Microsoft DP-700 Practice Test 22

Question 210

A team replaces a copied Delta dataset with a Tables shortcut. What demonstrates that it points to the intended live data?

Reconcile the sizes of two physically copied tables after a nightly batch process.
Check whether raw Parquet files under Files have a matching display name.
Update a controlled record in the original Delta source and confirm a suitably authorized query against the shortcut reflects it without rerunning a physical copy job.
Promote the analyst to workspace Contributor and confirm an unrestricted query succeeds.

Explanation

Correct Answer

A source-to-shortcut read test verifies the logical reference and effective access, distinguishing it from a stale materialized duplicate.

Incorrect Answers

Copy counts can agree while a shortcut points to a wrong or stale source; this checks copying, not live reference.

A Files naming match does not establish a Tables shortcut's Delta target or effective reader access.

A privileged query can hide source-identity denials, failing to validate the intended least-privilege consumer.

Question 211

A data scientist needs Spark to read external Parquet files in an ADLS Gen2 folder without turning them into managed Delta tables. Which OneLake design is appropriate?

Create a Tables shortcut to arbitrary Parquet and assume it becomes a Delta table for SQL.
Use the lakehouse SQL analytics endpoint as the only way to read the Files shortcut.
Copy the external folder into every notebook's local driver filesystem before each run.
Create a supported Files shortcut pointing to the external folder and use the corresponding OneLake Files path in Spark.

Explanation

Correct Answer

The Files area supports raw and semi-structured file references that Spark can read directly without table registration or a full ingest copy.

Incorrect Answers

Raw Parquet without Delta log doesn't satisfy table discovery and is unnecessary for Spark file processing.

SQL endpoints expose eligible Delta tables, while the requested Parquet files are best read through Spark.

Local copies cause data duplication and are not required when supported Files shortcuts expose the remote ADLS path.

Question 212

A shortcut to external Parquet lands in Lakehouse Files, but the SQL analytics endpoint shows no table with that name. Why?

The Files directory is not automatically registered as a SQL Delta table; automatic SQL discovery is associated with suitable Delta data under Tables.
The SQL analytics endpoint needs the remote ADLS account to have a running Fabric Warehouse before it can read Parquet in Files.
The remote ADLS Gen2 account does not contain a Fabric SQL warehouse.
The notebook has not copied external Parquet into its local workspace storage first.

Explanation

Correct Answer

Parquet files exposed for Spark do not become SQL analytics endpoint tables solely because a shortcut exists in the Files hierarchy.

Incorrect Answers

An external SQL Warehouse is not a prerequisite. Raw Parquet stored under Files does not become a registered SQL Delta table simply because a shortcut exists.

A SQL warehouse in the source account is not a prerequisite for Spark to read the external Parquet Files path.

Files shortcuts expose remote objects directly; physical per-notebook copies are not necessary for Spark reads.

Question 213

An engineer creates a Files shortcut for external logs. What validates the design and source permissions?

Confirm that the SQL endpoint lists a same-named table without testing a Spark read.
Read a known Parquet object through its shortcut path using a permitted Spark identity and confirm row contents match the external source.
Count how many folder aliases were created under unrelated workspaces.
Test access using the workspace owner's privileged account instead of the authorized data scientist.

Explanation

Correct Answer

An actual authorized file read tests the correct remote folder and compatible file format without falsely claiming SQL table registration.

Incorrect Answers

The user requested direct raw-file access; SQL auto-discovery is neither required nor a valid proof of file-path authorization.

Alias inventory does not confirm bytes or permissions for the particular ADLS folder referenced by the shortcut.

A privileged identity may access files that the intended runtime identity cannot read; it misses source permissions.

Question 214

A supported operational database must continuously replicate transactions into Microsoft Fabric analytical storage with minimal hand-built ETL. Which feature should be assessed?

Point a OneLake shortcut at the operational database's raw transaction-log file bytes.
Schedule a full one-time SQL snapshot and assume future changes propagate automatically.
Choose a supported Fabric mirroring connector and validate its initial snapshot, ongoing change capture, privileges and documented source limitations.
Use a generic source-side timestamp filter without checking connector support for deletes.

Explanation

Correct Answer

Mirroring can bring source records into Fabric through managed replication when the source database and connector meet requirements.

Incorrect Answers

A raw shortcut does not interpret supported proprietary database change-capture semantics or replicate relational tables.

One snapshot captures initial state only, not ongoing inserts, updates and deletes as required.

Timestamp-only extraction can miss deletes, while supported mirroring change capture has defined source prerequisites.

Question 215

A mirroring setup imports initial rows but no subsequent source updates appear. Change tracking prerequisites were not enabled on the source. Why?

The source database is not being copied again through a daily warehouse Import job.
The target lakehouse lacks a V-Order optimization schedule for replicated rows.
The mirror did not generate a new snapshot after every source INSERT.
The replication connector cannot obtain the required ongoing change feed after its initial snapshot because source mirroring prerequisites are incomplete.

Explanation

Correct Answer

A baseline import and continuous replication are separate phases; supported change-capture permissions/configuration are needed for ongoing synchronization.

Incorrect Answers

Managed mirroring uses a source change feed, not a separate daily warehouse import as a requirement for updates.

V-Order affects physical read layout; it does not enable the source connector's missing change-tracking feed.

An initial snapshot is followed by continuous supported change capture; repeating a full snapshot is not the root requirement.

Question 216

A team enables managed mirroring for a source database. What acceptance test demonstrates continuous replication?

Reconcile the initial snapshot, then insert, update and delete controlled source keys and inspect replication status and destination results after expected lag.
Verify that only the original source snapshot count matches the target once.
Read the mirror's display name and compare it with the source database name.
Test only a newly created Fabric table unrelated to the mirrored source.

Explanation

Correct Answer

Testing post-snapshot changes validates ongoing capture rather than claiming success from only the initial data load.

Incorrect Answers

Snapshot equality cannot establish that ongoing changed rows and deletes are captured after setup.

Naming equivalence gives no evidence of actual change-feed subscriptions, lag or data reconciliation.

An unrelated Fabric table does not exercise the connector and source change-capture behavior under review.

Question 217

An existing OLTP database uses a proprietary on-disk page format, and analysts need its relational tables in Fabric. Which ingestion approach should the architect choose?

Rename an OLTP database storage page file to Parquet and register it under lakehouse Tables.
Use a supported database mirroring or connector-based ingestion method that understands the source schema instead of pointing a raw file shortcut at its datafiles.
Create a Files shortcut to the database's native storage folder and rely on Fabric SQL to decode it.
Build a dashboard over the database file path instead of using a supported ingest connector.

Explanation

Correct Answer

OneLake shortcuts expose compatible file data; they are not a generic database engine for proprietary storage pages and logs.

Incorrect Answers

Renaming does not convert proprietary database page structures into a valid Delta/Parquet relational table.

Raw filesystem references don't supply the database connector protocol or transactional schema interpretation.

Presentation cannot turn proprietary storage bytes into typed relational rows in Fabric.

Question 218

A team creates a Files shortcut to a proprietary database file and then attempts to query it as a Delta table. What is the root issue?

The SQL engine was not given permission to rename a proprietary file with a Delta suffix.
The source table has not been exported to a Power BI model first.
The target is neither a compatible Delta table nor a raw file format the chosen Fabric engine can interpret as a relational table.
A OneLake shortcut was attached to Files rather than to a Fabric domain.

Explanation

Correct Answer

A pointer does not transform vendor-specific database files into OneLake-registered Delta tables.

Incorrect Answers

Renaming extensions cannot create Delta transaction-log semantics or a supported relational reader for the file format.

A semantic model is not a proprietary database storage decoder; a supported connector is needed.

Domain assignment does not parse proprietary database pages, and Tables placement alone cannot convert a proprietary format.

Question 219

An ingestion design replaces a nonfunctional proprietary-file shortcut with a supported connector. Select TWO validations that prove it works.

Inspect the shortcut icon and infer that all underlying source columns were ingested.
Rename the source file to .delta and verify the SQL query no longer errors.
Query a representative set of relational rows and column types in the Fabric destination, comparing them to the source.
Validate only that everyone has Contributor access to the source storage account.
Exercise an approved ongoing change or scheduled incremental load and confirm a later update is reflected.

Explanation

Correct Answers

A typed data result proves the connector decoded the original database representation correctly.

A post-initial-load change test verifies the selected ingestion or mirroring mode rather than only its initial connection.

Incorrect Answers

The icon shows a reference, not a successful decode, schema mapping, or relational row transfer.

An extension rename does not create the required Delta log and can leave incompatible bytes unchanged.

Broad authorization cannot prove the connector interpreted columns and supported subsequent inserts/updates correctly.

Question 220

A new Fabric solution includes complex PySpark lake processing and occasional SQL analytics on Delta data. Which store is the better primary engineering landing zone?

Choose a lakehouse with OneLake Delta/Files and Spark, using its SQL analytics endpoint for supported read-only relational queries.
Use a standalone SQL Warehouse as the only storage surface and run all large PySpark files through it.
Use Eventhouse native KQL ingestion as the primary store for conventional PySpark transformations.
Rely on a lakehouse SQL analytics endpoint to execute all writes via T-SQL INSERT.

Explanation

Correct Answer

Lakehouse offers native file and Delta engineering workflows while still exposing tables through a read-only T-SQL surface.

Incorrect Answers

Warehouse optimizes relational SQL DML; the task specifically needs Spark-centric lakehouse Files and Delta engineering.

Eventhouse targets KQL/event workloads, not the normal Spark lakehouse engineering file surface.

The lakehouse SQL endpoint is read-only for table-data DML; Spark writes provide the engineering path.

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Pass Your Microsoft DP-700 Exam Easy!

Question 210

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Question 211

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Question 212

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Question 213

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Question 214

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Question 215

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Question 216

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Question 217

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Question 218

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Question 219

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Question 220

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