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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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Implementing Data Engineering Solutions Using Microsoft Fabric (DP-700) Practice Tests

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Microsoft DP-700 Practice Test 8

Question 70

A staging table contains several order versions and only the newest per order_id should survive. Which action best addresses the requirement?

  1. Use ROW_NUMBER partitioned by order_id ordered newest first.
  2. Use PySpark unionByName with controlled missing-field handling.
  3. Create a pipeline Copy activity into the destination.
  4. Use Delta MERGE keyed by order ID.

Explanation

Correct Answer

A:

Ranking selects the latest version per business key.

Incorrect Answers

B:

Name-based union aligns columns without relying on position.

C:

Copy handles scalable source-to-target data movement.

D:

MERGE handles matched updates and unmatched inserts.

Question 71

The same pipeline fails in one workspace but works in another and engineers need a meaningful comparison. What should the engineer implement first to satisfy the stated need?

  1. Compare run parameters, linked connection contexts, and activity outputs across environments.
  2. Correlate stream diagnostics with source connection errors at the credential rotation time.
  3. Set the schedule's failure-notification recipients.
  4. Inspect Copy activity input/output metrics and reconcile source and sink counts.

Explanation

Correct Answer

A:

Contextual run evidence isolates configuration drift.

Incorrect Answers

B:

Connection failures may stop ingestion while item metadata remains present.

C:

Scheduled-run notifications report failures for that configured schedule.

D:

Copy statistics reveal transfer counts and possible skipped or rejected rows.

Question 72

A refresh alert fires even though the semantic model's latest attempt returned an error. Which engineering decision is most suitable for this specific situation?

  1. Check Eventhouse ingestion freshness and the dashboard query time range separately.
  2. Read refresh error details rather than treating attempted refresh time as success.
  3. Define the alert against the appropriate windowed aggregate, not individual raw events.
  4. Inspect the dataflow refresh history and detailed step errors.

Explanation

Correct Answer

B:

Refresh attempts and successful completion are separate events.

Incorrect Answers

A:

A stale visualization may result from query filtering rather than ingestion failure.

C:

Window-level conditions reduce noise from single transient messages.

D:

Refresh history identifies dataflow execution status and transformation failures.

Question 73

A warehouse query globally sorts a huge intermediate dataset and runs slowly. Which action best addresses the requirement?

  1. Reduce acceleration cache period to the needed window.
  2. Push selective filters before the expensive join.
  3. Reduce the filtered set before a necessary global sort.
  4. Push WHERE and aggregate logic into the Warehouse SQL query.

Explanation

Correct Answer

C:

Global sorting can trigger non-scalable processing.

Incorrect Answers

A:

Cache policy limits how much external history is accelerated.

B:

Earlier predicates reduce processed rows.

D:

Server-side filtering reduces network and client load.

Question 74

A new production notebook requires a runtime with features not available in the runtime currently attached to its environment. Which engineering decision is most suitable for this specific situation?

  1. Publish a Fabric environment with the common library and attach it to the notebooks.
  2. Set spark.highConcurrency.max to 20 in the attached environment's Spark properties.
  3. Select the intended pool configuration in the notebook's environment Compute settings.
  4. Choose the required supported Fabric Spark runtime in the environment and publish it.

Explanation

Correct Answer

D:

The runtime version is configured on a Fabric environment and applied when a new session starts.

Incorrect Answers

A:

Environment items centrally define reusable libraries and Spark runtime settings.

B:

That property can raise the high-concurrency session's supported workload limit within the documented bounds.

C:

Environment compute configuration can specify pool resources when workspace overrides are allowed.

Question 75

A Warehouse conversion fails when a varchar column contains values outside the expected numeric range. What should the engineer implement first to satisfy the stated need?

  1. Correct the event subject filter to match the actual file path and event type.
  2. Correct the Copy mapping and type conversions for the affected columns.
  3. Handle conversion errors explicitly and route malformed values for review.
  4. Use an appropriate target type and safe conversion with invalid-record handling.

Explanation

Correct Answer

D:

Unsafe casts fail when values cannot be represented in the target type.

Incorrect Answers

A:

Event-based triggers fire only for events satisfying configured matching conditions.

B:

Data type mismatch occurs at mapping or insert even when connectivity succeeds.

C:

Power Query type conversion can fail when source values violate the expected type.

Question 76

An analytics model needs a one-row-per-customer table for current lookup and another table to track historical status changes. Which engineering decision is most suitable for this specific situation?

  1. Apply delete change events to the target using the source's business keys.
  2. Separate the current dimension representation from the historized change representation.
  3. Implement slowly changing dimension Type 2 versioned records with effective dates.
  4. Rebuild the current-state target from each complete snapshot with controlled overwrite.

Explanation

Correct Answer

B:

Current-state and time-versioned structures serve different analytical queries.

Incorrect Answers

A:

Delete propagation is needed in addition to insert/update loading when the target mirrors current state.

C:

Type 2 preserves previous attribute values by storing dated versions.

D:

A replacement snapshot represents the new complete state rather than append-only changes.

Question 77

A batch contains both valid and malformed JSON and good rows must not be lost. What should the engineer implement first to satisfy the stated need?

  1. Choose a Fabric lakehouse for file-oriented analytical storage.
  2. Use a supported pipeline or Dataflow connector with explicit XML mapping.
  3. Route good rows to curated storage and bad records to a quarantine sink.
  4. Filter categories in Power Query before writing to the curated destination.

Explanation

Correct Answer

C:

Separate validation branches retain evidence of invalid input.

Incorrect Answers

A:

Lakehouses combine OneLake files, Spark Delta tables, and a SQL endpoint.

B:

Structured-file ingestion requires appropriate parsing rules.

D:

A source-side logical filter prevents irrelevant rows reaching the output.

Question 78

A source has never been ingested into Fabric and the team needs a complete historical baseline before any change-only updates. Which engineering decision is most suitable for this specific situation?

  1. Perform an initial full load of the source records.
  2. Use a timestamp watermark and MERGE changed records by stable customer key.
  3. Separate the current dimension representation from the historized change representation.
  4. Use a controlled schema-evolution load with explicit null defaults for older records.

Explanation

Correct Answer

A:

A full load establishes all existing rows before incremental processing can track changes.

Incorrect Answers

B:

Timestamp-based detection with key-based merge handles updates as well as inserts.

C:

Current-state and time-versioned structures serve different analytical queries.

D:

Schema evolution allows new fields without misaligning established rows.

Question 79

A compliance administrator needs OneLake API and file access diagnostic events to land in a governed diagnostics lakehouse, but no OneLake diagnostics destination is configured in the Fabric workspace. Which single implementation most directly meets the stated requirement?

  1. Select the intended pool configuration in the notebook's environment Compute settings.
  2. Enable diagnostic events in workspace OneLake settings and select the approved diagnostic lakehouse.
  3. Assign the appropriate domain administrator role to the designated domain owner.
  4. Enable the For notebooks high-concurrency option under the workspace Spark settings.

Explanation

Correct Answer

B:

The OneLake settings tab allows workspace administrators to route supported OneLake diagnostic events to a designated lakehouse. The direct validation would be: New supported OneLake operations create diagnostic records in the selected lakehouse with the expected operation metadata.

Incorrect Answers

A:

Environment compute configuration can specify pool resources when workspace overrides are allowed.

C:

Domain-level administration delegates governance within the domain's scope.

D:

High-concurrency mode allows compatible interactive notebooks to attach to a shared running Spark session instead of creating an independent Spark application for each notebook.

Question 80

A warehouse receives new credit-card columns, and policy requires visible results to hide all but a few identifying digits for support staff. Which engineering decision is most suitable for this specific situation?

  1. Apply column-level constraints in a read-only OneLake security role.
  2. Configure a partial mask tailored to the supported string column.
  3. Define an RLS predicate that evaluates the current user context.
  4. Use column-level access controls to deny the Salary column to analysts.

Explanation

Correct Answer

B:

Partial masking preserves selected characters while concealing the remainder of a string.

Incorrect Answers

A:

OneLake Read roles can constrain selected columns for the specified members.

C:

Identity-aware filtering maps each caller to authorized rows at query time.

D:

Column-level security restricts permission to access specific columns rather than merely displaying masked values.