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Microsoft DP-700 Bundle

Exam Code: DP-700

Exam Name Implementing Data Engineering Solutions Using Microsoft Fabric

Certification Provider: Microsoft

Corresponding Certification: Microsoft Certified: Fabric Data Engineer Associate

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Microsoft DP-700 Practice Test 38

Question 370

A data engineer prepares a star schema from normalized operational tables. The current environment exhibits the following problem: a many-to-many promotion tag join duplicates revenue across several promotions. Which approach best satisfies this requirement?

  1. Use a stable business key or mapped surrogate relationship
  2. Allocate fact revenue across promotion tags using documented weighting factors
  3. Preserve the order-line fact grain and connect to conformed dimensions
  4. Insert a controlled unknown/inferred dimension key rather than discard valid facts

Explanation

Correct Answer

B:

Allocate fact revenue across promotion tags using documented weighting factors. The bridge links tags but additive reporting also requires an explicit allocation rule.

Incorrect Answers

A:

Mutable customer names are used as joins between sales and customers. Names aren't reliable relational identifiers.

C:

Order lines and order headers are aggregated before joining to customer dimensions. Correct grain prevents unintended loss and double counting.

D:

An order record references an unknown product and the load silently drops it. Fact completeness requires handling late or missing dimensions.

Question 371

A Fabric Warehouse performs data cleansing with supported SQL. An acceptance test shows that text dates in the staging table cause conversion failures. Which action should the team take next?

  1. Choose an appropriate left join and unknown-member strategy when facts must survive
  2. Parse source dates with a declared locale and reject ambiguous formats before loading
  3. Normalize values to suitable DECIMAL precision and currency conventions
  4. Apply documented null-handling and unknown labels without inventing values

Explanation

Correct Answer

B:

Parse source dates with a declared locale and reject ambiguous formats before loading. Explicit locale rules protect date semantics against silent day-month swaps.

Incorrect Answers

A:

A SQL join unexpectedly drops fact records without matching dimensions. Join type determines whether unmatched facts are retained.

C:

Currency amounts include inconsistent scaling and precision. Correct numeric types preserve arithmetic and avoid implicit rounding.

D:

A reporting table has sparse null dimensions that break groupings. Null normalization must preserve the difference between missing and zero.

Question 372

A Fabric Warehouse performs data cleansing with supported SQL. The engineer has established the following constraint: a staging table contains duplicates by source transaction key. Which approach best satisfies this requirement?

  1. Deduplicate with an explicit business-order rule before MERGE
  2. Apply documented null-handling and unknown labels without inventing values
  3. Choose an appropriate left join and unknown-member strategy when facts must survive
  4. Parse source dates with a declared locale and reject ambiguous formats before loading

Explanation

Correct Answer

A:

Deduplicate with an explicit business-order rule before MERGE. Deduplication must choose a deterministic surviving record.

Incorrect Answers

B:

A reporting table has sparse null dimensions that break groupings. Null normalization must preserve the difference between missing and zero.

C:

A SQL join unexpectedly drops fact records without matching dimensions. Join type determines whether unmatched facts are retained.

D:

Text dates in the staging table cause conversion failures. Explicit locale rules protect date semantics against silent day-month swaps.

Question 373

A Fabric Warehouse performs data cleansing with supported SQL. During an operations handoff, engineers find that currency amounts include inconsistent scaling and precision. Which corrective action addresses the root cause?

  1. Parse source dates with a declared locale and reject ambiguous formats before loading
  2. Choose an appropriate left join and unknown-member strategy when facts must survive
  3. Apply documented null-handling and unknown labels without inventing values
  4. Normalize values to suitable DECIMAL precision and currency conventions

Explanation

Correct Answer

D:

Normalize values to suitable DECIMAL precision and currency conventions. Correct numeric types preserve arithmetic and avoid implicit rounding.

Incorrect Answers

A:

Text dates in the staging table cause conversion failures. Explicit locale rules protect date semantics against silent day-month swaps.

B:

A SQL join unexpectedly drops fact records without matching dimensions. Join type determines whether unmatched facts are retained.

C:

A reporting table has sparse null dimensions that break groupings. Null normalization must preserve the difference between missing and zero.

Question 374

A Fabric Warehouse performs data cleansing with supported SQL. The team observes that a SQL join unexpectedly drops fact records without matching dimensions. What should the administrator implement or verify?

  1. Apply documented null-handling and unknown labels without inventing values
  2. Choose an appropriate left join and unknown-member strategy when facts must survive
  3. Parse source dates with a declared locale and reject ambiguous formats before loading
  4. Normalize values to suitable DECIMAL precision and currency conventions

Explanation

Correct Answer

B:

Choose an appropriate left join and unknown-member strategy when facts must survive. Join type determines whether unmatched facts are retained.

Incorrect Answers

A:

A reporting table has sparse null dimensions that break groupings. Null normalization must preserve the difference between missing and zero.

C:

Text dates in the staging table cause conversion failures. Explicit locale rules protect date semantics against silent day-month swaps.

D:

Currency amounts include inconsistent scaling and precision. Correct numeric types preserve arithmetic and avoid implicit rounding.

Question 375

A Fabric Warehouse performs data cleansing with supported SQL. The implementation review confirms that a reporting table has sparse null dimensions that break groupings. Which option directly handles this observed condition?

  1. Deduplicate with an explicit business-order rule before MERGE
  2. Parse source dates with a declared locale and reject ambiguous formats before loading
  3. Choose an appropriate left join and unknown-member strategy when facts must survive
  4. Apply documented null-handling and unknown labels without inventing values

Explanation

Correct Answer

D:

Apply documented null-handling and unknown labels without inventing values. Null normalization must preserve the difference between missing and zero.

Incorrect Answers

A:

A staging table contains duplicates by source transaction key. Deduplication must choose a deterministic surviving record.

B:

Text dates in the staging table cause conversion failures. Explicit locale rules protect date semantics against silent day-month swaps.

C:

A SQL join unexpectedly drops fact records without matching dimensions. Join type determines whether unmatched facts are retained.

Question 376

A Fabric notebook refines semi-structured records with PySpark. A scheduled execution exposes this issue: a nested field name changes capitalization across source files. Which technical choice fits this evidence?

  1. Investigate skew and repartition or preaggregate appropriately
  2. Normalize schema names with controlled parsing and mapping rules
  3. Assert input/output cardinality and business-key constraints before commit
  4. Prefer built-in distributed expressions for compatible operations

Explanation

Correct Answer

B:

Normalize schema names with controlled parsing and mapping rules. Schema normalization avoids duplicate or missing fields from drift.

Incorrect Answers

A:

A groupBy result exceeds memory because one key is extremely hot. Skew concentrates work in a small number of tasks.

C:

A notebook writes a Delta table without validating input record counts. Data quality tests catch unintended row loss or multiplication.

D:

A transformation uses Python UDFs for work supported by native Spark functions. Native functions can be optimized and avoid serialization overhead.

Question 377

A Fabric notebook refines semi-structured records with PySpark. Data validation establishes that a transformation uses Python UDFs for work supported by native Spark functions. Which step preserves the intended data behavior?

  1. Normalize schema names with controlled parsing and mapping rules
  2. Prefer built-in distributed expressions for compatible operations
  3. Keep large transformations in Spark DataFrames rather than driver memory
  4. Assert input/output cardinality and business-key constraints before commit

Explanation

Correct Answer

B:

Prefer built-in distributed expressions for compatible operations. Native functions can be optimized and avoid serialization overhead.

Incorrect Answers

A:

A nested field name changes capitalization across source files. Schema normalization avoids duplicate or missing fields from drift.

C:

A DataFrame is accidentally converted to Pandas on the driver. Distributed processing prevents driver overload.

D:

A notebook writes a Delta table without validating input record counts. Data quality tests catch unintended row loss or multiplication.

Question 378

A Fabric notebook refines semi-structured records with PySpark. The production incident report states that a DataFrame is accidentally converted to Pandas on the driver. Which change most directly resolves this failure?

  1. Keep large transformations in Spark DataFrames rather than driver memory
  2. Normalize schema names with controlled parsing and mapping rules
  3. Assert input/output cardinality and business-key constraints before commit
  4. Prefer built-in distributed expressions for compatible operations

Explanation

Correct Answer

A:

Keep large transformations in Spark DataFrames rather than driver memory. Distributed processing prevents driver overload.

Incorrect Answers

B:

A nested field name changes capitalization across source files. Schema normalization avoids duplicate or missing fields from drift.

C:

A notebook writes a Delta table without validating input record counts. Data quality tests catch unintended row loss or multiplication.

D:

A transformation uses Python UDFs for work supported by native Spark functions. Native functions can be optimized and avoid serialization overhead.

Question 379

A Fabric notebook refines semi-structured records with PySpark. A postdeployment check reveals that a groupBy result exceeds memory because one key is extremely hot. What is the best engineering response?

  1. Normalize schema names with controlled parsing and mapping rules
  2. Keep large transformations in Spark DataFrames rather than driver memory
  3. Assert input/output cardinality and business-key constraints before commit
  4. Investigate skew and repartition or preaggregate appropriately

Explanation

Correct Answer

D:

Investigate skew and repartition or preaggregate appropriately. Skew concentrates work in a small number of tasks.

Incorrect Answers

A:

A nested field name changes capitalization across source files. Schema normalization avoids duplicate or missing fields from drift.

B:

A DataFrame is accidentally converted to Pandas on the driver. Distributed processing prevents driver overload.

C:

A notebook writes a Delta table without validating input record counts. Data quality tests catch unintended row loss or multiplication.

Question 380

A Fabric notebook refines semi-structured records with PySpark. The current environment exhibits the following problem: a notebook writes a Delta table without validating input record counts. Which action should the team take next?

  1. Normalize schema names with controlled parsing and mapping rules
  2. Investigate skew and repartition or preaggregate appropriately
  3. Keep large transformations in Spark DataFrames rather than driver memory
  4. Assert input/output cardinality and business-key constraints before commit

Explanation

Correct Answer

D:

Assert input/output cardinality and business-key constraints before commit. Data quality tests catch unintended row loss or multiplication.

Incorrect Answers

A:

A nested field name changes capitalization across source files. Schema normalization avoids duplicate or missing fields from drift.

B:

A groupBy result exceeds memory because one key is extremely hot. Skew concentrates work in a small number of tasks.

C:

A DataFrame is accidentally converted to Pandas on the driver. Distributed processing prevents driver overload.