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Confirm each key has one selected row matching the maximum sequence. This validate action addresses a different case (window latest record), where the pipeline picks an arbitrary record instead of the newest sequence. ROW_NUMBER ordered by a deterministic sequence within each key selects the desired latest row. In the stated problem, a newly selected checkpoint location makes the stream reread historical input; this choice would not establish the required streaming checkpoint identity decision.

Restart a fresh session and confirm the imported module version. This validate action addresses a different case (library precedence), where a notebook-scoped installation overrides the expected cluster version. The runtime can resolve conflicting versions from different installation scopes. In the stated problem, a newly selected checkpoint location makes the stream reread historical input; this choice would not establish the required streaming checkpoint identity decision.

Append new files and confirm only new input is processed under checkpoint tracking. This validate action addresses a different case (streaming table choice), where periodic full-refresh queries miss required incremental freshness. A streaming table tracks continuously arriving source data incrementally. In the stated problem, a newly selected checkpoint location makes the stream reread historical input; this choice would not establish the required streaming checkpoint identity decision.

Grant only the needed foreign object privileges and parent usage. This design action addresses a different case (foreign catalog permission), where catalog discovery was mistaken for full table access. Unity Catalog usage and object privileges are still required for foreign data objects. In the stated problem, partners risk losing access or using a leaked long-lived credential; this choice would not establish the required sharing credential rotation decision.

Place selective supported filters near the remote table access. This design action addresses a different case (federated pushdown), where an analyst pulls millions of rows before a selective filter. Supported predicate pushdown can reduce data transferred from a federated source. In the stated problem, partners risk losing access or using a leaked long-lived credential; this choice would not establish the required sharing credential rotation decision.

Use an open-sharing recipient with restricted data and secure credential delivery. This design action addresses a different case (open protocol recipient), where the partner cannot use an internal Databricks-to-Databricks credential flow. Open Delta Sharing can support non-Databricks recipients using the sharing protocol. In the stated problem, partners risk losing access or using a leaked long-lived credential; this choice would not establish the required sharing credential rotation decision.

Compare ingestion delay percentiles with current watermark threshold. This diagnose action addresses a different case (late event semantics), where reports silently discard legitimate late events under an aggressive watermark. A watermark trades supported lateness for state size and timeliness. In the stated problem, returned values cannot be converted to the declared Spark return type; this choice would not establish the required udf return schema decision.

Compare production deployment variables with bundle target overrides. This diagnose action addresses a different case (bundle target overrides), where development job arguments were deployed into the production target. Bundle targets can override environment-specific variables and resource settings. In the stated problem, returned values cannot be converted to the declared Spark return type; this choice would not establish the required udf return schema decision.

Compare deployed job JSON with the version-controlled bundle. This diagnose action addresses a different case (databricks cli resource updates), where production configuration drifts from repository source definitions. Declarative deployment describes resources as reviewed version-controlled artifacts. In the stated problem, returned values cannot be converted to the declared Spark return type; this choice would not establish the required udf return schema decision.

Adjust appropriate shuffle or executor settings based on evidence. This implement action addresses a different case (spark ui failure stage), where operators increase driver memory but failures occur on executors. Spark UI task failures and shuffle/spill metrics distinguish worker pressure from driver issues. In the stated problem, customers receive repeated notifications for the same batch; this choice would not establish the required idempotent retry repair decision.

Correct the missing dependency or incompatible runtime package. This implement action addresses a different case (cluster log diagnostics), where the notebook cell shows only a generic stage failure. Driver and executor logs often contain the exact import or JVM error. In the stated problem, customers receive repeated notifications for the same batch; this choice would not establish the required idempotent retry repair decision.

Deploy using an explicit reviewed production target. This implement action addresses a different case (target-safe deployment), where the wrong workspace is selected because target is implicit. Explicit bundle target selection prevents accidental environment deployment. In the stated problem, customers receive repeated notifications for the same batch; this choice would not establish the required idempotent retry repair decision.

Monitor overlapping intervals and multiple current versions. This monitor action addresses a different case (scd type 2 effective dates), where a single current tier overwrites history in the dimension. SCD Type 2 stores effective start/end intervals for distinct historical dimension versions. In the stated problem, a denormalized mixed-grain table produces redundant attributes and expensive corrections; this choice would not establish the required star schema analytics decision.

Monitor unmapped country codes and divergent dimension versions. This monitor action addresses a different case (conformed dimensions), where the same country appears under conflicting dimension keys across marts. A conformed dimension provides common attribute definitions across fact models. In the stated problem, a denormalized mixed-grain table produces redundant attributes and expensive corrections; this choice would not establish the required star schema analytics decision.

Monitor uniqueness violations at the chosen grain. This monitor action addresses a different case (fact table grain), where summing revenue double-counts orders with multiple items. A dimensional fact must have one clearly documented row grain. In the stated problem, a denormalized mixed-grain table produces redundant attributes and expensive corrections; this choice would not establish the required star schema analytics decision.

Migrate a compatible task to serverless execution with approved permissions. This implement action addresses a different case (serverless job fit), where provisioning overhead exceeds useful task execution time. Supported serverless jobs can reduce infrastructure management and startup overhead. In the stated problem, each small delete rewrites large data files and increases write cost; this choice would not establish the required deletion vectors decision.

Create the managed table under the approved catalog and migrate governed data. This implement action addresses a different case (managed table maintenance), where each table has custom file cleanup jobs and broad storage permissions. Unity Catalog managed tables provide platform-managed data placement and maintenance capabilities. In the stated problem, each small delete rewrites large data files and increases write cost; this choice would not establish the required deletion vectors decision.

Run OPTIMIZE or enable managed optimization where supported. This implement action addresses a different case (small file compaction), where read overhead grows because query plans open excessive small files. OPTIMIZE can compact compatible Delta files without changing logical table contents. In the stated problem, each small delete rewrites large data files and increases write cost; this choice would not establish the required deletion vectors decision.

Inspect Query Profile operators for scan volume and pruning. This diagnose action addresses a different case (query profile scans), where operators assume the query is always CPU-bound. Query Profile identifies scan, pruning and shuffle behavior for SQL workloads. In the stated problem, operators only see the most recent run in the Jobs UI; this choice would not establish the required job operational history decision.

Inspect current notification settings and run failure statuses. This diagnose action addresses a different case (job email notification), where operators manually check the run page every morning. Lakeflow Jobs support status-triggered notifications for run outcomes. In the stated problem, operators only see the most recent run in the Jobs UI; this choice would not establish the required job operational history decision.

Inspect allowed notification-destination types and current run triggers. This diagnose action addresses a different case (job webhook destination), where only personal email notifications are configured. Supported job notification destinations can integrate workflow status with an incident system. In the stated problem, operators only see the most recent run in the Jobs UI; this choice would not establish the required job operational history decision.

Build the bridge and allocate or filter measures according to reporting rules. This implement action addresses a different case (many-to-many bridge), where joining facts directly to the membership table multiplies totals. A bridge table models many-to-many relationships at an explicit membership grain. In the stated problem, joining facts directly on mutable business IDs links history incorrectly; this choice would not establish the required surrogate dimension keys decision.

On change, close the prior version and insert a new dimension version. This implement action addresses a different case (scd type 2 effective dates), where a single current tier overwrites history in the dimension. SCD Type 2 stores effective start/end intervals for distinct historical dimension versions. In the stated problem, joining facts directly on mutable business IDs links history incorrectly; this choice would not establish the required surrogate dimension keys decision.

Materialize the aggregate from quality-controlled silver facts. This implement action addresses a different case (aggregate gold model), where interactive queries scan billions of detail rows for identical totals. A summarized gold table can serve a stable reporting grain efficiently. In the stated problem, joining facts directly on mutable business IDs links history incorrectly; this choice would not establish the required surrogate dimension keys decision.

Inspect executor stderr and driver logs near the failed stage. This diagnose action addresses a different case (cluster log diagnostics), where the notebook cell shows only a generic stage failure. Driver and executor logs often contain the exact import or JVM error. In the stated problem, the only visible dashboard status is FAILED without a dataset name; this choice would not establish the required lakeflow event error classes decision.

Inspect Python version, wheel tags and package resolution logs. This diagnose action addresses a different case (python dependency triage), where a package import error appears only on the target compute. Runtime-specific wheel compatibility and pinning must be tested in CI. In the stated problem, the only visible dashboard status is FAILED without a dataset name; this choice would not establish the required lakeflow event error classes decision.

Inspect failed executor task metrics and exception messages. This diagnose action addresses a different case (spark ui failure stage), where operators increase driver memory but failures occur on executors. Spark UI task failures and shuffle/spill metrics distinguish worker pressure from driver issues. In the stated problem, the only visible dashboard status is FAILED without a dataset name; this choice would not establish the required lakeflow event error classes decision.

Run small and large test inputs and compare execution strategies. This validate action addresses a different case (adaptive execution), where static join strategy performs poorly at varying input volumes. Adaptive Query Execution can adjust supported shuffle partitions and join strategies at runtime. In the stated problem, provisioning overhead exceeds useful task execution time; this choice would not establish the required serverless job fit decision.

Reconcile changed rows and compare scanned file counts. This validate action addresses a different case (predicate-limited merge), where the merge scans unrelated historical partitions. Constraining merge matches to relevant partitions can reduce unnecessary target scanning. In the stated problem, provisioning overhead exceeds useful task execution time; this choice would not establish the required serverless job fit decision.

Compare file count and query result checksums before and after. This validate action addresses a different case (small file compaction), where read overhead grows because query plans open excessive small files. OPTIMIZE can compact compatible Delta files without changing logical table contents. In the stated problem, provisioning overhead exceeds useful task execution time; this choice would not establish the required serverless job fit decision.

Keep filter predicates aligned with the stored partition column. This design action addresses a different case (partition pruning), where a query wraps business_date in a transformation and reads many partitions. Direct predicates on partition keys can enable partition pruning. In the stated problem, the consumer reads only newly appended rows and cannot see updates or deletes; this choice would not establish the required change data feed consumers decision.

Use supported AQE with appropriate shuffle and join settings. This design action addresses a different case (adaptive execution), where static join strategy performs poorly at varying input volumes. Adaptive Query Execution can adjust supported shuffle partitions and join strategies at runtime. In the stated problem, the consumer reads only newly appended rows and cannot see updates or deletes; this choice would not establish the required change data feed consumers decision.

Cache only frequently reused and appropriately sized intermediate data. This design action addresses a different case (cache reuse), where repeated recomputation dominates each branch's execution. Persistence can avoid repeated work if reused data fits the memory budget. In the stated problem, the consumer reads only newly appended rows and cannot see updates or deletes; this choice would not establish the required change data feed consumers decision.