Microsoft DP-700 Practice Test 14

Question 130

A Fabric pipeline must run Monday through Friday at 02:00 in the finance team’s specified local time zone. What is the right schedule configuration?

Use a UTC 02:00 recurrence for a business that requires 02:00 local time throughout DST.
Trigger at 02:00 daily including Saturday and Sunday, then filter the output data later.
Create a fixed weekday schedule with the intended local 02:00 time and the named business time zone, then review DST occurrence behavior.
Read the author’s browser time zone on every pipeline run instead of selecting a named schedule zone.

Explanation

Correct Answer

A named schedule time zone is needed so the specified business hour, rather than a fixed UTC offset, controls submission times.

Incorrect Answers

A UTC-only time can shift relative to a daylight-saving business zone, so it does not preserve the requested local wall-clock recurrence.

A daily run violates the specified Monday-through-Friday execution calendar even if downstream rows are filtered.

A schedule is evaluated by its configured time-zone/recurrence rules. The author device locale does not provide an audited stable business-zone setting.

Question 131

An ETL pipeline fires an hour early after daylight-saving changes. Select TWO settings or diagnoses relevant to this scheduling failure.

The Copy destination is missing a Delta optimization schedule.
The ETL schedule inherits the user laptop locale automatically at each run.
The schedule was configured for 02:00 UTC instead of 02:00 in the business time zone.
The schedule must use the supported named time zone for local recurrence and be reviewed around DST dates.
The pipeline was paused by a warehouse data-retention rule at 02:00.

Explanation

Correct Answers

A UTC clock hour corresponds to different business-local hours on opposite sides of a daylight-saving transition.

Time-zone selection affects when weekday 02:00 occurs in UTC, preventing drift between intended and executed local times.

Incorrect Answers

The observed one-hour shift at daylight-saving time is a recurrence time-zone issue, not a downstream file-layout maintenance configuration.

A pipeline uses its configured schedule and supported time-zone setting. The author must explicitly account for the intended business zone rather than assume the operator device sets it.

Data retention is unrelated to a consistent one-hour shift aligned with DST; review the schedule zone and UTC/local interpretation first.

Question 132

A scheduled pipeline was edited to align with local 02:00. Which test catches clock and frequency configuration mistakes before release?

Check that the schedule’s textual label contains 02:00 but do not inspect actual occurrences.
Manually start the pipeline at 02:00 today and assume its automatic calendar is correct.
Compare only the latest output row count with yesterday’s output.
Inspect the schedule’s forthcoming occurrence list for representative weekdays and DST boundary dates, then verify a controlled scheduled run.

Explanation

Correct Answer

Future occurrence verification validates calendar semantics; a single manual Run click proves execution but not clock-trigger alignment.

Incorrect Answers

A displayed time without its configured time zone can conceal UTC/local confusion. Future occurrences across DST are the essential check.

A manual invocation bypasses recurrence settings and cannot verify weekday selection or daylight-saving transitions.

Counts can be identical despite a trigger firing an hour early or on the wrong weekday; schedule occurrence and actual run time must be examined.

Question 133

A shared Fabric pipeline must copy different monthly source tables based on a caller-provided `TableName`. What design removes hard-coded table names?

Declare TableName but keep the activity source hard-coded as Customers.
Declare a pipeline parameter for the table name and use a validated dynamic expression in the source configuration.
Build a dynamic expression only for the sink table, not the source selection.
Concatenate TableName without validating the permitted identifier set.

Explanation

Correct Answer

Pipeline parameters are supplied per run and dynamic-content expressions bind supported activity properties without editing the definition each invocation.

Incorrect Answers

A parameter definition alone has no effect while the Copy activity points to a fixed table. The source must explicitly bind to the runtime value.

Changing the destination does not make Copy read Orders instead of Customers. The requested control is the source table binding.

Blind identifier insertion can select unintended objects or produce invalid source references; the scenario needs supported validated dynamic source selection.

Question 134

A pipeline is invoked with `TableName=Orders`, but Copy continues reading Customers. The source dataset references a literal Customers name. Why is the parameter ignored?

The Copy destination uses Append rather than Merge and therefore ignores TableName.
The pipeline parameter has been declared but the invocation is missing a Spark environment.
The activity source is not bound to the pipeline parameter; a parameter declaration alone cannot override a hard-coded dataset value.
Source table schema caching prevents replacement of all fixed dataset identifiers.

Explanation

Correct Answer

An input value affects execution only when the relevant source expression actually consumes that parameter at runtime.

Incorrect Answers

Sink write mode controls how rows are persisted, not which literal Customers source table the Copy activity resolves.

A Copy activity source reference must use a dynamic expression linked to the declared parameter; Spark runtime attachment is not the missing mechanism.

The dataset/source still explicitly points at Customers. No amount of schema refresh can cause a fixed literal to evaluate the caller’s TableName parameter.

Question 135

A parameterized pipeline is ready for use across two source tables. What validates that it uses the supplied run input?

Check that the declaration of TableName exists but do not inspect the activity’s effective source.
Run only the default TableName and accept success for both supported source tables.
Inspect the source connection credential without comparing the destination rows for Orders and Customers.
Run the pipeline with two explicitly supported table names and inspect the resolved source in each activity run and its destination records.

Explanation

Correct Answer

The resulting source selection and row lineage establish that each parameter value affects the data movement rather than being ignored.

Incorrect Answers

A declared parameter may be ignored by a literal dataset reference. The test must prove that distinct values change the resolved table.

One successful default case cannot expose a hard-coded source path that ignores a different caller-provided value.

A valid credential is orthogonal to parameter binding. Effective activity parameters and dataset-specific records must be checked for both inputs.

Question 136

A Fabric notebook activity transforms one customer shard at a time. The pipeline knows the shard ID but the notebook uses a default value. What should the pipeline configure?

Provide the shard identifier through the notebook activity’s supported base-parameter mapping and read it in the notebook parameters cell.
Pass the shard ID only to the pipeline parameter and leave the notebook activity base-parameter list blank.
Put the shard ID into the notebook’s display name and keep its default parameter value.
Change the notebook default lakehouse to another shard while keeping the tenant parameter unmapped.

Explanation

Correct Answer

The notebook activity parameter binding sends run-specific values into notebook execution; defining a pipeline variable alone does not replace a notebook default.

Incorrect Answers

Pipeline-level inputs do not automatically override notebook parameter cells. The notebook activity requires explicit mapping.

Renaming a notebook is metadata and cannot pass a runtime identifier into the actual PySpark parameters cell.

A lakehouse attachment controls the data mount; it does not bind the intended shard ID for one invocation.

Question 137

A notebook activity runs successfully but always processes tenant 001 despite the pipeline being called with tenant 041. The activity has no base-parameter mapping. Why?

The notebook has the wrong Spark pool size for tenant 041.
The pipeline tenant value is not passed into the notebook parameters, so the notebook falls back to its own default.
The pipeline variable is automatically copied into all notebook cells without explicit configuration.
A separate OneLake domain must be created for every tenant before parameters are propagated.

Explanation

Correct Answer

An orchestration parameter must be mapped into the notebook activity contract to affect notebook code.

Incorrect Answers

A notebook can succeed on any supported pool while still processing its default tenant. The input is absent because no base-parameter mapping was supplied.

Notebook activity invocation requires a supported parameter mapping and matching notebook parameter cell; pipeline variable existence alone does not override the notebook default.

Fabric governance domains do not transmit runtime notebook arguments. The missing base-parameter binding explains why tenant 001 remains active.

Question 138

A notebook parameter handoff is added to an ETL pipeline. What directly verifies the correct tenant is processed?

Read the notebook parameter default in its source without submitting an override.
Confirm that the pipeline run succeeded without inspecting tenant-qualified output.
Run with a known tenant ID, inspect the activity’s effective parameter values and verify the notebook output contains only that tenant’s expected records.
Check only that the notebook and pipeline share a Fabric workspace.

Explanation

Correct Answer

Input and output correlation checks both binding and execution semantics; a successful activity badge alone could mask default-parameter use.

Incorrect Answers

A source-code default cannot show that the pipeline passes tenant 041 into the executed notebook session.

A successful run could still process tenant 001; effective invocation values and the resulting tenant keys must be compared.

Co-location is neither a parameter map nor evidence that the correct entity identifier reached the notebook execution.

Question 139

A pipeline first extracts customer data and only then should execute a dependent data-quality transform. How should its activities be linked?

Use Upon completion between Copy and transform because it always waits for success.
Use Upon failure between Copy and transform to delay the transformation until extraction ends.
Connect activities using a timing delay with no status-dependent condition.
Connect the transformation activity to the extraction using an explicit Upon success dependency.

Explanation

Correct Answer

The green success dependency prevents the downstream transform from executing when its prerequisite Copy activity fails.

Incorrect Answers

Upon completion waits for a terminal outcome, including failure. It can run the transform on a failed extract; the requirement needs Upon success.

A failure dependency runs only for unsuccessful Copy, the opposite of the intended successful-extract gate.

A fixed delay does not prove a successful upstream copy. The transformation must be explicitly gated by the Copy activity outcome.

Question 140

A downstream notebook starts even though the preceding Copy activity failed. The pipeline uses a completion dependency. What explains the execution?

An Upon completion dependency continues after both success and failure, rather than requiring success from the upstream activity.
The Copy activity has no retry interval, so the transform is allowed to start on success only.
The transform is bound to a success-only condition but the monitoring hub mislabels failure.
The upstream Copy and downstream notebook have identical activity names.

Explanation

Correct Answer

Completion describes finalization, not successful extraction. The downstream step needs the success condition to enforce this business rule.

Incorrect Answers

The configured Upon completion branch fires on success and failure. Retry timing cannot turn that dependency into an Upon success gate.

The scenario expressly identifies an Upon completion condition, which includes both terminal outcomes. There is no evidence of a monitoring labeling defect.

Name similarity is unrelated to dependency outcome semantics. The completion link, not activity naming, permitted the downstream run.

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

Microsoft DP-700 Practice Test 12 - 10 Questions (Last Reviewed 23-Oct-2026)
Microsoft DP-700 Practice Test 13 - 10 Questions (Last Reviewed 22-Oct-2026)
Microsoft DP-700 Practice Test 14 - 10 Questions (Last Reviewed 21-Oct-2026)
Microsoft DP-700 Practice Test 15 - 10 Questions (Last Reviewed 20-Oct-2026)
Microsoft DP-700 Practice Test 16 - 10 Questions (Last Reviewed 19-Oct-2026)

Pass Your Microsoft DP-700 Exam Easy!

Question 130

Explanation

Correct Answer

Incorrect Answers

Question 131

Explanation

Correct Answers

Incorrect Answers

Question 132

Explanation

Correct Answer

Incorrect Answers

Question 133

Explanation

Correct Answer

Incorrect Answers

Question 134

Explanation

Correct Answer

Incorrect Answers

Question 135

Explanation

Correct Answer

Incorrect Answers

Question 136

Explanation

Correct Answer

Incorrect Answers

Question 137

Explanation

Correct Answer

Incorrect Answers

Question 138

Explanation

Correct Answer

Incorrect Answers

Question 139

Explanation

Correct Answer

Incorrect Answers

Question 140

Explanation

Correct Answer

Incorrect Answers

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