Microsoft DP-700 Practice Test 16

Question 150

An ingestion pipeline now runs a Notebook activity before a quality check. Which test proves that the intended Spark code executed?

Check only the pipeline activity run state without inspecting notebook execution artifacts.
Read the output Delta table without checking whether the Notebook activity ran in this invocation.
Inspect the notebook activity’s run and configured environment, then verify the expected Delta output exists before the dependent quality activity starts.
Confirm that Dataflow Gen2 has a refresh schedule even though the pipeline calls a notebook.

Explanation

Correct Answer

The notebook run evidence plus downstream data and dependency ordering tests actual execution, not only that a notebook item was linked.

Incorrect Answers

A green pipeline state can coexist with an empty or wrong Delta output; the actual notebook activity, environment and resulting table must be examined.

An old table may already exist. Correlating activity execution and current output is necessary before attributing the transformation to this run.

A dataflow schedule does not prove the custom PySpark notebook was invoked and completed before the quality dependency.

Question 151

A business analyst needs repeatable UI-based data shaping across supported connectors using joins and column type changes. No custom Spark code is required. What Fabric component fits?

Schedule an empty pipeline Notebook activity to run the Power Query joins.
Configure a Copy activity with no transformations and rely on source field names to perform joins.
Create a Dataflow Gen2 without defining a data destination or executing refresh.
Build a Dataflow Gen2 with the necessary Power Query transformations, source connections and destination configuration.

Explanation

Correct Answer

Dataflow Gen2 supports low-code Power Query shaping and can write results to supported Fabric destinations as a refreshable artifact.

Incorrect Answers

The task asks for repeatable low-code connector-based data shaping, which Dataflow Gen2 supports directly. An empty notebook does not define the join or type conversions.

Copy transfers data and mappings; it does not automatically merge two connectors and implement the specified Power Query shaping rules.

The Power Query transformations can exist in preview but must have a supported materialization destination and run to produce the curated data.

Question 152

A Power Query transformation is created in a Dataflow Gen2 but target rows never change because the dataflow has no configured destination. What is missing?

The dataflow must have a supported destination and successful refresh path for transformed results to be materialized in the target.
Power Query automatically writes every prepared query into the default lakehouse without a destination selection.
The missing rows indicate that the source requires a larger Spark executor pool.
The dataflow cannot write joined data until the workspace is assigned to a governance domain.

Explanation

Correct Answer

Authoring M queries in the editor does not itself write updated data into a lakehouse or warehouse without an operational destination.

Incorrect Answers

A Dataflow Gen2 transformation is not a materialized output until it has an appropriate supported destination configured and refreshed.

Dataflow Gen2 uses its dataflow engine, and an absent output target cannot be corrected by changing unrelated Spark compute.

Fabric domains organize ownership and discovery; they do not supply a missing dataflow destination mapping.

Question 153

An operations team wants to verify a Dataflow Gen2 that joins two supported sources and writes a curated table. What evidence is strongest?

Inspect the Power Query preview but skip the published refresh and destination check.
Run the dataflow refresh, inspect query-step diagnostics and confirm the expected destination schema and representative transformed rows.
Verify that source connections exist without checking the transformed target schema.
Read the target lakehouse name only, without checking the query-specific destination configuration.

Explanation

Correct Answer

Actual refresh and result tests prove both connector execution and shaping semantics; preview-only correctness is insufficient.

Incorrect Answers

Preview rows prove transformation design at authoring time, not that a refreshed target table received those rows.

A connection can succeed while the destination is unmapped or query-step errors prevent the intended table from materializing.

Naming the target does not show that every output query is mapped to the correct table and the refresh completed successfully.

Question 154

A data movement task needs to copy supported Azure SQL rows into a Fabric lakehouse with no complex transformation. Which pipeline activity should be selected?

Use a Notebook activity with no code to access Azure SQL and assume data will move.
Choose Dataflow Gen2 without connecting the Azure SQL source or assigning an output destination.
Configure a pipeline Copy data activity with supported source and sink connections, mappings and an appropriate write mode.
Select Copy but keep the sink write mode and mapping unspecified for the target table.

Explanation

Correct Answer

The Copy activity is a dedicated data movement operator and avoids unnecessary custom Spark code for straightforward ingestion.

Incorrect Answers

A notebook needs explicit source-read and sink-write logic; an empty Notebook activity is not the low-code Copy data movement requested.

Dataflow Gen2 is a supported transformation path but an unconfigured source and destination cannot accomplish this simple database copy.

Copy requires a valid source, destination, mappings and expected write mode. An undefined sink can fail or write somewhere unintended.

Question 155

A pipeline invokes only a notebook print statement and never reads the Azure SQL source, but the author expected a table to appear in a lakehouse. What is missing?

Printing a success message implicitly creates a Copy activity for the configured Azure SQL source.
The lakehouse refuses the write because Copy activity has the wrong mapped column order.
A storage event trigger must be attached before any pipeline can copy SQL rows.
No configured data movement activity or equivalent notebook ingestion logic reads the source and writes the destination.

Explanation

Correct Answer

A successful notebook print is not proof of a Copy operation; the pipeline must execute an actual read/write path.

Incorrect Answers

A notebook print operation has no automatic database transfer side effects; no source read or destination write is implemented in the current pipeline.

The scenario contains no Copy activity or ingestion logic at all, so a column-mapping defect is not the explanation for zero transferred rows.

Triggers determine when a pipeline starts. They cannot substitute for a missing data movement activity within an already invoked pipeline.

Question 156

An initial Copy activity load reports success. What independent check is most appropriate?

Inspect Copy activity row/byte metrics and compare representative source keys with the intended sink rows, schema and write mode.
Accept the Copy activity’s Success status as proof that every source row reached the intended lakehouse.
Check the lakehouse item exists but compare neither source keys nor copied row counts.
Inspect Spark notebook logs that are unrelated to the Copy activity.

Explanation

Correct Answer

Source-to-destination reconciliation catches wrong targets and silent mapping mismatches that a green activity icon alone cannot detect.

Incorrect Answers

Success alone does not establish that the configured sink, write mode, schemas and business keys match the intended source.

Creating a sink item is not proof that the relevant table data was written correctly during the activity.

An unrelated Spark run says nothing about source-to-sink mapping, Copy metrics or target row accuracy.

Question 157

An orchestration computes the number of files copied and must use it in a later notification. Which approach preserves that transient result within the pipeline run?

Hard-code the current count in the notification subject line.
Capture the Copy activity output or set a pipeline variable and reference it with the appropriate dynamic expression in the downstream activity.
Reference the Copy activity by display name as plain text without expression evaluation.
Set the variable before Copy finishes and never update it from the actual activity output.

Explanation

Correct Answer

Activity outputs and variables are runtime values that can be evaluated by later pipeline activities in the same execution context.

Incorrect Answers

A constant might be correct once but cannot track varying Copy output counts from each pipeline invocation.

A literal activity name cannot resolve the per-run rows-copied output. Supported dynamic expressions or variables must be evaluated.

A pre-set value does not reflect the number of files copied; the later notification needs the executed output or updated variable.

Question 158

A downstream notification prints a literal `@variables('fileCount')` string instead of the numeric result. What should the author check?

The Copy activity output cannot be referenced by any later activity until the workspace is republished.
The variable value is permanently stored in the lakehouse and should be selected with a SQL query.
The activity property was populated as plain text rather than an evaluated dynamic expression for the pipeline variable.
The notification sender substitutes variable names only when the Copy job finishes at midnight.

Explanation

Correct Answer

A textual placeholder is not automatically evaluated in a field that was not configured for dynamic content.

Incorrect Answers

Within one Fabric pipeline run, supported activity outputs and variables can feed downstream dynamic expressions; the problem is evaluation syntax, not workspace publishing.

Pipeline variables are transient per-run expressions, not automatically materialized as database columns for notifications.

Variable interpolation follows the activity property’s dynamic expression syntax, not the clock time at which the run completes.

Question 159

A pipeline uses a variable populated from Copy’s row count. What validation verifies correct propagation?

Compare only the configured variable name against the pipeline JSON without executing Copy.
Use a hard-coded message count and assume it proves that the variable updates.
Confirm the Copy step completed but do not inspect the evaluated variable or sent message.
Run a controlled small copy, check the Copy output count, inspect the evaluated variable and compare the notification content to the same value.

Explanation

Correct Answer

Three linked runtime observations prove that the value was assigned and consumed rather than being a fixed literal or stale prior-run value.

Incorrect Answers

Correct spelling is insufficient: the count must be populated from an actual Copy output and evaluated in the downstream notification.

A constant can match one run by coincidence and cannot validate runtime propagation of the Copy activity’s real row count.

Copy success alone cannot demonstrate the output-to-variable-to-notification data flow specified by the scenario.

Question 160

A Fabric production pipeline must target a production SQL connection, while the same workflow in test targets a sandbox connection. What design minimizes hard-coded stage-specific details?

Use supported pipeline parameters or variable-library references for stage-specific connection settings and bind the activities to the resolved values.
Keep the Test SQL connection hard-coded and change only the pipeline item name to Production.
Use a workspace domain assignment as the only stage-specific connection selector.
Define a production variable but leave the Copy activity using its literal Test source.

Explanation

Correct Answer

Environment-scoped configuration separates deployment logic from connection details and avoids accidentally copying a test endpoint literal into production.

Incorrect Answers

Renaming the pipeline does not change which SQL source Copy actually queries; the active binding remains the sandbox.

Governance domain labels do not substitute for a production parameter, variable-library value or real Copy connection mapping.

Creating a variable alone does not alter the activity. It must be referenced in a supported expression at run time.

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Question 150

Explanation

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Question 151

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Question 152

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Question 153

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Question 154

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Question 155

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Question 156

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Question 157

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Question 158

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Question 159

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Question 160

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