays 500 distinct products but only 10 unique product values. What does that imply?

  1. Apply Trim to join-key text columns before comparison.
  2. Enable full-dataset profiling where feasible and run explicit validation checks.
  3. Recognize that many product values repeat while ten occur exactly once.
  4. Use Power Query's column profiling views as diagnostics.

Explanation

Correct Answer

C:

Recognize that many product values repeat while ten occur exactly once. Distinct counts categories; unique counts values whose frequency is one.

Incorrect Answers

A:

Trimming removes leading or trailing whitespace that changes equality. That is a different operating condition from “distinct products but only 10 unique product values”: “making a join fail despite visually equal IDs”.

B:

Sampling can miss data-quality issues outside the sampled subset. It serves “rows, but rare invalid country codes appear later”, not “distinct products but only 10 unique product values”.

D:

Profiling helps assess data patterns before authoring destructive transformations. That matters for “per-column value distribution without modifying the underlying dataset” rather than the condition “distinct products but only 10 unique product values”.

Question 94

A text field has trailing spaces making a join fail despite visually equal IDs. What targeted fix fits?

  1. Recognize that many product values repeat while ten occur exactly once.
  2. Apply Trim to join-key text columns before comparison.
  3. Group by source system and count rows with null CustomerID.
  4. Enable full-dataset profiling where feasible and run explicit validation checks.

Explanation

Correct Answer

B:

Apply Trim to join-key text columns before comparison. Trimming removes leading or trailing whitespace that changes equality.

Incorrect Answers

A:

Distinct counts categories; unique counts values whose frequency is one. That matters for “distinct products but only 10 unique product values” rather than the condition “making a join fail despite visually equal IDs”.

C:

Grouped counts locate missing-key quality problems by origin. This solves “compare frequency of null CustomerIDs by source system”; the stated issue is “making a join fail despite visually equal IDs”.

D:

Sampling can miss data-quality issues outside the sampled subset. The deciding difference is “rows, but rare invalid country codes appear later” versus “making a join fail despite visually equal IDs”.

Question 95

A manager wants to inspect per-column value distribution without modifying the underlying dataset. What tool fits?

  1. Enable full-dataset profiling where feasible and run explicit validation checks.
  2. Use Power Query's column profiling views as diagnostics.
  3. Apply Trim to join-key text columns before comparison.
  4. Recognize that many product values repeat while ten occur exactly once.

Explanation

Correct Answer

B:

Use Power Query's column profiling views as diagnostics. Profiling helps assess data patterns before authoring destructive transformations.

Incorrect Answers

A:

Sampling can miss data-quality issues outside the sampled subset. The deciding difference is “rows, but rare invalid country codes appear later” versus “per-column value distribution without modifying the underlying dataset”.

C:

Trimming removes leading or trailing whitespace that changes equality. That is a different operating condition from “per-column value distribution without modifying the underlying dataset”: “making a join fail despite visually equal IDs”.

D:

Distinct counts categories; unique counts values whose frequency is one. It serves “distinct products but only 10 unique product values”, not “per-column value distribution without modifying the underlying dataset”.

Question 96

A finance extract stores tax inclusive amounts in one region and tax exclusive amounts in another. What is needed before aggregation?

  1. Preserve valid rows when only optional descriptive fields are absent.
  2. Standardize accounting definitions and currency treatment by documented rules.
  3. Parse durations into a consistent numeric unit or duration type.
  4. Avoid treating all same-date and amount rows as erroneous duplicates.

Explanation

Correct Answer

B:

Standardize accounting definitions and currency treatment by documented rules. Combining incompatible financial measures without normalization distorts totals.

Incorrect Answers

A:

Optional null text does not necessarily invalidate a transaction record. It serves “optional comments even though numeric fields are valid”, not “one region and tax exclusive amounts in another”.

C:

Parse durations into a consistent numeric unit or duration type. That matters for “as text like '3 hours' and '45 minutes'” rather than the condition “one region and tax exclusive amounts in another”.

D:

Distinct transaction identifiers and posting grain matter more than equal values. The boundary it addresses is “share amount and date”, while this case concerns “one region and tax exclusive amounts in another”.

Question 97

A source has legitimate double-entry bookkeeping records that share amount and date. What should the modeler avoid?

  1. Preserve valid rows when only optional descriptive fields are absent.
  2. Convert monetary amounts into a consistent reporting currency before summing.
  3. Avoid treating all same-date and amount rows as erroneous duplicates.
  4. Parse durations into a consistent numeric unit or duration type.

Explanation

Correct Answer

C:

Avoid treating all same-date and amount rows as erroneous duplicates. Distinct transaction identifiers and posting grain matter more than equal values.

Incorrect Answers

A:

Optional null text does not necessarily invalidate a transaction record. The boundary it addresses is “optional comments even though numeric fields are valid”, while this case concerns “share amount and date”.

B:

Convert monetary amounts into a consistent reporting currency before summing. This solves “denominated in EUR and USD without exchange conversion”; the stated issue is “share amount and date”.

D:

Parse durations into a consistent numeric unit or duration type. Its application is “as text like '3 hours' and '45 minutes'”, not the need “share amount and date”.

Question 98

A BI pipeline drops all rows with missing optional comments even though numeric fields are valid. What should change?

  1. Preserve valid rows when only optional descriptive fields are absent.
  2. Avoid treating all same-date and amount rows as erroneous duplicates.
  3. Standardize accounting definitions and currency treatment by documented rules.
  4. Convert monetary amounts into a consistent reporting currency before summing.

Explanation

Correct Answer

A:

Preserve valid rows when only optional descriptive fields are absent. Optional null text does not necessarily invalidate a transaction record.

Incorrect Answers

B:

Distinct transaction identifiers and posting grain matter more than equal values. That is a different operating condition from “optional comments even though numeric fields are valid”: “share amount and date”.

C:

Combining incompatible financial measures without normalization distorts totals. It serves “one region and tax exclusive amounts in another”, not “optional comments even though numeric fields are valid”.

D:

Convert monetary amounts into a consistent reporting currency before summing. The present requirement is “optional comments even though numeric fields are valid”; “denominated in EUR and USD without exchange conversion” is a distinct situation.

Question 99

A report combines customer revenue denominated in EUR and USD without exchange conversion. What is the correction?

  1. Standardize accounting definitions and currency treatment by documented rules.
  2. Avoid treating all same-date and amount rows as erroneous duplicates.
  3. Convert monetary amounts into a consistent reporting currency before summing.
  4. Preserve valid rows when only optional descriptive fields are absent.

Explanation

Correct Answer

C:

Convert monetary amounts into a consistent reporting currency before summing. Adding different currency units yields misleading totals.

Incorrect Answers

A:

Combining incompatible financial measures without normalization distorts totals. The present requirement is “denominated in EUR and USD without exchange conversion”; “one region and tax exclusive amounts in another” is a distinct situation.

B:

Distinct transaction identifiers and posting grain matter more than equal values. That matters for “share amount and date” rather than the condition “denominated in EUR and USD without exchange conversion”.

D:

Optional null text does not necessarily invalidate a transaction record. This solves “optional comments even though numeric fields are valid”; the stated issue is “denominated in EUR and USD without exchange conversion”.

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”.