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Separate valid silver rows from a restricted quarantine table. This design action addresses a different case (quarantine bad records), where dropping invalid rows silently hides upstream quality failures. Quarantine preserves invalid events separately while allowing valid records through. In the stated problem, events from different regions are ordered incorrectly in a UTC dashboard; this choice would not establish the required timestamp timezone normalization decision.

Rank events by business key, event time and a deterministic tie-breaker. This design action addresses a different case (latest-per-key with ties), where two events have the same event time and yield different surviving rows. A deterministic tie-breaker is required in a per-key window ranking. In the stated problem, events from different regions are ordered incorrectly in a UTC dashboard; this choice would not establish the required timestamp timezone normalization decision.

Preserve unmatched facts with a left join when business rules require them. This design action addresses a different case (join with nullable keys), where rows with null external IDs disappear from an inner join. SQL null equality and join type determine whether unmatched rows survive. In the stated problem, events from different regions are ordered incorrectly in a UTC dashboard; this choice would not establish the required timestamp timezone normalization decision.

Monitor query scans and available column stats. This monitor action addresses a different case (data skipping statistics), where file-level statistics are missing for the filter column. Delta data skipping uses collected column statistics to eliminate files. In the stated problem, partitioning by device would produce many tiny partitions; this choice would not establish the required liquid clustering cardinality decision.

Monitor MERGE scan bytes across incremental batches. This monitor 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, partitioning by device would produce many tiny partitions; this choice would not establish the required liquid clustering cardinality decision.

Monitor plans that lose partition pruning. This monitor 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, partitioning by device would produce many tiny partitions; this choice would not establish the required liquid clustering cardinality decision.

Reproduce an account merge and verify historical facts retain correct links. This validate action addresses a different case (surrogate dimension keys), where joining facts directly on mutable business IDs links history incorrectly. Stable surrogate keys decouple dimensional identity from changing source identifiers. In the stated problem, joining facts directly to the membership table multiplies totals; this choice would not establish the required many-to-many bridge decision.

Compare query file skipping and fact totals before and after. This validate action addresses a different case (liquid clustering for fact models), where rigid daily partitions offer little pruning for customer filters. Liquid clustering enables query-aware data layout without high-cardinality partitioning. In the stated problem, joining facts directly to the membership table multiplies totals; this choice would not establish the required many-to-many bridge decision.

Query facts as of past dates and verify the appropriate historical tier. This validate 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 to the membership table multiplies totals; this choice would not establish the required many-to-many bridge decision.

Query a foreign table from Databricks and reconcile sampled source rows. This validate action addresses a different case (federated connection), where engineers export the full source database into cloud object storage each hour. Lakehouse Federation can query supported external sources through a catalog connection. In the stated problem, catalog discovery was mistaken for full table access; this choice would not establish the required foreign catalog permission decision.

Verify recipient sees the selected dataset but not excluded tables. This validate action addresses a different case (share catalog scope), where the recipient can discover unintended datasets. A Delta Sharing share should contain only explicitly approved assets. In the stated problem, catalog discovery was mistaken for full table access; this choice would not establish the required foreign catalog permission decision.

Confirm the partner reads only the approved shared dataset. This validate 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, catalog discovery was mistaken for full table access; this choice would not establish the required foreign catalog permission decision.

Enable liquid clustering and execute an eligible OPTIMIZE process. This implement action addresses a different case (liquid clustering for fact models), where rigid daily partitions offer little pruning for customer filters. Liquid clustering enables query-aware data layout without high-cardinality partitioning. In the stated problem, malformed source events contaminate metrics without an intermediate quality boundary; this choice would not establish the required medallion bronze silver gold decision.

Generate dimension surrogate IDs and reference them from fact rows. This implement action addresses a different case (surrogate dimension keys), where joining facts directly on mutable business IDs links history incorrectly. Stable surrogate keys decouple dimensional identity from changing source identifiers. In the stated problem, malformed source events contaminate metrics without an intermediate quality boundary; this choice would not establish the required medallion bronze silver gold 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, malformed source events contaminate metrics without an intermediate quality boundary; this choice would not establish the required medallion bronze silver gold decision.

Inspect membership cardinalities and duplicated fact amounts. This diagnose 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, rigid daily partitions offer little pruning for customer filters; this choice would not establish the required liquid clustering for fact models decision.

Inspect dimension history and event order for each account key. This diagnose 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, rigid daily partitions offer little pruning for customer filters; this choice would not establish the required liquid clustering for fact models decision.

Inspect fact primary keys and duplicates at candidate grains. This diagnose 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, rigid daily partitions offer little pruning for customer filters; this choice would not establish the required liquid clustering for fact models decision.

Read the intended dataset and verify access is denied outside the scope. This validate action addresses a different case (external location governance), where raw cloud permissions bypass central catalog governance. External locations map cloud paths to governed Unity Catalog storage credentials. In the stated problem, table and column metadata lack meaningful descriptions; this choice would not establish the required catalog descriptions decision.

Search tagged assets and confirm all known PII columns are classified. This validate action addresses a different case (governed tags), where naming conventions alone are inconsistent across teams. Governed tags can classify assets for discovery and policy workflows. In the stated problem, table and column metadata lack meaningful descriptions; this choice would not establish the required catalog descriptions decision.

Compare returned metadata to known visible catalog objects. This validate action addresses a different case (information schema discovery), where it uses notebook-specific private metadata APIs that break after upgrades. INFORMATION_SCHEMA provides SQL metadata views subject to visibility permissions. In the stated problem, table and column metadata lack meaningful descriptions; this choice would not establish the required catalog descriptions decision.

Create a supported metrics query for failed expectation records. This implement action addresses a different case (expectation monitoring), where analysts confuse successful pipeline status with zero rejected records. Quality metric events reveal input and dropped records separately from update status. In the stated problem, consumer lag grows while microbatch intervals appear stable; this choice would not establish the required streaming backlog decision.

Capture supported query-history metrics in a restricted view. This implement action addresses a different case (sql query history), where no baseline is available for the warehouse query execution. Query history/system query records support comparison of execution time and workload. In the stated problem, consumer lag grows while microbatch intervals appear stable; this choice would not establish the required streaming backlog decision.

Change the relevant join or aggregation strategy based on Spark UI metrics. This implement action addresses a different case (spark ui shuffle metrics), where operators only monitor cluster-wide CPU and miss skewed task durations. Spark UI stage metrics reveal shuffle, spill and task skew. In the stated problem, consumer lag grows while microbatch intervals appear stable; this choice would not establish the required streaming backlog decision.

Monitor state-store row counts, checkpoint size and spill. This monitor action addresses a different case (streaming state growth), where executor memory pressure grows even though input rate is stable. State retention and checkpoint state-store configuration affect memory requirements. 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.

Monitor cluster-key usefulness as query patterns change. This monitor action addresses a different case (liquid clustering cardinality), where partitioning by device would produce many tiny partitions. Liquid clustering organizes data by useful filter keys without high-cardinality partition directories. 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.

Monitor plans that lose partition pruning. This monitor 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.

Choose a materialized view for refreshable precomputed analytics. This design action addresses a different case (materialized view choice), where each dashboard query recomputes the same expensive aggregation. A materialized view maintains a precomputed query result through refresh. In the stated problem, a transitive dependency updated to an incompatible version; this choice would not establish the required library version pinning decision.

Use one managed and repeatable dependency source for the workload. This design 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 transitive dependency updated to an incompatible version; this choice would not establish the required library version pinning decision.

Prefer native Spark expressions or a compatible vectorized Pandas UDF for the calculation. This design action addresses a different case (vectorized udf), where row-by-row Python invocation dominates stage runtime. Pandas UDFs process batches through Arrow rather than per-row Python calls. In the stated problem, a transitive dependency updated to an incompatible version; this choice would not establish the required library version pinning decision.

Alert on cost spikes and untagged spend. This monitor action addresses a different case (billing usage monitoring), where cost reports omit some job and workspace labels. Databricks system billing tables can expose usage records for cost attribution. In the stated problem, workspace notebook logs do not show the full audited account activity; this choice would not establish the required audit log review decision.

Monitor alert evaluations and delivery failures. This monitor action addresses a different case (sql alert threshold), where the current dashboard has no automated alert for missing units. SQL alerts can evaluate a query result against a configured threshold. In the stated problem, workspace notebook logs do not show the full audited account activity; this choice would not establish the required audit log review decision.

Alert on freshness lag rather than only job failure. This monitor action addresses a different case (end-to-end sla), where operators monitor compute uptime rather than data freshness. SLA monitoring should measure source event time to published table availability. In the stated problem, workspace notebook logs do not show the full audited account activity; this choice would not establish the required audit log review decision.