Microsoft DP-700 Practice Test 27

Question 260

A daily incremental job filters for `OrderDate = today` and ignores yesterday’s orders delivered after midnight. Why?

Its filter assumes event date and ingestion date coincide and therefore excludes late-arriving business events.
The incremental job should always use OrderDate equal to the current date without a lookback.
The output needs more Fabric Spark workers to make yesterday's rows eligible.
The warehouse SQL query optimizer isn't preserving historical date partitions.

Explanation

Correct Answer

Event-time rows can arrive after their original date; the loader needs a change or lateness strategy beyond a strict current-date predicate.

Incorrect Answers

This duplicates the defect; delayed source arrival may carry an older business date than processing date.

Additional workers won't change a predicate that intentionally excludes those rows.

The explicit today-only filter, not optimizer partition pruning, removes legitimate delayed arrivals.

Question 261

After adding a late-arrival reprocessing window, what confirms its effectiveness?

Replay a delayed order dated yesterday and treat it as a new current-day business-date bucket regardless of its original OrderDate.
Deliver a previous-day order after today’s load, rerun the applicable window and verify the correct historical bucket updates exactly once.
Inspect only records with business dates equal to processing date.
Verify only that the processing job has green status without source/target order reconciliation.

Explanation

Correct Answer

A deliberate late delivery validates both historical correction and idempotence of the reprocessed period.

Incorrect Answers

An ingestion occurring today does not change the event’s historical business date. Assigning yesterday’s order to today’s reporting bucket would misstate the reconciled daily measures.

This never tests the late-arriving orders that motivated the recovery window.

A successful job can still silently exclude yesterday's newly delivered orders.

Question 262

A OneLake shortcut points to a Delta table that has been relocated to a new source path. Consumers now see a broken target. What should the owner update?

Rename only the consumer lakehouse item and keep the shortcut's outdated target path.
Copy the old shortcut icon to a different folder without updating source URI or identifiers.
Repair or recreate the shortcut with the correct source item/path and verify access using the affected consumer identity.
Increase shortcut cache timeout and assume a moved source Delta path becomes valid again.

Explanation

Correct Answer

Shortcuts are logical references; they do not retain an independent physical copy when their source target disappears or moves.

Incorrect Answers

The shortcut stores a source reference that must resolve to the relocated Delta folder; consumer names are irrelevant.

Moving the visible shortcut item doesn't correct a stale underlying target location.

Caching cannot repair a source item identifier or path that no longer resolves.

Question 263

A shortcut stops returning rows immediately after the source lakehouse table folder is renamed, though no target data was copied. What caused it?

The shortcut fails because its original dataset was not physically copied into the destination first.
The destination workspace was never added to the same business domain as its source.
The shortcut's SQL analytics endpoint has automatically converted the source table to a new region.
The shortcut’s stored target reference no longer resolves to the moved or renamed source path.

Explanation

Correct Answer

A shortcut resolves the current source location at read time and can fail after uncoordinated source path changes.

Incorrect Answers

A shortcut is a reference to the source; a moved target path can break even though copying isn't required.

Domain grouping doesn't update stale OneLake path identifiers after a source rename.

The observed change was a source path move, not a documented endpoint-region conversion.

Question 264

A cross-workspace shortcut is repaired after source relocation. What acceptance check is needed?

Query a known source row through the repaired shortcut with a suitably authorized user and compare the resolved source path with the new location.
Check that a shortcut object with the old name still exists in the destination.
Run a privileged query of a different table in the destination workspace.
Compare only the downstream dashboard refresh date after source relocation.

Explanation

Correct Answer

Path plus data-read validation confirms the reference and permissions were fixed without assuming a local copy exists.

Incorrect Answers

A shortcut icon can persist even when its stored target path no longer resolves.

An unrelated table can't validate the repaired source link or the intended consumer identity.

A refreshed dashboard can use cached data and cannot prove live resolution to the new source location.

Question 265

Compliance requires a vendor-controlled data lake to remain in its source cloud account while Fabric analysts query approved slices. Which architecture minimizes unnecessary copies?

Create a full nightly Fabric copy while keeping the external vendor account as source.
Use an authorized external OneLake shortcut to the vendor-controlled storage, enforce the approved delegated connection and OneLake security, and do not create replicated Fabric copies.
Give every analyst direct public access to the vendor storage account for convenience.
Use a SQL warehouse import job and delete the local copy after every query.

Explanation

Correct Answer

A shortcut references data that remains in its vendor storage account; the external connection identity and effective OneLake security limit authorized consumption. Database mirroring would physically replicate records and is inappropriate under a strict source-only residency policy.

Incorrect Answers

The copied replica violates the explicit sole-source residency requirement even if the vendor data stays intact.

Public/unrestricted access violates approved-slice security and is unnecessary for controlled shortcut connections.

Temporary physical replication still creates data outside the permitted source account and increases handling risk.

Question 266

A proposed design performs a full daily copy of a regulated external data lake even though data must remain solely with the vendor. What is the conflict?

The proposed daily copy keeps residency intact because the source account remains unchanged.
The copy will be compliant as long as the destination workspace has a matching domain label.
A full physical copy creates additional stored data outside the approved source account, contrary to the residency requirement.
A full batch copy is equivalent to a metadata-only OneLake shortcut.

Explanation

Correct Answer

The architectural requirement favors reference-based access where supported, not indiscriminate duplication to new storage.

Incorrect Answers

The design creates an extra stored replica even if the original stays at the vendor, violating sole-residency controls.

Governance labels don't override a strict requirement not to store vendor data outside the source account.

A physical extract and a remote reference have different storage and residency consequences.

Question 267

An external-data shortcut is selected for governance. Which evidence best validates source residency and access?

Check only that a shortcut item exists and ignore its actual endpoint URI.
Verify only that the vendor's source data remains readable after deployment.
Test access using a global Fabric admin instead of the intended analyst identity.
Inspect the shortcut target account/path, confirm no unintended full-copy dataset was created and test a permitted read under the approved identity.

Explanation

Correct Answer

This verifies the logical reference, source location and access policy rather than assuming the presence of a shortcut icon proves compliance.

Incorrect Answers

An item icon can't prove references reach the approved vendor account without creating local copies.

Source availability doesn't establish that an unapproved duplicate wasn't created in Fabric storage.

Privileged success cannot prove the configured shortcut's least-privilege source-side authorization.

Question 268

A Dataflow Gen2 has several transformed output queries. The upstream merge succeeds, but two target lakehouse destinations show column-type mapping errors and no new rows. What should the author inspect first?

Inspect the two affected queries’ destination mappings, reconcile incoming column types with the sink schema, then refresh and examine each failing output step.
Assume every Power Query output inherits the first query's lakehouse mapping automatically.
Inspect only the Dataflow preview and publish without checking per-output destination names.
Run a source validation but ignore destination credential and column-mapping errors.

Explanation

Correct Answer

Per-output destination mappings and type-conversion errors can fail even when the upstream transformation is valid. Correcting each sink contract and retesting isolates the two failed writes.

Incorrect Answers

Each intended output requires an appropriate destination; one configured query doesn't imply all three tables are written.

An editor preview proves transformations, not that target mappings are saved and refresh writes reach the intended tables.

A successful source read can coexist with failing or missing sink writes for individual output queries.

Question 269

A Dataflow Gen2 preview contains correct values but one of three configured lakehouse destinations rejects a decimal-to-integer field mapping while the others refresh. Why can this one output remain stale?

The preview rows had already been saved into each destination automatically by the Power Query editor.
That specific output has an incompatible destination column mapping or conversion error even though Power Query preview and other destination writes succeed.
Every output query inherits the first output's lakehouse destination mapping.
The output failure means the source Power Query transform must have returned no rows.

Explanation

Correct Answer

Each query-to-destination mapping must satisfy its own sink schema and conversion rules; a successful preview or sibling output does not validate the failed decimal-to-integer write.

Incorrect Answers

Editor preview can materialize an in-memory sample without configuring or executing target writes for other queries.

Dataflows require suitable destination settings per output; one successful target doesn't guarantee the other queries are mapped.

A complete preview shows transforms can produce rows; destination omission/failure is a separate cause.

Question 270

After correcting a Dataflow Gen2 destination decimal-to-integer mapping, what regression test should confirm no unintended rounding, overflow, or lost data across its three output queries?

Inspect only the first successful destination table and declare the entire Dataflow complete.
Compare transformations in Preview while ignoring the persisted destination row sets.
Inspect per-output refresh success and reconcile fractional, boundary and null input fixtures against stored data types and values in all three lakehouse target tables.
Check an unrelated notebook's most recent completion timestamp.

Explanation

Correct Answer

The fixture detects unintended truncation and overflow, while full destination inspection verifies that all three outputs still write accurately instead of merely completing refresh.

Incorrect Answers

A multi-output dataflow must validate all intended output mappings and target tables, not just one.

Preview doesn't prove per-query refresh and writes occurred in the lakehouse.

A notebook success is not evidence that any specific Dataflow Gen2 destination has refreshed.

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Question 260

Explanation

Correct Answer

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Question 261

Explanation

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Question 262

Explanation

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Question 263

Explanation

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Question 264

Explanation

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Question 265

Explanation

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Question 266

Explanation

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Question 267

Explanation

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Question 268

Explanation

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Question 269

Explanation

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Question 270

Explanation

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