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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.

Set the supported job notification destination for failed runs. This implement 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, most provisioned resources sit idle between job runs; this choice would not establish the required compute utilization decision.

Implement Jobs API run enumeration and status extraction. This implement action addresses a different case (jobs rest monitoring), where a manual UI report omits failed tasks and paginated runs. The Jobs API exposes run records that a program can paginate and aggregate. In the stated problem, most provisioned resources sit idle between job runs; this choice would not establish the required compute utilization decision.

Record stage timestamps and expose a freshness query. This implement 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, most provisioned resources sit idle between job runs; this choice would not establish the required compute utilization decision.

Confirm access is controlled through Unity Catalog and queries return identical rows. This validate 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, repeated recomputation dominates each branch's execution; this choice would not establish the required cache reuse decision.

Delete a few rows and confirm snapshot consistency without unnecessary rewrites. This validate action addresses a different case (deletion vectors), where each small delete rewrites large data files and increases write cost. Deletion vectors can mark deleted rows without immediate whole-file rewrite when supported. In the stated problem, repeated recomputation dominates each branch's execution; this choice would not establish the required cache reuse decision.

Benchmark identical queries with correct outputs and controlled resources. This validate action addresses a different case (photon query execution), where an unsupported execution mode limits vectorized engine use. Photon can accelerate supported SQL and DataFrame operators on compatible compute. In the stated problem, repeated recomputation dominates each branch's execution; this choice would not establish the required cache reuse decision.

Inspect per-key sequence collisions and upstream log offsets. This diagnose action addresses a different case (cdc duplicate sequence), where the processing order for tied source events is nondeterministic. A deterministic sequencing contract needs a unique ordered event position. In the stated problem, tests only assert that the DataFrame is nonempty; this choice would not establish the required dataframe testing decision.

Inspect pip resolution output and the installed dependency tree. This diagnose action addresses a different case (library version pinning), where a transitive dependency updated to an incompatible version. Explicit compatible dependency versions improve reproducibility across new compute environments. In the stated problem, tests only assert that the DataFrame is nonempty; this choice would not establish the required dataframe testing decision.

Inspect Python UDF execution time and batch serialization in Spark UI. This diagnose 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, tests only assert that the DataFrame is nonempty; this choice would not establish the required dataframe testing decision.

Select Photon-enabled compute for eligible SQL workloads. This design action addresses a different case (photon query execution), where an unsupported execution mode limits vectorized engine use. Photon can accelerate supported SQL and DataFrame operators on compatible compute. In the stated problem, repeated recomputation dominates each branch's execution; this choice would not establish the required cache reuse decision.

Collect useful statistics for frequently filtered columns within supported limits. This design 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, repeated recomputation dominates each branch's execution; this choice would not establish the required cache reuse decision.

Use CDF with committed version checkpoints for incremental changes. This design action addresses a different case (change data feed consumers), where the consumer reads only newly appended rows and cannot see updates or deletes. Delta Change Data Feed exposes row-level changes for supported versions when enabled. In the stated problem, repeated recomputation dominates each branch's execution; this choice would not establish the required cache reuse decision.

Separate environment failures from business logic failures in triage. This design 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, operators rerun the entire job and duplicate completed side effects; this choice would not establish the required repair failed tasks decision.

Tune executor-side partition sizing or skew rather than only driver memory. This design 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, operators rerun the entire job and duplicate completed side effects; this choice would not establish the required repair failed tasks decision.

Define a parameterized replay process with idempotent writes. This design action addresses a different case (override run parameters), where the scheduler launches a full rebuild instead of rerunning the affected date. Job repair or rerun parameters can target a specific processing window. In the stated problem, operators rerun the entire job and duplicate completed side effects; this choice would not establish the required repair failed tasks decision.

Inspect catalog BROWSE and SELECT grants for the discovery group. This diagnose action addresses a different case (browse metadata privilege), where teams grant SELECT merely to expose object listings. BROWSE enables controlled metadata discovery without granting data read access. In the stated problem, both environments use the same default catalog name in notebooks; this choice would not establish the required environment catalog isolation decision.

Inspect effective parent usage and table SELECT grants. This diagnose action addresses a different case (catalog privilege inheritance), where the team lacks USE CATALOG and USE SCHEMA on the parent containers. Unity Catalog object access generally requires relevant parent usage privileges. In the stated problem, both environments use the same default catalog name in notebooks; this choice would not establish the required environment catalog isolation decision.

Inspect external location paths and grants against business scope. This diagnose 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, both environments use the same default catalog name in notebooks; this choice would not establish the required environment catalog isolation decision.

Monitor duplicate bridge rows and invalid overlapping memberships. This monitor 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, a single current tier overwrites history in the dimension; this choice would not establish the required scd type 2 effective dates 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 single current tier overwrites history in the dimension; this choice would not establish the required scd type 2 effective dates 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 single current tier overwrites history in the dimension; this choice would not establish the required scd type 2 effective dates decision.

Monitor max task duration and hot-key distribution. This monitor action addresses a different case (skewed group aggregation), where a single campaign dominates rows and one reduce task runs for minutes. A hot aggregation key causes skew despite ample parallelism. In the stated problem, an order-level aggregation mistakenly treats the array as a single item; this choice would not establish the required array explosion decision.

Monitor merge conflicts and duplicate source-key exceptions. This monitor action addresses a different case (cdc merge semantics), where the current INSERT-only target contains duplicate customer keys. A keyed Delta MERGE can update existing rows and insert new keys. In the stated problem, an order-level aggregation mistakenly treats the array as a single item; this choice would not establish the required array explosion decision.

Monitor invalid timezone offsets and parsing errors. This monitor action addresses a different case (timestamp timezone normalization), where events from different regions are ordered incorrectly in a UTC dashboard. Time-zone normalization is required before comparing timestamps from different sources. In the stated problem, an order-level aggregation mistakenly treats the array as a single item; this choice would not establish the required array explosion 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 merge scans unrelated historical partitions; this choice would not establish the required predicate-limited merge 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, the merge scans unrelated historical partitions; this choice would not establish the required predicate-limited merge decision.

Monitor storage growth and compression effectiveness. This monitor action addresses a different case (columnar compression), where row-oriented text storage increases scans and transfer volumes. Columnar formats and appropriate encoding improve analytical read efficiency. In the stated problem, the merge scans unrelated historical partitions; this choice would not establish the required predicate-limited merge decision.

Model Type 2 records with effective intervals and current-row flag. This design 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.

Choose one order-line grain for transaction detail metrics. This design 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.

Define a common country dimension with agreed identifiers and attributes. This design 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.