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The data platform team needs a narrowly scoped fix: A finance ingestion must run at 02:00 daily with no external source event signal. Which approach satisfies the documented test that run history contains the expected daily occurrence at the configured schedule?
Scheduled triggers execute a workflow at defined times independent of source change events. Verify that run history contains the expected daily occurrence at the configured schedule.
Warehouse destinations in Dataflow Gen2 can require staging before the destination write. However, this alone does not show that run history contains the expected daily occurrence at the configured schedule.
Spark code supports programmable distributed transforms and Delta upsert patterns. However, this alone does not show that run history contains the expected daily occurrence at the configured schedule.
Parameterized notebook execution separates business run context from transformation logic. However, this alone does not show that run history contains the expected daily occurrence at the configured schedule.
A data engineer must validate the completed action: Use a bounded overlap window with keyed deduplication and a safe watermark advancement rule. It targets this failure: A source uses event-time timestamps that arrive several hours late, and a strict previous-midnight watermark misses corrections. Which test outcome should be accepted?
Late-arriving updates require revisiting a lookback period rather than processing only new wall-clock dates.
Streaming architectures ingest and query events with low delay compared with scheduled batch loads. It does not show that a two-hour-late update lands in the target once after reconciliation.
Type 2 slowly changing dimensions preserve prior attribute versions rather than overwriting history. It does not show that a two-hour-late update lands in the target once after reconciliation.
Unknown dimension handling preserves fact integrity without fabricating dimension matches. It does not show that a two-hour-late update lands in the target once after reconciliation.
A Fabric engineer has implemented the following change: Align destination decimal precision/scale and validate source data before copying. The original requirement was: A pipeline run completes but the target schema rejects rows because decimal precision is smaller than source values. Which observation best validates that the change works?
Type overflow creates a deterministic mapping failure, not a transient network fault.
SQL dialect support differs by Fabric engine and operation. It does not show that high-precision test rows load without truncation or silent rounding.
State schema or operator changes can invalidate persisted checkpoints. It does not show that high-precision test rows load without truncation or silent rounding.
A single dataflow query supports a constrained destination configuration; invalid multiples fail validation. It does not show that high-precision test rows load without truncation or silent rounding.
A Fabric engineer has implemented the following change: Check the applicable OneLake security role or item data access grants in addition to the item permission. The original requirement was: An external consumer has an item Read grant but cannot read Lakehouse data through a supported OneLake endpoint. Which observation best validates that the change works?
Item visibility and data-plane read rights can be independently evaluated.
Endorsement communicates content quality and ownership rather than encryption or data authorization. It does not show that the intended user can query the approved data and not a protected neighboring table.
Promotion is a discoverability signal; certification requires an authorized organizational review process. It does not show that the intended user can query the approved data and not a protected neighboring table.
Classification, trust signaling and audit history solve distinct governance requirements. It does not show that the intended user can query the approved data and not a protected neighboring table.
The operations team has applied: Investigate the staging-artifact permission failure and follow the documented ownership/support remediation. Its purpose was: A Dataflow Gen2 refresh fails because the owner of internal staging artifacts left the tenant. Which of the following provides the strongest direct evidence of success?
System-created staging permissions can be tied to an original owner and may outlive a user's access.
An authentication failure must be fixed at the connection boundary, not by changing data mapping. It does not show that the refresh can access the required staging artifacts without granting unrestricted permissions.
Scheduled Spark jobs use their declared environment rather than an old notebook session's temporary libraries. It does not show that the refresh can access the required staging artifacts without granting unrestricted permissions.
Retries and reruns can reexecute completed work, so append-only sinks need duplicate protection. It does not show that the refresh can access the required staging artifacts without granting unrestricted permissions.
A Fabric release must resolve two independent technical requirements. Requirement A: A pipeline If Condition expression evaluates a missing property from a Lookup result and fails before any Copy starts. Requirement B: A Dataflow Gen2 refresh fails because the owner of internal staging artifacts left the tenant. Select TWO actions that directly satisfy both requirements.
Dynamic expressions depend on the actual shape of activity JSON output. Confirm that the condition takes the intended branch for empty and nonempty lookup results.
System-created staging permissions can be tied to an original owner and may outlive a user's access. Confirm that the refresh can access the required staging artifacts without granting unrestricted permissions.
Unbalanced shuffle partitions can exhaust one executor while others remain underutilized.
Shortcut path references are not automatically rewritten when source folders are relocated.
After a production change, the runbook records: Check source connection health, destination mapping and authorization in ingestion diagnostics. The previous problem was: An Eventhouse suddenly contains no new records because its ingestion source connection expired. What test result confirms effective remediation?
Expired source access interrupts ingestion even if the KQL query syntax is valid.
Dataflow source/query schema and destination mapping must agree. It does not show that a new source event appears after the connection is restored.
System-created staging permissions can be tied to an original owner and may outlive a user's access. It does not show that a new source event appears after the connection is restored.
Admins can inherit broader data access than a Viewer; shortcut visibility is not universal authorization. It does not show that a new source event appears after the connection is restored.
The project has two blockers. Blocker A: A Power BI semantic model shows stale metrics after data ingestion succeeded. Blocker B: A dataflow has intermittently slow refreshes but its latest status is green. What pair of actions resolves both blockers?
Step 1 resolves requirement A: Upstream data arrival is separate from semantic model refresh completion. Step 2 resolves requirement B: A healthy final status does not rule out performance regression. Both acceptance tests are required: a successful model refresh timestamp follows the completed ingest and report values update; and the historical refresh report isolates the query whose duration increased.
A healthy final status does not rule out performance regression. The other proposed action (Inspect the pipeline run and its activity-level details in the supported Monitoring hub) does not implement the first control. Upstream data arrival is separate from semantic model refresh completion.
Upstream data arrival is separate from semantic model refresh completion. The other proposed action (open the notebook/Spark application run details and relevant driver or executor logs) addresses a different operation. A healthy final status does not rule out performance regression.
Upstream data arrival is separate from semantic model refresh completion. A healthy final status does not rule out performance regression. The proposed steps (Inspect the pipeline run and its activity-level details in the supported Monitoring hub; open the notebook/Spark application run details and relevant driver or executor logs) address other capabilities instead of this required pair.
The project has two blockers. Blocker A: A pipeline Copy activity fails with an HTTP 401 after a source credential rotation. Blocker B: A PySpark transformation runs out of driver memory after calling collect() on a huge DataFrame. What pair of actions resolves both blockers?
Step 1 resolves requirement A: An authentication failure must be fixed at the connection boundary, not by changing data mapping. Step 2 resolves requirement B: Collect moves data to driver memory and can exhaust it irrespective of executor capacity. Both acceptance tests are required: the authenticated copy test succeeds using the rotated credential; and a large input completes with bounded driver memory consumption.
An authentication failure must be fixed at the connection boundary, not by changing data mapping. The other proposed action (deduplicate the source with deterministic version precedence before applying the merge) addresses a different operation. Collect moves data to driver memory and can exhaust it irrespective of executor capacity.
Collect moves data to driver memory and can exhaust it irrespective of executor capacity. The other proposed action (Repair the approved shortcut connection authorization and validate source data permissions) does not implement the first control. An authentication failure must be fixed at the connection boundary, not by changing data mapping.
An authentication failure must be fixed at the connection boundary, not by changing data mapping. Collect moves data to driver memory and can exhaust it irrespective of executor capacity. The proposed steps (Repair the approved shortcut connection authorization and validate source data permissions; deduplicate the source with deterministic version precedence before applying the merge) address other capabilities instead of this required pair.
A Fabric data engineering release must address both of these independent requirements: (1) Engineers need complex PySpark transformations and Delta MERGE logic on a large Lakehouse table. (2) A Dataflow Gen2 refresh must land transformed rows into a Warehouse and fails because its staging prerequisites were removed. Which combined plan meets both without substituting an unrelated change?
Step 1 resolves requirement A: Spark code supports programmable distributed transforms and Delta upsert patterns. Step 2 resolves requirement B: Warehouse destinations in Dataflow Gen2 can require staging before the destination write. Both acceptance tests are required: a parameterized run updates the expected Delta table without full replacement; and the refreshed dataflow loads into the target Warehouse with no staging validation warning.
Spark code supports programmable distributed transforms and Delta upsert patterns. Warehouse destinations in Dataflow Gen2 can require staging before the destination write. The proposed steps (Build a Fabric data pipeline with Copy, Notebook, dependencies and an on-failure notification path; create a success dependency from Copy to Notebook in the pipeline) address other capabilities instead of this required pair.
Spark code supports programmable distributed transforms and Delta upsert patterns. The other proposed action (create a success dependency from Copy to Notebook in the pipeline) addresses a different operation. Warehouse destinations in Dataflow Gen2 can require staging before the destination write.
Warehouse destinations in Dataflow Gen2 can require staging before the destination write. The other proposed action (Build a Fabric data pipeline with Copy, Notebook, dependencies and an on-failure notification path) does not implement the first control. Spark code supports programmable distributed transforms and Delta upsert patterns.
A Fabric data engineering release must address both of these independent requirements: (1) A notebook needs a different date range on every scheduled invocation without source-code edits. (2) A pipeline replays an interrupted load and creates duplicate records because each retry blindly appends all rows. Which combined plan meets both without substituting an unrelated change?
Step 1 resolves requirement A: Parameterized notebook execution separates business run context from transformation logic. Step 2 resolves requirement B: Orchestration retries are at-least-once attempts unless destination writes are explicitly idempotent. Both acceptance tests are required: two scheduled runs process distinct requested windows without code changes; and a deliberately replayed run leaves one logical row per source event.
Parameterized notebook execution separates business run context from transformation logic. The other proposed action (enable required staging and configure the supported Warehouse data destination) addresses a different operation. Orchestration retries are at-least-once attempts unless destination writes are explicitly idempotent.
Orchestration retries are at-least-once attempts unless destination writes are explicitly idempotent. The other proposed action (Use a Dataflow Gen2 with Power Query transformations and a configured data destination) does not implement the first control. Parameterized notebook execution separates business run context from transformation logic.
Parameterized notebook execution separates business run context from transformation logic. Orchestration retries are at-least-once attempts unless destination writes are explicitly idempotent. The proposed steps (Use a Dataflow Gen2 with Power Query transformations and a configured data destination; enable required staging and configure the supported Warehouse data destination) address other capabilities instead of this required pair.
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