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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.

Submit a known broken transformation and confirm deployment is blocked. This validate 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, deployers have no record of the previous working configuration; this choice would not establish the required rollback deployment decision.

Run the pipeline and confirm the previous error class does not recur. This validate action addresses a different case (lakeflow event error classes), where the only visible dashboard status is FAILED without a dataset name. Pipeline event logs identify failing update and dataset messages. In the stated problem, deployers have no record of the previous working configuration; this choice would not establish the required rollback deployment decision.

Confirm the fix handles the record and leaves valid cases unchanged. This validate action addresses a different case (pipeline debug sampling), where re-running a huge source in development is slow and risky. A minimal reproducible input isolates the failure without modifying production data. In the stated problem, deployers have no record of the previous working configuration; this choice would not establish the required rollback deployment decision.

Use immutable surrogate dimension keys mapped from source business IDs. This design 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, a single current tier overwrites history in the dimension; this choice would not establish the required scd type 2 effective dates decision.

Plan an additive nullable dimension attribute with documented defaults. This design action addresses a different case (dimension evolution), where a destructive table recreation would invalidate downstream consumers. Additive schema evolution can preserve contracts when null defaults are acceptable. 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.

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 single current tier overwrites history in the dimension; this choice would not establish the required scd type 2 effective dates 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 refactor broke a notebook-relative import in the deployed job; this choice would not establish the required python wheel packaging 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 refactor broke a notebook-relative import in the deployed job; this choice would not establish the required python wheel packaging decision.

Inspect task invocation mode and module entrypoint expectations. This diagnose action addresses a different case (python task entrypoints), where the task exits before setting up its argument parser. A standard Python entrypoint and explicit arguments avoid notebook runtime assumptions. In the stated problem, a refactor broke a notebook-relative import in the deployed job; this choice would not establish the required python wheel packaging decision.

Inspect run intervals, executor utilization and billed usage. This diagnose action addresses a different case (compute utilization), where most provisioned resources sit idle between job runs. Compute metrics and usage history distinguish idle capacity from active work. In the stated problem, cost reports omit some job and workspace labels; this choice would not establish the required billing usage monitoring decision.

Inspect query history for SQL text identity, duration and warehouse. This diagnose 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, cost reports omit some job and workspace labels; this choice would not establish the required billing usage monitoring decision.

Inspect run status, task IDs and time windows in supported jobs system tables. This diagnose action addresses a different case (job operational history), where operators only see the most recent run in the Jobs UI. Jobs system records support historical analysis of runs and tasks. In the stated problem, cost reports omit some job and workspace labels; this choice would not establish the required billing usage monitoring 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, operators run blanket manual OPTIMIZE jobs regardless of table use; this choice would not establish the required predictive optimization 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, operators run blanket manual OPTIMIZE jobs regardless of table use; this choice would not establish the required predictive optimization decision.

Choose a supported columnar Delta or Parquet layout for analytics. This design 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, operators run blanket manual OPTIMIZE jobs regardless of table use; this choice would not establish the required predictive optimization decision.

Replay representative late events and verify expected acceptance or exclusion. This validate 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, applying events as append-only rows leaves outdated active accounts; this choice would not establish the required auto cdc keys decision.

Replay a batch ID and confirm target rows are not duplicated. This validate action addresses a different case (streaming idempotent sink), where a retry of a completed batch produces duplicate sink rows. foreachBatch can execute a batch again and requires idempotent external writes for exactly-once effects. In the stated problem, applying events as append-only rows leaves outdated active accounts; this choice would not establish the required auto cdc keys 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, applying events as append-only rows leaves outdated active accounts; this choice would not establish the required auto cdc keys decision.

Monitor ownership transfers and stale personal owners. This monitor action addresses a different case (ownership reassignment), where new data stewards cannot maintain its grants. Unity Catalog ownership should be assigned to a maintained principal. In the stated problem, investigators cannot tell which upstream transformation produced it; this choice would not establish the required lineage tracking decision.

Monitor unexpected new tables entering the governed schema. This monitor action addresses a different case (schema-level grant inheritance), where analysts must repeatedly request identical SELECT grants for each table. Unity Catalog schema-level privileges can apply to existing and future child objects. In the stated problem, investigators cannot tell which upstream transformation produced it; this choice would not establish the required lineage tracking decision.

Audit unapproved external paths and location changes. This monitor action addresses a different case (managed storage ownership), where the data platform cannot apply consistent lifecycle governance. Managed locations centralize storage ownership for Unity Catalog managed assets. In the stated problem, investigators cannot tell which upstream transformation produced it; this choice would not establish the required lineage tracking decision.

Inspect consumer business need and raw table grants. This diagnose action addresses a different case (secure view sharing), where granting SELECT on the raw fact table violates minimization. A governed aggregation view can expose only approved derived information. In the stated problem, a broad SELECT still includes unstructured notes; this choice would not establish the required sensitive data suppression decision.

Inspect notebook and folder ACLs for broad edit permissions. This diagnose action addresses a different case (workspace object acl), where the team needs read-only access for most users. Workspace object permissions can restrict who can edit or manage shared code. In the stated problem, a broad SELECT still includes unstructured notes; this choice would not establish the required sensitive data suppression decision.

Inspect user identity mapping and current filter-policy attachment. This diagnose action addresses a different case (unity catalog row filter), where a shared table currently returns all regions to every reader. Unity Catalog row filters can restrict row visibility through governed policies. In the stated problem, a broad SELECT still includes unstructured notes; this choice would not establish the required sensitive data suppression decision.

Run the workflow while the author is inactive and verify intended access. This validate 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, both the normal and repair tasks run despite a branching requirement; this choice would not establish the required conditional task branches decision.

Observe a Spark job only after the action runs. This validate action addresses a different case (spark lazy evaluation), where engineers assume an intermediate filter executed before any action. Spark transformations are lazy until an action triggers a job. In the stated problem, both the normal and repair tasks run despite a branching requirement; this choice would not establish the required conditional task branches decision.

Inject malformed and null cases and verify tests fail correctly. This validate action addresses a different case (dataframe testing), where tests only assert that the DataFrame is nonempty. DataFrame equality and schema assertions test data behavior and contracts. In the stated problem, both the normal and repair tasks run despite a branching requirement; this choice would not establish the required conditional task branches decision.

Confirm dashboard results refresh after source data updates. This validate 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, developers tune executor size without inspecting actual exchanges; this choice would not establish the required spark explain plans decision.

Confirm deployment reflects the reviewed configuration without extra changes. This validate 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, developers tune executor size without inspecting actual exchanges; this choice would not establish the required spark explain plans decision.

Replay a batch ID and confirm target rows are not duplicated. This validate action addresses a different case (streaming idempotent sink), where a retry of a completed batch produces duplicate sink rows. foreachBatch can execute a batch again and requires idempotent external writes for exactly-once effects. In the stated problem, developers tune executor size without inspecting actual exchanges; this choice would not establish the required spark explain plans decision.