DP-750 Exam Dumps - Microsoft Certified: Azure Databricks Data Engineer Questions and Answers

The correct answer is D — Continuous trigger.

A Continuous trigger keeps the job running as a perpetual loop. As soon as one micro-batch or iteration completes, the next begins. New records are picked up with the shortest possible latency — as close to real-time as a Lakeflow Jobs pipeline gets.

File Arrival (Option B) is event-driven but introduces per-file trigger overhead and is best suited for file-based ingestion rather than continuous streaming workloads. Scheduled (Option C) runs at fixed clock intervals — if new data arrives between runs, it waits until the next scheduled execution. Manual (Option A) requires a human to start each run.

The question specifies serverless compute, which pairs naturally with Continuous trigger because serverless handles cluster lifecycle automatically — the job stays active without managing a persistent cluster. ' New records must be processed as soon as they become available ' is the exact use case the Continuous trigger is designed for.

COPY INTO provides a concise SQL-based mechanism for loading files from cloud storage directly into a Delta table. It requires substantially less custom code than constructing a Spark ingestion application and is suitable for a straightforward bulk load of multiple CSV files. COPY INTO is also retryable and idempotent: it tracks files already loaded into the target table and skips them during later executions, helping prevent accidental duplication. Auto Loader is optimized primarily for incremental and continuously arriving files and normally requires a streaming or triggered pipeline. Apache Spark read APIs require additional code for reading, transforming, tracking, and writing the files. Manually uploading 500 GB would be inefficient and operationally unsuitable. Therefore, COPY INTO best satisfies both implementation-speed and minimal-code requirements. Microsoft Learn

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.

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.