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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 22

Question 210

In a Microsoft Fabric solution, a fraud detector wants ten-minute windows that refresh every two minutes. Which configuration or implementation should the engineer choose?

  1. Use summarize count() by application and bin(timestamp, 1m).
  2. Use a hopping window with ten-minute size and two-minute hop.
  3. Use a tumbling window aggregation of five minutes.
  4. Apply an event-time watermark that covers the accepted lateness policy.

Explanation

Correct Answer

B:

Hopping windows overlap to give rolling aggregates at regular steps.

Incorrect Answers

A:

KQL bin defines discrete time buckets for grouping.

C:

Tumbling windows partition time into consecutive nonoverlapping intervals.

D:

Watermarks bound the amount of late data retained in streaming state.

Question 211

Concurrent notebooks may write Delta updates to the same table. Which action best addresses the requirement?

  1. Parse files and write them to registered Delta tables.
  2. Create a Tables shortcut to the source Delta table.
  3. Use Delta transactional writes and retry genuine write conflicts appropriately.
  4. Consume the existing mirrored tables instead of building a redundant daily Copy pipeline.

Explanation

Correct Answer

C:

Delta transaction logs protect supported concurrent operations.

Incorrect Answers

A:

Files storage is separate from registered lakehouse tables.

B:

A table shortcut exposes the referenced Delta data for queries.

D:

Mirroring already handles source change propagation.

Question 212

A Delta table has liquid clustering enabled and its clustering columns have just changed, but existing files have not been reorganized to the new clustering keys. Which single implementation most directly meets the stated requirement?

  1. Run Delta OPTIMIZE compaction.
  2. Run OPTIMIZE FULL for the liquid-clustered table after the clustering-column change.
  3. Reduce acceleration cache period to the needed window.
  4. Lower source concurrency to a sustainable rate.

Explanation

Correct Answer

B:

Microsoft documents OPTIMIZE FULL as a one-time full reclustering option after changing liquid clustering columns; changing metadata alone does not rewrite the old layout. The direct validation would be: The table retains the new clustering-column metadata and a full reclustering operation has rewritten the affected files, improving selective file skipping.

Incorrect Answers

A:

Compaction combines small files into more efficient files.

C:

Cache policy limits how much external history is accelerated.

D:

Excess parallelism causes throttling and retries.

Question 213

A KQL series uses arrival time for anomaly detection and must distinguish event-time delay from actual signal changes. Which action best addresses the requirement?

  1. Use deduplication keyed on event ID within a defined streaming state policy.
  2. Use a stateful streaming join with a valid event-time bound and watermark policy.
  3. Record both event timestamp and ingestion timestamp and compare their lag.
  4. Add an Eventstream filter on event type before the destination.

Explanation

Correct Answer

C:

Tracking both times reveals transport delay independently of source behavior.

Incorrect Answers

A:

Keyed streaming deduplication limits duplicate business contributions.

B:

Bounded joins limit held state and match related events by key and time.

D:

Eventstream transformations can route selected records according to event attributes.

Question 214

In a Microsoft Fabric solution, a notebook runs successfully with one Spark runtime but fails on a package API after its attached runtime changes. Which configuration or implementation should the engineer choose?

  1. Update the KQL column reference or project a compatible alias.
  2. Rebind the notebook to the intended target-stage default lakehouse and resolve the path.
  3. Pin a compatible package/runtime combination in the published environment.
  4. Inspect evaluated dynamic expressions and correct the runtime source path.

Explanation

Correct Answer

C:

Runtime upgrades can change dependent library compatibility.

Incorrect Answers

A:

KQL queries must reference fields that exist in the current table schema.

B:

Notebook references depend on an effective lakehouse binding in each environment.

D:

A wrong path can fail even when the connection itself is valid.

Question 215

An engineer needs to know whether a long pipeline is still running or stuck at an upstream dependency. What should the engineer implement first to satisfy the stated need?

  1. Inspect Eventhouse ingestion and database monitoring metrics.
  2. Inspect the current run's activity state and dependency graph.
  3. Inspect KQL query diagnostics or query statistics for representative runs.
  4. Inspect supported SQL dynamic management views for requests and sessions.

Explanation

Correct Answer

B:

Activity states reveal which step has begun, is pending, or is blocked.

Incorrect Answers

A:

Ingestion monitoring distinguishes source outages from slow arrival or indexing.

C:

Query metrics show performance beyond final row counts.

D:

SQL DMVs expose active workload state needed for investigation.

Question 216

A date-filtered Spark job still opens every partition on a partitioned Delta table. Which action best addresses the requirement?

  1. Use a skew-aware join plan or carefully designed salting.
  2. Use a predicate on the physical partition column and preserve pruning.
  3. Choose these frequent predicate keys for liquid clustering.
  4. Tune shuffle partition count to match volume and resources.

Explanation

Correct Answer

B:

An eligible filter allows partition pruning. The result addresses the requirement stated in this case.

Incorrect Answers

A:

Redistribution prevents one worker from becoming a straggler.

C:

Cluster-column selection should reflect query workload.

D:

Excess tiny tasks add scheduling overhead.

Question 217

A platform team needs two independent notebooks to run concurrently after staging rather than sequentially. Which action best addresses the requirement?

  1. Build a Fabric data pipeline with Copy and Notebook activities and failure handling.
  2. Use an Until loop with a bounded timeout and explicit success predicate.
  3. Place both notebook activities on parallel branches after the staging success dependency.
  4. Place the dataflow activity upstream of the notebook in the pipeline dependency graph.

Explanation

Correct Answer

C:

Parallel branches enable independent work once common prerequisites are met.

Incorrect Answers

A:

Pipelines orchestrate multi-activity ingestion and dependent jobs.

B:

Until can repeat an activity until the condition is true or its configured limit is reached.

D:

Explicit dependencies prevent processing before upstream dataflow completion.

Question 218

A streaming alert should fire only when a rolling error rate exceeds a threshold for several minutes. What should the engineer implement first to satisfy the stated need?

  1. Define the alert against the appropriate windowed aggregate, not individual raw events.
  2. Open the pipeline run in the Fabric monitoring experience and inspect its activity runs.
  3. Aggregate pipeline run outcomes by date and failure type.
  4. Read refresh error details rather than treating attempted refresh time as success.

Explanation

Correct Answer

A:

Window-level conditions reduce noise from single transient messages.

Incorrect Answers

B:

Activity-level monitoring identifies failing steps and their diagnostic output.

C:

Trend monitoring reveals recurrent errors not visible from a single run.

D:

Refresh attempts and successful completion are separate events.

Question 219

A customer dimension must retain a history of a region assignment that changed last month. What should the engineer implement first to satisfy the stated need?

  1. Use a timestamp watermark and MERGE changed records by stable customer key.
  2. Implement slowly changing dimension Type 2 versioned records with effective dates.
  3. Assign a controlled unknown or inferred dimension member until the dimension becomes available.
  4. Implement a Type 1 overwrite of the matching dimension attribute.

Explanation

Correct Answer

B:

Type 2 preserves previous attribute values by storing dated versions.

Incorrect Answers

A:

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

C:

An unknown or inferred member preserves fact referential integrity for late dimensions.

D:

Type 1 changes the current value rather than preserving superseded versions.

Question 220

A scheduled ingestion should run on two distinct times each day and needs different runtime parameter values for each window. Which action best addresses the requirement?

  1. Expose the business date as a pipeline parameter and supply it at invocation.
  2. Create two schedules on the pipeline with their respective parameter values.
  3. Configure failure notifications on the scheduled pipeline run.
  4. Inspect the pipeline's individual activity runs and their error outputs.

Explanation

Correct Answer

B:

Multiple schedules can invoke the same pipeline with different times and supplied parameters.

Incorrect Answers

A:

A runtime parameter separates job logic from each run's effective processing date.

C:

Scheduled pipelines support configured failure notifications distinct from manual invocation.

D:

Activity-level results expose the step and message causing an orchestration failure.