DP-750 Practice Exam Questions

The right ingestion tool depends on the source characteristics:

File-based telemetry and maintenance data → Auto Loader (cloudFiles). It monitors ADLS Gen2 for new arrivals, handles schema inference and evolution for the frequent schema drift, and requires minimal operational effort.

Real-time telemetry from Event Hubs → Spark Structured Streaming with the azure-eventhubs-spark connector. This provides exactly-once semantics and checkpoint-based recovery, satisfying ' resume processing after failures without reprocessing. '

Structured maintenance data from PostgreSQL → JDBC connector. Databricks supports direct JDBC reads from relational databases with pushdown predicates.

Daily CSV ERP extracts (50–100 GB) → COPY INTO or Auto Loader. Both support idempotent incremental batch loading into Delta tables with minimal code.

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 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.