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A Microsoft Fabric project must complete both tasks before signoff. Task 1: An ingestion pipeline replays a successful Copy after a downstream failure and duplicates target rows. Task 2: A notebook raises AnalysisException when reading a Delta table after a source schema change. Choose exactly TWO controls that collectively meet the requirements.
A missing or renamed column breaks Spark analysis before valid execution. Confirm that a schema-compatible rerun reads the expected columns and row counts reconcile.
Retries and reruns can reexecute completed work, so append-only sinks need duplicate protection. Confirm that rerunning the same batch leaves stable row counts.
A single dataflow query supports a constrained destination configuration; invalid multiples fail validation.
Conflicting source matches prevent unambiguous one-target-row updates.
After a production change, the runbook records: Write immutable or versioned raw source files into bronze before curated transformations. The previous problem was: A product catalog must be copied into a bronze raw Lakehouse without losing the original JSON payload for audit. What test result confirms effective remediation?
Bronze ingestion preserves source evidence separately from silver/gold business interpretations.
A file-ready boundary prevents reading partially delivered source data. It does not show that a sample curated row can be traced to its original raw JSON and ingestion ID.
A high-watermark incremental load fetches changes since the last committed boundary. It does not show that a sample curated row can be traced to its original raw JSON and ingestion ID.
Event time supports domain analysis and processing time supports operational freshness diagnosis. It does not show that a sample curated row can be traced to its original raw JSON and ingestion ID.
The data platform team needs a narrowly scoped fix: Notebook jobs share a default environment but a patched library has not reached production sessions after an environment edit. Which approach satisfies the documented test that a new session imports the updated approved package while the published environment version is recorded?
Environment drafts and published settings must be distinguished; existing sessions can retain earlier configuration. Verify that a new session imports the updated approved package while the published environment version is recorded.
Airflow tasks rely on configured connections and runtime settings independent of a Fabric pipeline definition. However, this alone does not show that a new session imports the updated approved package while the published environment version is recorded.
Airflow configuration belongs to the workspace/orchestration setup rather than individual Spark SQL statements. However, this alone does not show that a new session imports the updated approved package while the published environment version is recorded.
Business domains organize content, while workspaces and OneLake roles govern operational access. However, this alone does not show that a new session imports the updated approved package while the published environment version is recorded.
The data platform team needs a narrowly scoped fix: An operations team needs relational T-SQL transformations and governed SQL reporting tables with ACID transactions. Which approach satisfies the documented test that T-SQL loads and analytic queries run against the intended warehouse tables?
Warehouse is optimized for supported SQL data engineering and analytics operations. Verify that T-SQL loads and analytic queries run against the intended warehouse tables.
Shortcuts depend on the current external source connection and permissions. However, this alone does not show that T-SQL loads and analytic queries run against the intended warehouse tables.
Flattening nested structures creates the relational schema required by T-SQL consumers. However, this alone does not show that T-SQL loads and analytic queries run against the intended warehouse tables.
Join choice affects record preservation and business reconciliation. However, this alone does not show that T-SQL loads and analytic queries run against the intended warehouse tables.
An enterprise Fabric solution must resolve this issue: A data warehouse team wants SQL schema objects reviewed as code rather than maintained only through manual portal changes. The acceptance criterion is: a reviewed schema change generates the intended database objects in the target environment. Which implementation should be selected?
Database projects make selected relational schemas deployable and reviewable as source artifacts. Verify that a reviewed schema change generates the intended database objects in the target environment.
Version control alone does not enforce release quality or change authorization. However, this alone does not show that a reviewed schema change generates the intended database objects in the target environment.
Branch-based review resolves competing definition edits before a shared release branch is updated. However, this alone does not show that a reviewed schema change generates the intended database objects in the target environment.
Fabric Git integration support is item-specific; workspace membership alone is insufficient. However, this alone does not show that a reviewed schema change generates the intended database objects in the target environment.
The reviewer will approve this deployment only when it fixes both issues: (a) A sales warehouse needs a SQL view that excludes an internal margin column without hiding the entire table from authorized reporting users. (b) A team needs classification metadata for sensitive Fabric warehouse and Lakehouse artifacts visible to governance tools. Which proposed pair of changes is appropriate?
Step 1 resolves requirement A: Database object-level permissions and views can provide a controlled SQL projection. Step 2 resolves requirement B: Sensitivity labels mark data classification and inform downstream governance; they do not automatically grant read access. Both acceptance tests are required: the reporting user queries the view but lacks SELECT on the confidential base table; and the labels appear on the intended items while existing access permissions remain intact.
Database object-level permissions and views can provide a controlled SQL projection. The other proposed action (use the applicable Microsoft Fabric/Microsoft Purview audit log search with principal, time and operation filters) addresses a different operation. Sensitivity labels mark data classification and inform downstream governance; they do not automatically grant read access.
Sensitivity labels mark data classification and inform downstream governance; they do not automatically grant read access. The other proposed action (Correlate audit events with pipeline run identifiers and supported lineage records) does not implement the first control. Database object-level permissions and views can provide a controlled SQL projection.
Database object-level permissions and views can provide a controlled SQL projection. Sensitivity labels mark data classification and inform downstream governance; they do not automatically grant read access. The proposed steps (Correlate audit events with pipeline run identifiers and supported lineage records; use the applicable Microsoft Fabric/Microsoft Purview audit log search with principal, time and operation filters) address other capabilities instead of this required pair.
The reviewer will approve this deployment only when it fixes both issues: (a) A team sees repeated slow T-SQL query shapes in a Warehouse but cannot identify which one consumes most runtime. (b) An Eventstream destination is overwhelmed by high-volume invalid events before they reach the KQL table. Which proposed pair of changes is appropriate?
Step 1 resolves requirement A: Historical query telemetry identifies problematic workload patterns rather than guessing from one query. Step 2 resolves requirement B: Early stream filtering reduces downstream ingestion and query overhead. Both acceptance tests are required: the highest-cost query shape is isolated and benchmarked before modification; and valid event throughput improves and filtered invalid event counts are recorded.
Early stream filtering reduces downstream ingestion and query overhead. The other proposed action (Run supported Delta OPTIMIZE compaction for the affected table after evaluating workload impact) does not implement the first control. Historical query telemetry identifies problematic workload patterns rather than guessing from one query.
Historical query telemetry identifies problematic workload patterns rather than guessing from one query. Early stream filtering reduces downstream ingestion and query overhead. The proposed steps (Run supported Delta OPTIMIZE compaction for the affected table after evaluating workload impact; review shuffle stages and partition counts and tune aggregation strategy for the workload) address other capabilities instead of this required pair.
Historical query telemetry identifies problematic workload patterns rather than guessing from one query. The other proposed action (review shuffle stages and partition counts and tune aggregation strategy for the workload) addresses a different operation. Early stream filtering reduces downstream ingestion and query overhead.
A Fabric data engineer receives a change request: A team needs to filter malformed sensor events and transform fields before sending data to Eventhouse. Which action best produces this measurable result: the target receives valid transformed events and rejected records are accounted for?
Stream transformations can filter or project event fields before ingestion. Verify that the target receives valid transformed events and rejected records are accounted for.
Query acceleration caches relevant shortcut data for faster queries while retaining source storage. However, this alone does not show that the target receives valid transformed events and rejected records are accounted for.
Streaming imposes state and always-on operation costs that a finite batch task may not need. However, this alone does not show that the target receives valid transformed events and rejected records are accounted for.
Materialized aggregations can precompute repeated summaries to reduce query cost. However, this alone does not show that the target receives valid transformed events and rejected records are accounted for.
After a production change, the runbook records: Check Eventstream destination health, bindings, event schema and routing rather than changing source timestamps first. The previous problem was: A telemetry producer publishes data to an Eventstream but a consumer reads no events because its destination is disconnected. What test result confirms effective remediation?
The stream must have an active compatible destination for events to be materialized there.
Custom microbatch side effects can rerun; checkpoint alone does not make arbitrary writes exactly once. It does not show that a test message travels from source through the configured path to the sink.
Materialized aggregations can precompute repeated summaries to reduce query cost. It does not show that a test message travels from source through the configured path to the sink.
A OneLake shortcut exposes external Delta data for KQL querying without a full ingestion copy. It does not show that a test message travels from source through the configured path to the sink.
A Fabric data engineering release must address both of these independent requirements: (1) Two Fabric Lakehouses in different workspaces must share a stable read-only data path without copying tables. (2) A notebook accidentally converts a huge Spark DataFrame to pandas and crashes the driver. Which combined plan meets both without substituting an unrelated change?
Step 1 resolves requirement A: Internal shortcuts avoid redundant copies and simplify data sharing. Step 2 resolves requirement B: Collecting full distributed data to the driver defeats Spark scaling and can exhaust memory. Both acceptance tests are required: the target reads the source updates and storage is not redundantly ingested; and a large dataset runs without driver out-of-memory and outputs correct aggregate results.
Internal shortcuts avoid redundant copies and simplify data sharing. Collecting full distributed data to the driver defeats Spark scaling and can exhaust memory. The proposed steps (Use an Eventhouse/KQL database rather than forcing the logs into a nightly relational warehouse load; define a type-safe source-to-target schema mapping and test conversion behavior) address other capabilities instead of this required pair.
Collecting full distributed data to the driver defeats Spark scaling and can exhaust memory. The other proposed action (Use an Eventhouse/KQL database rather than forcing the logs into a nightly relational warehouse load) does not implement the first control. Internal shortcuts avoid redundant copies and simplify data sharing.
Internal shortcuts avoid redundant copies and simplify data sharing. The other proposed action (define a type-safe source-to-target schema mapping and test conversion behavior) addresses a different operation. Collecting full distributed data to the driver defeats Spark scaling and can exhaust memory.
Two data engineering defects have separate acceptance criteria. First: A customer pipeline failed overnight and operators need the exact failing Copy activity, duration and source error. Second: A Power BI semantic model shows stale metrics after data ingestion succeeded. Which TWO changes are necessary to resolve both?
Upstream data arrival is separate from semantic model refresh completion. Confirm that a successful model refresh timestamp follows the completed ingest and report values update.
Run-level status does not replace individual activity diagnostics for failure localization. Confirm that the report identifies the failing activity, run ID, error and elapsed time.
Notebook failures can originate in the Spark job rather than pipeline orchestration.
Source activity does not prove a downstream destination is consuming messages.
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