Score Higher on Verified DP-750 | Implementing Data Engineering Solutions Using Azure Databricks Exam Questions with Answers

The correct answer is D — a NOT NULL constraint on transaction_id and a CHECK constraint on amount.

Delta Lake table constraints are enforced at write time by the Delta engine itself. A NOT NULL constraint rejects any INSERT or UPDATE that would place a null in transaction_id. A CHECK constraint with amount > 0 rejects any row where amount is zero or negative. Combined, they implement exactly the stated quality rules: bad rows are rejected when data is written, not filtered away at read time.

Options A and C (SELECT with WHERE / views) are read-time constructs — they don ' t prevent invalid data from entering the table. A clever pipeline bypass could write directly to the table and skip the view entirely. Option B (row-level security with WHERE conditions) is an access-control feature for restricting which rows users see, not for enforcing data quality on writes. Table constraints are the only mechanism that genuinely blocks bad data at the storage layer.

The correct answer is B — configure a checkpoint location.

A checkpoint is the Structured Streaming mechanism for fault tolerance. Databricks writes the committed offset (i.e., how far through the source stream the job has successfully read and processed) to a durable path in ADLS Gen2 or DBFS after each micro-batch. When the cluster restarts, the engine reads that offset and resumes from the next unprocessed record — nothing is reprocessed, nothing is skipped.

Option A (increase trigger interval) affects how frequently micro-batches run but does nothing to record progress between runs. Option C (watermark) handles late-arriving events in event-time windows but doesn ' t control source offset tracking. Option D (enable CDF on the target table) tracks changes made to a Delta table for downstream consumers — it has no bearing on the streaming job ' s own fault tolerance or offset management.

Checkpointing is a required configuration for any production streaming job. Without it, every cluster restart triggers a full replay from the source.

The correct answer is B — No.

A Databricks Access Connector is an Azure resource that provides a managed identity for Databricks to authenticate to Azure Storage services. It is an authentication bridge — nothing more. Creating one doesn ' t register any external database, doesn ' t create any Unity Catalog objects representing DB1, and doesn ' t enable querying of SQL Server data from Databricks.

An Access Connector is a prerequisite for setting up storage credentials and external locations in Unity Catalog (to access ADLS Gen2, for example), but it has no role in Lakehouse Federation or foreign catalog creation. It cannot expose an external database ' s schemas and tables in Unity Catalog alongside native objects.

The correct solution for this scenario is a foreign catalog (Q47), which requires a connection object pointing to DB1 — not an Access Connector.