Microsoft DP-750 - Implementing Data Engineering Solutions Using Azure Databricks

The solution requires spark.readStream with format( ' cloudFiles ' ) for Auto Loader, paired with .writeStream using mergeSchema=true and a checkpointLocation.

Auto Loader ' s cloudFiles source incrementally processes new JSON files without rescanning the entire directory. The mergeSchema option handles schema drift — when sensors add new fields, the target Delta table schema expands automatically instead of throwing a parse error. This directly addresses Contoso ' s requirement to ' support schema drift. '

The checkpointLocation is what gives the pipeline its resilience. Databricks writes the stream ' s committed offset and schema state to that path. If the cluster restarts, the engine reads the checkpoint and picks up exactly where it left off — no events are reprocessed, satisfying ' exactly-once semantics ' and ' resume processing after failures without reprocessing the data. '

Without a checkpoint, the stream would restart from the beginning on every cluster bounce, which is precisely the problem Contoso is trying to eliminate.

The correct answer is D. Breaking the pipeline into separate tasks for ingestion, cleansing, and curation is the foundation of well-designed Lakeflow Jobs pipelines. Each task should own one responsibility — when a task does too much, debugging a failure becomes a hunt through unrelated code, and retry logic becomes expensive because you re-execute work that already succeeded.

Contoso ' s planned changes explicitly call for ' a clear execution order and dependencies ' and ' orchestrate multi-step ingestion and transformation workflows. ' Separate tasks map directly to those goals: Lakeflow Jobs tracks each task ' s status independently, so if cleansing fails, ingestion doesn ' t re-run.

Option A bundles everything into one notebook, which means a curation bug forces a full re-ingestion. Option B copies logic three times — any future change must be applied in triplicate, which is a maintenance hazard. Option C forces everything through SQL MERGE, which is the wrong tool for raw-event ingestion and doesn ' t address cleansing or schema drift.

Production ingestion: Job compute

BI: Serverless SQL warehouse

Job compute is designed for automated production workloads executed through Lakeflow Jobs. Its lifecycle can be tied to the job run, providing workload isolation and avoiding the cost of maintaining an interactive all-purpose cluster continuously. It is therefore appropriate for scheduled ingestion and transformation processing. A serverless SQL warehouse is designed for BI and Databricks SQL workloads. It provides rapid startup, automatic infrastructure management, scaling, and optimized SQL-query execution for dashboards and reporting tools. All-purpose compute is intended primarily for interactive notebook development and exploration, while shared compute does not provide the same job-specific isolation or SQL-serving experience. Consequently, job compute should support production ingestion, and a serverless SQL warehouse should serve the BI workload.

The correct mapping is SCD Type 1 for equipment metadata and SCD Type 2 for IoT sensor ownership history.

SCD Type 1 overwrites the existing record whenever an attribute changes — no history is kept. Contoso ' s requirement for equipment metadata (name, manufacturer, model, commissioning date) states ' historical values are NOT required, ' which is the textbook definition of Type 1. A MERGE INTO with WHEN MATCHED THEN UPDATE handles this cleanly in Delta Lake.

SCD Type 2 creates a new row for each change, preserving the full history through effective-date or version columns. Contoso requires that ' analysts must track the full history of ownership ' as sensors change hands over time — that full audit trail is only possible with Type 2. Type 3 (keeping just the previous value in an extra column) would lose earlier ownership records, so it doesn ' t satisfy the ' full history ' requirement.