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Create governed dimension tables and stable foreign keys in the fact. This implement action addresses a different case (star schema analytics), where a denormalized mixed-grain table produces redundant attributes and expensive corrections. A star schema links a clear-grain fact to reusable dimensions for consistent analytics. In the stated problem, a destructive table recreation would invalidate downstream consumers; this choice would not establish the required dimension evolution decision.

Map both marts to the governed conformed dimension. This implement 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 destructive table recreation would invalidate downstream consumers; this choice would not establish the required dimension evolution 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, a destructive table recreation would invalidate downstream consumers; this choice would not establish the required dimension evolution decision.

Define a controlled change-management path for resource configuration. This design action addresses a different case (unmanaged resource drift), where subsequent CI redeployment unexpectedly overwrites it. Declarative source of truth should include intentional job-resource changes. In the stated problem, an invalid resource key would be rejected only after production change; this choice would not establish the required bundle validation decision.

Require unit and integration tests plus deployment validation. This design action addresses a different case (ci quality gates), where the deployment pipeline only checks YAML syntax. CI must run transformation tests and controlled integration checks before deployment. In the stated problem, an invalid resource key would be rejected only after production change; this choice would not establish the required bundle validation decision.

Require named deployment targets and production approvals. This design 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, an invalid resource key would be rejected only after production change; this choice would not establish the required bundle validation decision.

Monitor job run arguments for unexpected target or region values. This monitor action addresses a different case (job parameters), where code contains hard-coded region IDs and catalog names. Task parameters allow runtime-specific values without changing notebook source. 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.

Track dependency upgrades and compatibility test failures. This monitor 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, returned values cannot be converted to the declared Spark return type; this choice would not establish the required udf return schema decision.

Audit run-as changes and permission denials. This monitor action addresses a different case (job run-as identity), where an employee departure causes permissions failures during scheduled runs. A stable least-privilege service principal can provide durable job identity. 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.

Inspect filters, cardinalities and existing partition layout. This diagnose 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 current partition count is too low for data volume; this choice would not establish the required shuffle partition count decision.

Inspect current table features, delete patterns and runtime compatibility. This diagnose 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, the current partition count is too low for data volume; this choice would not establish the required shuffle partition count decision.

Inspect managed table eligibility and current optimization settings. This diagnose action addresses a different case (predictive optimization), where operators run blanket manual OPTIMIZE jobs regardless of table use. Predictive optimization can automate supported managed-table maintenance based on workload signals. In the stated problem, the current partition count is too low for data volume; this choice would not establish the required shuffle partition count decision.

Inspect wheel tags, runtime Python version and library load error. This diagnose action addresses a different case (wheel abi compatibility), where the wheel works on a developer laptop but fails with a binary compatibility error. Compiled extensions must match the target runtime platform and Python ABI. In the stated problem, a transitive dependency updated to an incompatible version; this choice would not establish the required library version pinning decision.

Inspect dependency graph and upstream task statuses. This diagnose action addresses a different case (job task dag), where dependent tasks sometimes run before the bronze load finishes. Lakeflow Job task dependencies form a directed acyclic execution graph. In the stated problem, a transitive dependency updated to an incompatible version; this choice would not establish the required library version pinning decision.

Inspect task dependency edges and condition outcome values. This diagnose action addresses a different case (conditional task branches), where both the normal and repair tasks run despite a branching requirement. Lakeflow Jobs condition tasks can route downstream execution based on a result. In the stated problem, a transitive dependency updated to an incompatible version; this choice would not establish the required library version pinning decision.

Alert when rescued-data volume rises after schema changes. This monitor action addresses a different case (auto loader rescued data), where unknown or mismatched fields disappear during the bronze load. The rescued data column can preserve records with unexpected schema content for later remediation. In the stated problem, a merge step fails after a producer changes an integer field to string; this choice would not establish the required parquet schema mismatch decision.

Monitor schema-registry incompatibility and decode failures. This monitor action addresses a different case (avro schema evolution), where an older consumer cannot deserialize records from a newer producer schema. Avro writer-reader schema compatibility must be maintained for evolution. In the stated problem, a merge step fails after a producer changes an integer field to string; this choice would not establish the required parquet schema mismatch decision.

Monitor schema changes and rescued-field counts. This monitor action addresses a different case (nested json ingest), where the initial reader assumes one unchanging flat schema and loses new fields. Explicit schema management and rescued data handling protect evolving semi-structured ingestion. In the stated problem, a merge step fails after a producer changes an integer field to string; this choice would not establish the required parquet schema mismatch decision.

Require named deployment targets and production approvals. This design 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, a package import error appears only on the target compute; this choice would not establish the required python dependency triage decision.

Require unit and integration tests plus deployment validation. This design action addresses a different case (ci quality gates), where the deployment pipeline only checks YAML syntax. CI must run transformation tests and controlled integration checks before deployment. In the stated problem, a package import error appears only on the target compute; this choice would not establish the required python dependency triage decision.

Grant only required target write access plus parent usage. This design action addresses a different case (job run permission debug), where the run-as principal lacks MODIFY on the target table. Unity Catalog authorization applies to the job execution identity. In the stated problem, a package import error appears only on the target compute; this choice would not establish the required python dependency triage decision.

Monitor resource drift outside the deployment process. This monitor action addresses a different case (unmanaged resource drift), where subsequent CI redeployment unexpectedly overwrites it. Declarative source of truth should include intentional job-resource changes. In the stated problem, the deployment pipeline only checks YAML syntax; this choice would not establish the required ci quality gates decision.

Monitor validation failures after dependency or CLI upgrades. This monitor action addresses a different case (bundle validation), where an invalid resource key would be rejected only after production change. Bundle validation detects many configuration problems before deployment. In the stated problem, the deployment pipeline only checks YAML syntax; this choice would not establish the required ci quality gates decision.

Monitor CI authentication failures and principal grants. This monitor action addresses a different case (api deployment authentication), where scheduled production deployment fails when the developer's token expires. Workload identity or service principal authentication decouples CI from employees. In the stated problem, the deployment pipeline only checks YAML syntax; this choice would not establish the required ci quality gates decision.

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, each dashboard query recomputes the same expensive aggregation; this choice would not establish the required materialized view choice decision.

Change a customer tier and confirm old and current history rows are queryable. This validate action addresses a different case (scd type 2 history), where overwrite-style updates destroy historical state needed for point-in-time reporting. SCD Type 2 records effective history instead of replacing the previous version. In the stated problem, each dashboard query recomputes the same expensive aggregation; this choice would not establish the required materialized view choice decision.

Confirm target exchange or join behavior changed and query results remain equivalent. This validate action addresses a different case (spark explain plans), where developers tune executor size without inspecting actual exchanges. The physical execution plan exposes exchanges, scan filters and join strategy. In the stated problem, each dashboard query recomputes the same expensive aggregation; this choice would not establish the required materialized view choice decision.

Use Auto Loader with managed file discovery and checkpoint/schema locations. This design action addresses a different case (auto loader file discovery), where listing the full historical directory every run exceeds the ingestion SLA. Auto Loader is designed for incremental new-file discovery with persisted state. In the stated problem, a nightly overwrite accidentally removes historical audit events; this choice would not establish the required append-only delta bronze decision.

Use a stable checkpoint with a compatible idempotent Delta sink. This design action addresses a different case (kafka offset durability), where restarting with a fresh checkpoint replays previously ingested messages. A stable streaming checkpoint maintains Kafka offsets and sink progress across restarts. In the stated problem, a nightly overwrite accidentally removes historical audit events; this choice would not establish the required append-only delta bronze decision.

Enforce a common ingestion schema or controlled conversion before append. This design action addresses a different case (parquet schema mismatch), where a merge step fails after a producer changes an integer field to string. Parquet schema compatibility matters even when column names are unchanged. In the stated problem, a nightly overwrite accidentally removes historical audit events; this choice would not establish the required append-only delta bronze decision.

Choose fail semantics for non-negotiable constraints. This design action addresses a different case (expectation fail action), where the pipeline continues despite critical null records. Fail-on-violation expectations stop updates rather than silently filtering rows. In the stated problem, a plain dropDuplicates retains state without a practical bound for never-ending keys; this choice would not establish the required streaming dedup watermark 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, a plain dropDuplicates retains state without a practical bound for never-ending keys; this choice would not establish the required streaming dedup watermark decision.

Use a left join plus a documented fallback label for unmatched keys. This design action addresses a different case (conditional enrichment), where an unconditional inner join discards rows with missing region codes. A left join with explicit CASE logic preserves rows while enriching those that match. In the stated problem, a plain dropDuplicates retains state without a practical bound for never-ending keys; this choice would not establish the required streaming dedup watermark decision.