Microsoft DP-700 Practice Test 45

Question 440

After repairing a zero-row destination mismatch, select TWO pieces of evidence that prove data landed correctly.

Check only the number of Files objects created, without querying the target Delta table.
Query a different, preexisting SQL endpoint table as evidence of the repaired path.
Check the activity output for successful committed rows written to the intended destination path or table.
Independently query the exact target Delta table and reconcile known source keys and row counts.
Inspect the pipeline's success icon without comparing actual source and destination row samples.

Explanation

Correct Answers

Execution details should identify the actual sink and nonzero write outcome.

A physical target read verifies the committed data exists where downstream consumers expect it.

Incorrect Answers

File object count cannot prove the intended managed table was populated.

An unrelated table can be nonempty even when this job still writes to the wrong destination.

Success shows the configured path executed, not that data appeared in the promised table.

Question 441

A Dataflow Gen2 succeeds in development but fails immediately after promotion to production. What environment differences should be compared?

Assume a successful development refresh proves production credentials.
Compare stage-specific connections, gateway identity, destination permissions and variable/parameter values, then test the production configuration.
Change the dataflow thumbnail without revising connections.
Disable production refresh logs to prevent the observed failure.

Explanation

Correct Answer

Deployment moves definitions, but stage-local identities and endpoints may differ; a successful dev refresh does not validate production connectivity.

Incorrect Answers

Different deployment stages can have different service principals, gateways and source permissions. It does not implement the corrective control requested in this incident.

Item presentation cannot fix a stage-specific credential or target binding. It does not implement the corrective control requested in this incident.

Suppressing monitoring cannot make a misconfigured connection succeed. It does not implement the corrective control requested in this incident.

Question 442

A Dataflow Gen2 fails only in production where the source endpoint variable still resolves to the development tenant and credentials are not accepted. Why?

Stage-specific connection configuration was not correctly mapped or resolved during production deployment.
Promotion automatically rewrites every source URL and tenant-specific credential at the target.
The production Dataflow must be missing a KQL query acceleration policy.
The error proves every production lakehouse destination table schema is corrupt.

Explanation

Correct Answer

A dataflow can be structurally identical yet fail when executed with wrong environment-bound connection details.

Incorrect Answers

Environment-bound endpoints and secrets may remain dev-specific unless mapped and validated for production.

KQL caching doesn't repair a Power Query connection still pointed at development credentials.

A rejected source connection arises before destination schema writes and may be independent of sink layout.

Question 443

A Dataflow Gen2 production-only failure has been corrected. What acceptance test is appropriate?

Check only that the Dataflow succeeded again in development.
Compare production and development item display names instead of configured endpoint values.
Run a preview under an administrator, ignoring the scheduled production refresh.
Run the promoted dataflow using production identities and connections, then inspect refresh diagnostics and target values unique to the production source.

Explanation

Correct Answer

A real production-context refresh verifies the corrected environment bindings and data destination.

Incorrect Answers

Development success can't validate the newly corrected production connection and runtime identity.

Names don't establish the actual resolved connection variables, tenant IDs or credentials.

Admin preview can pass with different permissions while the production scheduled refresh still fails.

Question 444

A Spark notebook caches a customer DataFrame, upstream Delta records change, but repeated notebook queries return old values. Which behavior should be addressed?

Refresh or unpersist the cached DataFrame and re-read the current Delta version, with a documented cache-invalidation policy.
Increase the notebook’s display row limit while keeping the same cached DataFrame.
Rename the lakehouse table without refreshing the cached query.
Set a Fabric domain label to Current to indicate cache freshness.

Explanation

Correct Answer

Spark cache can retain a prior materialized result until explicitly refreshed or evicted; new source commits do not guarantee immediate cache re-evaluation.

Incorrect Answers

Showing more rows does not invalidate stale cached data or select a new Delta snapshot. It does not implement the corrective control requested in this incident.

A display-name change does not reliably refresh a DataFrame already materialized in memory. It does not implement the corrective control requested in this incident.

Catalog governance metadata cannot force Spark cached transformations to recompute. It does not implement the corrective control requested in this incident.

Question 445

A notebook continues showing the old customer status after the upstream Delta table changed. A DataFrame was cached earlier in the session. Why?

Spark cached DataFrames automatically re-read upstream Delta data before every action.
Subsequent actions reuse cached pre-update data rather than reading the updated Delta snapshot.
The old status proves the upstream Delta transaction log failed to commit.
Notebook caching is controlled entirely by Fabric domain ownership labels.

Explanation

Correct Answer

The cache stores an earlier computation result, so the notebook needs invalidation or refresh before asserting data freshness.

Incorrect Answers

A cached result may be reused from memory until explicitly invalidated/recomputed; the upstream write alone doesn't force refresh.

If the source query shows new data, local cached Spark state is a separate and more direct explanation.

Domain settings do not automatically invalidate a Spark DataFrame cached in an active session.

Question 446

After an engineering team changes Spark caching controls for a notebook that reads customer Delta data, which validation demonstrates that the fix works?

Check the cached query returns the same rows twice after the change.
Compare table display names but not the actual customer status values before/after refresh.
Change a controlled source record, inspect results before and after cache invalidation and confirm the refreshed query matches the latest Delta data.
Test only in a new notebook with no prior cache and infer the old active session is fixed.

Explanation

Correct Answer

An explicit source update and re-read shows cache lifecycle behavior rather than only proving the table can be queried once.

Incorrect Answers

Repeated identical cached results do not prove that the cache reflects the latest Delta commit.

Names can't establish cache invalidation or source-to-notebook value freshness.

A fresh session can read new data while the original notebook continues serving stale cached rows.

Question 447

A Fabric Lakehouse Delta table receives hundreds of small hourly ingestion files, and scanning it now spends much of its time enumerating individual Parquet files. Which maintenance approach most directly targets this bottleneck?

Run VACUUM with zero retention as the first response to small-file scan overhead.
Make every analyst a workspace Admin so file enumeration completes faster.
Add a new OneLake shortcut to the same table and assume its file layout improves.
Run a supported Delta OPTIMIZE/compaction operation or adjust optimized writes so eligible small files are consolidated, and benchmark the resulting read behavior.

Explanation

Correct Answer

Small-file compaction reduces metadata and scan overhead while preserving the logical Delta table; it is distinct from indiscriminate data deletion.

Incorrect Answers

VACUUM removes obsolete files rather than consolidating active Parquet files; overly short retention can break readers or rollback. It does not implement the corrective control requested in this incident.

User privileges do not merge fragmented data files or address the physical I/O overhead. It does not implement the corrective control requested in this incident.

A shortcut references the original data; it does not rewrite or consolidate the underlying Parquet files. It does not implement the corrective control requested in this incident.

Question 448

A Lakehouse query slows as identical total data volume is spread across thousands of tiny Parquet files. The SQL analytics endpoint is functioning normally. What physical layout issue explains the slowdown?

The Delta table has excessive file fragmentation that increases scan/open and metadata work per query.
The SQL analytics endpoint is slow because it does not support Delta tables.
The table must be copied into an Eventhouse to make any file compacting possible.
The source columns need a Fabric sensitivity label to enable partition pruning.

Explanation

Correct Answer

For Lakehouse, automatic Warehouse compaction is not a substitute for maintaining the Delta table file layout; too many files create disproportionate per-file overhead.

Incorrect Answers

The endpoint supports Delta; scanning thousands of tiny files introduces metadata and I/O overhead.

Fabric lakehouse Delta maintenance can coalesce files without moving the data to native KQL storage.

Labels govern protection, not the tiny-file scan overhead in the described physical layout.

Question 449

After compacting a heavily fragmented Fabric Lakehouse table, which comparison best establishes that the maintenance was useful without altering business data?

Compare query time alone while ignoring whether OPTIMIZE changed file counts and bytes.
Compare pre- and post-maintenance live file counts, representative query scan time, and logical row/key reconciliations on the Delta table.
Check only that an OPTIMIZE command returned success and never reconcile the rows.
Test using unrelated cached query results instead of the affected table's scan.

Explanation

Correct Answer

A reduced number of larger files and stable business results, with faster scan time under equivalent conditions, supports the compaction benefit.

Incorrect Answers

A timing fluctuation may be unrelated to compaction; verify the intended physical layout changed.

A successful maintenance job can still be accompanied by unexpected data changes or negligible scan gains.

Unrelated cached reads don't establish improved small-file handling on the target Delta table.

Question 450

A large Lakehouse Delta table is read by analytical queries much more often than it is updated. The team wants to assess Fabric V-Order to improve scan efficiency. What is the appropriate decision?

Assume V-Order is a role-level security filter that removes rows from unprivileged queries.
Assume V-Order eliminates the need for predicate filtering and query plans.
Evaluate V-Order in the supported write/maintenance path and benchmark representative read improvements against the extra write/optimization cost.
Delete Delta transaction logs to guarantee the speedup.

Explanation

Correct Answer

V-Order can improve Parquet-read patterns, but its CPU and storage-write tradeoffs should be measured for the read-heavy workload before enabling broadly.

Incorrect Answers

V-Order changes file representation, not identity-based authorization or row-level access. It does not implement the corrective control requested in this incident.

Layout optimization can improve scans, but efficient predicates and engine plans are still necessary. It does not implement the corrective control requested in this incident.

Removing the transaction log damages Delta consistency and cannot be a supported V-Order optimization step. It does not implement the corrective control requested in this incident.

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Question 440

Explanation

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Question 441

Explanation

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Question 442

Explanation

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Question 443

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Question 444

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Question 445

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Question 446

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Question 447

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Question 448

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Question 449

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Question 450

Explanation

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