Microsoft DP-750 Exam Dumps

The Databricks CLI contains the bundle command group for validating, deploying, and running Declarative Automation Bundles. CLI commands can be executed consistently from developer terminals or CI/CD pipelines, making deployments automated, repeatable, and suitable for multiple target environments. The Databricks SDK for Python can manage workspace APIs programmatically, but it is not the standard bundle deployment interface required here. Git folders provide source-control integration inside the workspace but do not deploy bundle-defined resources. The Jobs UI supports interactive creation and management of jobs, which introduces manual steps and does not provide the same infrastructure-as-code workflow. Using commands such as databricks bundle validate and databricks bundle deploy -t dev directly satisfies the automated development deployment requirement. Microsoft Learn

The correct answer is C --- Enable schema evolution.

When new columns are added to the source data, a pipeline without schema evolution treats the unexpected columns as a schema mismatch and fails the write. Schema evolution, when enabled in an SDP pipeline, automatically adds those new columns to the target Delta table on the next pipeline run. The pipeline continues without intervention, and no historical data is lost.

Option A (disable schema enforcement) is the wrong lever --- it removes all schema validation, which could allow corrupt or mistyped data into Table1. Schema evolution is a targeted, safer response.

Option B (row filters to exclude records with new columns) would silently discard valid records just because they carry extra fields --- that's data loss. Option D (separate table per schema version) creates an explosion of tables as schemas evolve and makes downstream analytics significantly more complex. Schema evolution is the clean, built-in solution.

The correct answer is B --- define a pipeline expectation.

SDP expectations with @dlt.expect_or_drop are built precisely for this scenario: the pipeline keeps running, bad records are excluded from Table1, and those records are automatically captured in the pipeline's event log as expectation violations --- available for review without any extra code.

Option A (custom quarantine logic) would work but requires writing and maintaining additional pipeline tables and routing logic. The whole point of SDP expectations is to handle this pattern declaratively, with far less code.

Option C (WHERE clauses in downstream queries) is a read-time filter, not a write-time guard. Invalid records would still land in Table1 and would simply be hidden from downstream views --- they're not preserved for review in any structured way. Option D (check constraint on Table1) would throw an exception on write and halt the pipeline, violating the 'pipeline continues to run' requirement.

The OPTIMIZE command performs bin-packing compaction, combining the Customer table's existing small Parquet files into fewer, larger files. This reduces file-open overhead and improves data-skipping efficiency without changing the logical table contents. Enabling optimized writes affects future write operations but does not compact the small files already stored. VACUUM removes unreferenced data files that are older than the configured retention threshold; it does not reorganize active small files and can restrict time-travel availability. Reducing delta.deletedFileRetentionDuration changes how long obsolete files remain available and can directly reduce the time-travel window, contrary to the requirement. OPTIMIZE is therefore the correct operation for improving the physical layout of current data while preserving Delta table semantics and history. Microsoft Learn

The correct answer is D --- apply the main branch changes to Branch1 and resolve the conflicts.

When a merge fails due to conflicts, the right workflow is to bring main's changes into the feature branch, resolve conflicts there, and then merge the clean feature branch into main. This is the standard Git conflict resolution pattern --- resolve in the feature branch, not in main --- because it protects the main branch from partial or broken states during resolution.

Option A (clone Branch1 as a new repository) creates a disconnected copy; it doesn't resolve the conflict and breaks the relationship with the remote. Option B (apply changes directly to main) bypasses the feature branch entirely and risks overwriting the other developer's work. Option C (clone main as a new repository) again creates a disconnected copy --- none of Branch1's changes would be incorporated, and history would be lost.