Microsoft Updated DP-750 Exam Questions and Answers by livia

The correct answer is B — implement checkpointing.

A checkpoint is a durable record of the streaming job ' s progress written to ADLS Gen2 or DBFS after each successfully committed micro-batch. When the job restarts after a transient failure, it reads the checkpoint to find the last committed offset and resumes from that exact point — no data is reprocessed, no data is lost.

This satisfies all three requirements directly: checkpointing enables resumption from the failure point (not from the beginning), restart is fast because there ' s no replay overhead, and costs are minimised because no compute is wasted reprocessing records already handled.

Option A (decrease retry interval) makes the job retry sooner but doesn ' t control where it resumes from. Option C (alert and manual restart) adds human latency and doesn ' t prevent reprocessing without a checkpoint. Option D (increase minimum nodes) reduces the likelihood of resource-related failures but increases cost and doesn ' t address the recovery behaviour itself.

The correct answer is C — the Databricks CLI.

Databricks Asset Bundles are deployed using the Databricks CLI (v0.205+) with the commands databricks bundle validate, databricks bundle deploy, and databricks bundle run. The CLI reads the databricks.yml file, resolves the target environment settings, and creates or updates all declared resources (jobs, pipelines, apps) in the workspace. This is the documented, supported deployment mechanism for DABs and integrates naturally into CI/CD pipelines.

Option A (Azure Developer CLI / azd) is a general Azure IaC tool with no native understanding of Databricks bundles. Option B (Git folders) syncs notebook and file content from a Git repository into a workspace — useful for code, but it doesn ' t deploy Lakeflow Jobs, SDP pipelines, or bundle configurations. Option D (Azure CLI) manages Azure infrastructure (resource groups, storage accounts, etc.) and has no native DAB deployment support.

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 A. Photon is Azure Databricks ' native vectorized query engine, written in C++, designed to accelerate data ingestion and SQL-heavy workloads significantly over the standard Spark JVM path. Enabling it on a job compute cluster directly addresses Contoso ' s requirement for ' fast and consistent performance for BI workloads ' and ' production ingestion workloads that can scale automatically during telemetry spikes. '

Photon integrates transparently — no code changes are needed — and pairs well with autoscaling job clusters to handle the bursty 40,000-sensor telemetry load.

Option B contradicts the isolation requirement: Contoso explicitly needs production and development separated, not merged onto shared compute. Option C with a fixed large node gives peak capacity at all times, driving up costs even during quiet periods. Option D disabling autoscaling is the opposite of what ' s needed — telemetry spikes require elastic scaling, not a locked node count.