Microsoft DP-700 Practice Test 5

Question 40

A Fabric data warehouse team wants CREATE TABLE and VIEW definitions to be reviewed as individual SQL files and deployed incrementally from source control. Which approach fits the supported warehouse lifecycle?

Use Eventstream to copy warehouse query logs into a lakehouse.
Create a Power BI dashboard for each SQL object.
Connect the warehouse workspace to Git so Fabric represents the warehouse schema as a database project containing SQL object definitions.
Manually copy all table data into a new workspace after every definition edit.

Explanation

Correct Answer

Fabric warehouse Git integration serializes schema into a database project with per-object .sql files; synchronized updates use schema-difference deployment rather than merely transferring query results.

Incorrect Answers

Eventstream telemetry is not a database schema project and does not version table/view definitions.

Reports are consumer artifacts, not authoritative source-controlled SQL schema objects.

A physical data copy is not the SQL database project workflow for source-controlled schema changes.

Question 41

After a Fabric warehouse is Git-connected, a developer expects its live table rows to appear as individual source-control files alongside the schema. Why is this expectation incorrect?

Git-connected warehouses cannot represent CREATE VIEW definitions.
The workspace must be in a domain to allow schema versioning.
The warehouse database project versions schema definitions, not the underlying table data as row-level Git files.
Lakehouse V-Order must be enabled before Git can track warehouse table data.

Explanation

Correct Answer

Fabric Git integration represents warehouse objects as SQL definitions; data remains in the Fabric warehouse and is not serialized into the project as a dataset.

Incorrect Answers

Supported warehouse database projects can represent schema objects, including SQL views, so the missing rows are a different issue.

Domain assignment is governance and is not the technical distinction between schema files and data rows.

V-Order changes Delta file layout and does not cause table rows to be checked into warehouse schema repositories.

Question 42

A release engineer wants proof that a Fabric warehouse schema update is genuinely managed as a database project rather than an untracked manual change. Which TWO checks should be included?

Require every existing warehouse row to appear in the Git repository as a SQL INSERT statement.
Inspect the Git repository for SQL object-definition files representing the approved warehouse schema change.
Confirm only that a new dashboard thumbnail was generated for the workspace.
Verify the approved project synchronization/deployment applied the intended incremental schema difference to the target warehouse.
Set the warehouse capacity to a different SKU and assume the schema change is versioned.

Explanation

Correct Answers

A warehouse database project stores schema definitions as individual .sql files, allowing change review in the branch before synchronization.

Fabric uses DacFx-style schema-difference deployment on synchronization. Checking the target metadata shows that the versioned definition, rather than only a local SQL edit, took effect.

Incorrect Answers

Warehouse Git integration does not version all table data as row-level INSERT scripts; schema definitions are its source-controlled unit.

A thumbnail is not evidence of a schema definition in the database project or its deployment.

Capacity choice is not a substitute for a Git commit of SQL definitions and synchronized schema deployment.

Question 43

Developers changed a Fabric notebook locally in a connected workspace while a separate feature-branch change to the same item was merged remotely. The workspace now reports divergent definitions. What should the team do before synchronizing?

Compare the workspace and repository revisions, resolve any conflict deliberately, and synchronize the approved source of truth.
Ignore the conflict and remove all notebook permissions.
Increase the workspace Spark pool node size.
Rename the default lakehouse in hopes the Git conflict disappears.

Explanation

Correct Answer

Fabric Git integration reports conflicts when the same item changes in both locations. Explicit comparison and conflict resolution prevents unreviewed overwrites and allows a known revision to synchronize.

Incorrect Answers

Item permissions do not reconcile competing source definitions, and ignoring a conflict risks applying the wrong content.

Spark compute sizing cannot decide between diverged notebook definitions in Git and the workspace.

Storage item naming does not merge concurrent edits to the notebook definition.

Question 44

A Fabric-connected workspace shows Git status Conflict and disables Commit for a notebook. The same notebook was edited in the workspace and the remote repository. Which condition explains that status?

The item has competing changes in both the workspace and Git since the last synchronization.
A warehouse transaction held a SQL lock on a lakehouse table.
The notebook’s Spark executor was out of memory.
The workspace has no sensitivity-label policy.

Explanation

Correct Answer

Fabric flags the item as conflicted when both sides have modifications; the operator must choose or merge an intended version before normal commit/sync proceeds.

Incorrect Answers

Warehouse SQL locks do not produce notebook Git definition conflicts or disable the source-control Commit control.

A runtime failure does not itself imply conflicting changes in workspace and remote Git item definitions.

Sensitivity classification is a separate control and cannot explain a direct Git status of Conflict for simultaneous edits.

Question 45

A team resolves a Fabric notebook conflict by reviewing both definitions and accepting the approved version from Git. What should it inspect next to verify there is no unresolved divergence?

Confirm the item’s Git status is synchronized and compare the notebook definition with the approved repository revision.
Check that the notebook still has the same folder color.
Verify an Eventhouse table has finished ingestion.
Inspect the old Spark session log for a successful print statement.

Explanation

Correct Answer

The post-resolution status and actual definition parity show that the chosen incoming version was applied; the workspace should no longer advertise the same item conflict.

Incorrect Answers

Visual folder styling has no bearing on source definition parity or Git conflict state.

Eventhouse ingestion health does not establish that a notebook conflict has been resolved.

A runtime output can be unchanged even when the notebook’s source-control definition is still divergent.

Question 46

A Fabric team wants to promote selected supported items from development through testing into production, with isolated workspaces for each stage. What should it configure?

Use one shared workspace for every stage and rename items after each promotion.
Create a Fabric deployment pipeline with mapped Development, Test, and Production stages, each assigned its appropriate workspace.
Replace the deployment pipeline with a Dataflow Gen2 query step.
Configure Apache Airflow worker autoscale settings to represent test and production.

Explanation

Correct Answer

Fabric deployment pipelines arrange supported workspace content into stages so a reviewed selection can be promoted from a source to a target stage without using one shared workspace for all environments.

Incorrect Answers

A single workspace does not provide the stage isolation and mapped-workspace comparisons intended by deployment pipelines.

A Dataflow transforms data but does not provide the Fabric ALM stage-and-workspace lifecycle.

Airflow pool sizing governs orchestration compute, not lifecycle stages for promoting Fabric workspace items.

Question 47

A team creates a Fabric deployment pipeline but never assigns a workspace to the Test stage. There is no content to compare or promote into that stage. What setup step is missing?

The appropriate Test workspace must be assigned to the pipeline stage before its content can be managed there.
The team must enable a warehouse query result cache before assigning a stage.
The pipeline must publish an Eventstream output before allowing workspace assignment.
Every existing notebook must be converted to a Power BI dashboard first.

Explanation

Correct Answer

Deployment pipeline stages represent specific workspaces. Without a stage workspace, the team lacks the normal target context for comparing, deploying, and checking test-stage items.

Incorrect Answers

Query caching affects data warehouse performance and is not a prerequisite for mapping a workspace to a pipeline stage.

Streaming destinations have no role in the prerequisite workspace assignment.

Fabric deployment pipelines support multiple Fabric item types; stage assignment does not require dashboards.

Question 48

A release engineer assigns one workspace to each stage of a Fabric deployment pipeline. Which observation best validates the structure before any production promotion?

Open the pipeline and confirm Development, Test, and Production each show the intended mapped workspace and supported items for stage comparison.
Verify that every workspace’s default lakehouse contains exactly the same data rows.
Compare the Spark driver memory of unrelated notebooks.
Check only that a Power BI report title contains the word Production.

Explanation

Correct Answer

The stage-to-workspace map and item list are the actual ALM structure; verifying them prevents targeting an unintended production workspace.

Incorrect Answers

Stage workspaces may intentionally contain different test and production data. Row equality does not verify deployment pipeline configuration.

Spark compute resources do not establish stage workspace assignments.

Display names are not authoritative evidence of which workspace is mapped to the production stage.

Question 49

A semantic model deployed from Test to Production must retain a production-specific database parameter rather than inherit the test connection. Which supported lifecycle control is relevant?

Rely on changing the workspace domain label from Test to Production.
Change only the Spark session executor count in Test.
Define an applicable parameter deployment rule on the Production-stage semantic model and redeploy so the rule takes effect.
Use a warehouse table comment containing the production connection string.

Explanation

Correct Answer

Fabric deployment rules can override supported semantic-model parameter values at a target stage. A rule must be valid and applied on deployment to preserve the production endpoint.

Incorrect Answers

Domain labels organize governance; they do not override a semantic model connection parameter on deployment.

Spark compute settings cannot bind a semantic model’s production database parameter.

Table comments are metadata and are not supported deployment parameter rules for semantic models.

Question 50

A Production semantic model used to retain its production connection parameter during each Fabric deployment. The team removed the target-stage parameter rule and then deployed again; the parameter reverted to the Test value. Why?

Every semantic model deployment also republishes its Spark notebook environment.
Fabric domains automatically overwrite data source values across workspaces.
The warehouse’s row-level security disabled the deployment parameter rule.
The production override was no longer defined in a valid applicable deployment rule when the new content was deployed.

Explanation

Correct Answer

Without the target-stage rule, deployment can propagate the source item’s parameter configuration to its paired destination. The rule is what substitutes the production value.

Incorrect Answers

A semantic model parameter is not controlled by notebook Spark environment publication.

Domains are governance groupings and are not the mechanism behind parameter replacement.

Row-level security filters query visibility, not the existence of a semantic-model deployment rule.

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Question 40

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Question 41

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Question 42

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Question 43

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Question 44

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Question 45

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Question 46

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Question 47

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Question 48

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Question 49

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Question 50

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