Pre-Order Your Implementing Data Engineering Solutions Using Azure Databricks (beta) DP-750

The correct answer is B — Repair the current job run.

Repair Run is designed for exactly this situation: a long-running job where most tasks succeeded but the final task failed due to a transient issue. Instead of restarting the entire eight-task job from the beginning — wasting hours of compute — Repair Run re-executes only the failed task and any dependents that were skipped as a result. All tasks that completed successfully are marked done and their outputs are reused.

Option A (update job parameters) changes configuration for future runs but doesn't re-execute the failed task in the current run. Option C (Restart Job1) re-runs every task from the start — precisely what the question says to avoid. Option D (disable and re-enable the schedule) creates a brand-new run on the next schedule trigger, again starting from the beginning rather than repairing the existing run.

The correct answer is C — a Lakeflow Spark Declarative Pipelines (SDP) pipeline.

SDP is tailor-made for exactly this pattern: ingest from cloud storage, transform through cleansing and enrichment stages, and publish Delta tables to Unity Catalog. What sets it apart from the other options is built-in monitoring (the pipeline graph shows row counts, expectation metrics, and run history) and automatic retries (failed tasks retry automatically based on pipeline settings, without manual re-run triggers).

Option A (Structured Streaming job) gives you the streaming engine but nothing else — monitoring, alerting, and retry logic all have to be built from scratch. Option B (scheduled notebook job) is a batch approach that requires manual monitoring and lacks the declarative lineage tracking SDP provides. Option D (Azure Data Factory with data flows) works but adds a separate Azure service to manage, introduces ADF licensing costs, and doesn't integrate natively with Unity Catalog governance.

The correct answers are B and E.

The goal is to reduce storage costs while preserving 30 days of time travel. Two actions are needed:

Set delta.deletedFileRetentionDuration to a value aligned with the 30-day requirement (Option B). Note: the answer option states '10 days' which would be insufficient for 30-day time travel — in practice this property should be set to at least 30 days. This property defines the retention floor: VACUUM will not delete any file newer than this threshold.

Run VACUUM on Table1 (Option E). VACUUM physically removes unreferenced data files older than the retention duration from storage. Without running VACUUM, obsolete files accumulate indefinitely regardless of the retention property setting — the property tells VACUUM what to keep; VACUUM is what does the actual cleanup.

Option C (OPTIMIZE) compacts small files for better query performance but never deletes anything. Option D (logRetentionDuration) keeps the transaction log for time travel but doesn't free up the data file storage.