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A team is working on two related Fabric work items. Work item A: A Lakehouse shortcut suddenly fails after a cloud storage credential was rotated. Work item B: A Dataflow Gen2 refresh fails after source columns were renamed but the destination still uses old field mappings. Select TWO specific implementation steps.
Shortcuts depend on the current external source connection and permissions. Confirm that file listing works through the shortcut after credentials are corrected.
Dataflow query shape and destination mapping must remain compatible. Confirm that the updated refresh loads each field into its intended destination column.
Managed gateway connectivity enables approved on-premises source access for supported connectors.
Mirroring replicates supported source changes into Fabric with managed synchronization.
An auditor reviews the applied Fabric control: Use a supported Fabric database project and version its SQL object definitions. It was selected to address: A data warehouse team wants SQL schema objects reviewed as code rather than maintained only through manual portal changes. Which evidence demonstrates the intended result?
Database projects make selected relational schemas deployable and reviewable as source artifacts.
Git sync and environment deployment are different workflows with different source-of-truth expectations. It does not show that a reviewed schema change generates the intended database objects in the target environment.
A linked branch does not automatically mean every local workspace change has been committed. It does not show that a reviewed schema change generates the intended database objects in the target environment.
Promotion of definitions does not imply every environment-specific connection is automatically safe. It does not show that a reviewed schema change generates the intended database objects in the target environment.
An enterprise Fabric solution must resolve this issue: A Spark job creates 5,000 partitions for a small 500 MB input and spends more time scheduling than computing. The acceptance criterion is: the same workload completes faster with a reasonable number of partitions. Which implementation should be selected?
Too many tiny tasks increase scheduling overhead and file output fragmentation. Verify that the same workload completes faster with a reasonable number of partitions.
Historical query telemetry identifies problematic workload patterns rather than guessing from one query. However, this alone does not show that the same workload completes faster with a reasonable number of partitions.
Queued ingestion batching combines small chunks to improve throughput, but adds a bounded freshness delay. However, this alone does not show that the same workload completes faster with a reasonable number of partitions.
Controlled concurrency can reduce end-to-end pipeline runtime without saturating dependencies. However, this alone does not show that the same workload completes faster with a reasonable number of partitions.
The Fabric engineering team must address this requirement: A sales warehouse needs a SQL view that excludes an internal margin column without hiding the entire table from authorized reporting users. Which implementation meets the requirement and provides this acceptance evidence: the reporting user queries the view but lacks SELECT on the confidential base table?
Database object-level permissions and views can provide a controlled SQL projection. Verify that the reporting user queries the view but lacks SELECT on the confidential base table.
Item audit events do not alone identify each source row or transformation step. However, this alone does not show that the reporting user queries the view but lacks SELECT on the confidential base table.
Classification, trust signaling and audit history solve distinct governance requirements. However, this alone does not show that the reporting user queries the view but lacks SELECT on the confidential base table.
Promotion is a discoverability signal; certification requires an authorized organizational review process. However, this alone does not show that the reporting user queries the view but lacks SELECT on the confidential base table.
A shared Fabric backlog includes two mandatory controls. First: A financial warehouse requires dynamically masking account identifiers for lower-privilege SQL readers. Second: A team uses the Promoted endorsement on a model but describes it as independent compliance certification. Which two-part implementation satisfies them together?
Step 1 resolves requirement A: Dynamic data masking changes displayed results for eligible low-privilege readers rather than encrypting stored values. Step 2 resolves requirement B: Promotion is a discoverability signal; certification requires an authorized organizational review process. Both acceptance tests are required: an ordinary reader sees masked identifiers while an authorized privileged account sees the permitted original; and the displayed label and review evidence agree with the actual governance decision.
Dynamic data masking changes displayed results for eligible low-privilege readers rather than encrypting stored values. The other proposed action (correlate audit events with pipeline run identifiers and supported lineage records) addresses a different operation. Promotion is a discoverability signal; certification requires an authorized organizational review process.
Dynamic data masking changes displayed results for eligible low-privilege readers rather than encrypting stored values. Promotion is a discoverability signal; certification requires an authorized organizational review process. The proposed steps (Use the applicable Microsoft Fabric/Microsoft Purview audit log search with principal, time and operation filters; correlate audit events with pipeline run identifiers and supported lineage records) address other capabilities instead of this required pair.
Promotion is a discoverability signal; certification requires an authorized organizational review process. The other proposed action (Use the applicable Microsoft Fabric/Microsoft Purview audit log search with principal, time and operation filters) does not implement the first control. Dynamic data masking changes displayed results for eligible low-privilege readers rather than encrypting stored values.
A data engineer must validate the completed action: Set a safe VACUUM retention policy and validate the required history/recovery window. It targets this failure: A data engineer wants to reclaim obsolete Delta files immediately after a write, ignoring active readers and time travel. Which test outcome should be accepted?
VACUUM removes files no longer needed beyond the retention threshold; aggressive cleanup can break readers or history.
Early time restriction and column projection reduce Eventhouse query work. It does not show that storage cleanup retains files needed by approved concurrent readers and historical queries.
Optimized staged-to-Lakehouse copy can avoid extra serialization and transfer overhead. It does not show that storage cleanup retains files needed by approved concurrent readers and historical queries.
Excessive shuffle overhead can dominate distributed grouping cost. It does not show that storage cleanup retains files needed by approved concurrent readers and historical queries.
The project has two blockers. Blocker A: Business analysts need to clean a small table visually with Power Query and load the result into a supported destination. Blocker B: Each item in a file manifest must be processed by the same notebook without writing 100 nearly identical activities. What pair of actions resolves both blockers?
Step 1 resolves requirement A: Dataflow Gen2 provides visual ingestion/transformation, reducing the need for custom Spark code. Step 2 resolves requirement B: Loop activities apply repeatable steps to each collection element. Both acceptance tests are required: the refreshed destination contains the correctly typed and filtered rows; and every input file has one traceable child execution and the configured concurrency limit is respected.
Dataflow Gen2 provides visual ingestion/transformation, reducing the need for custom Spark code. Loop activities apply repeatable steps to each collection element. The proposed steps (Define notebook parameters or a supported parameter cell and supply values through the pipeline activity; use a Fabric notebook or Spark job definition instead of only a visual Dataflow) address other capabilities instead of this required pair.
Dataflow Gen2 provides visual ingestion/transformation, reducing the need for custom Spark code. The other proposed action (use a Fabric notebook or Spark job definition instead of only a visual Dataflow) addresses a different operation. Loop activities apply repeatable steps to each collection element.
Loop activities apply repeatable steps to each collection element. The other proposed action (Define notebook parameters or a supported parameter cell and supply values through the pipeline activity) does not implement the first control. Dataflow Gen2 provides visual ingestion/transformation, reducing the need for custom Spark code.
Two data engineering defects have separate acceptance criteria. First: Direct Lake reporting over a Delta table is slow because newly written files lack read-optimized organization. Second: A Dataflow Gen2 staging Lakehouse is V-Order compressing transient intermediate data that downstream queries never reuse. Which TWO changes are necessary to resolve both?
V-Order organizes Parquet layout to favor supported Fabric analytic readers. Confirm that a benchmark demonstrates improved read time without changing table content.
Staging data typically has few rereads, so its write compression overhead may outweigh read benefit. Confirm that refresh CPU declines without changing the optimized destination output.
Excessive shuffle overhead can dominate distributed grouping cost.
Queued ingestion batching combines small chunks to improve throughput, but adds a bounded freshness delay.
A data engineer must validate the completed action: Deduplicate using a stable invoice key and ordered source version before applying an idempotent MERGE. It targets this failure: Daily source snapshots overlap in time and repeated imports have created duplicate invoices. Which test outcome should be accepted?
Business-key upserts prevent replayed snapshots from multiplying logical records.
Unknown dimension handling preserves fact integrity without fabricating dimension matches. It does not show that importing the same snapshot twice leaves identical invoice counts and values.
Streaming architectures ingest and query events with low delay compared with scheduled batch loads. It does not show that importing the same snapshot twice leaves identical invoice counts and values.
A file-ready boundary prevents reading partially delivered source data. It does not show that importing the same snapshot twice leaves identical invoice counts and values.
A data engineer must validate the completed action: Configure supported monitoring alerts or automation from pipeline failure status and run history. It targets this failure: A team wants an alert when a scheduled data ingestion has failed twice in succession. Which test outcome should be accepted?
Operational alerting turns run failures into actionable notifications rather than manual polling.
Editing/publishing validation and scheduled refresh are distinct operational states. It does not show that two seeded failed runs produce the intended alert and a successful run does not.
Run provenance identifies the environment that produced a dataset. It does not show that two seeded failed runs produce the intended alert and a successful run does not.
A healthy final status does not rule out performance regression. It does not show that two seeded failed runs produce the intended alert and a successful run does not.
The Fabric engineering team must address this requirement: A large bulk pipeline occasionally fails with transient throttling, but retrying malformed schema requests only wastes capacity. Which implementation meets the requirement and provides this acceptance evidence: a simulated 429 is retried within budget and an invalid schema stops with a report?
Retry policy should recover intermittent failures without endlessly replaying permanent errors. Verify that a simulated 429 is retried within budget and an invalid schema stops with a report.
Parameterized notebook execution separates business run context from transformation logic. However, this alone does not show that a simulated 429 is retried within budget and an invalid schema stops with a report.
Warehouse destinations in Dataflow Gen2 can require staging before the destination write. However, this alone does not show that a simulated 429 is retried within budget and an invalid schema stops with a report.
Query transformations and data destination settings are separate deployment concerns. However, this alone does not show that a simulated 429 is retried within budget and an invalid schema stops with a report.
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