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

Last checked October 22, 2026 against the official Microsoft exam page.

1. Question 1Ingest and transform data

   A data engineer must continuously ingest new JSON files that arrive in an Azure Data Lake Storage folder, with the schema evolving over time. Which Databricks feature is designed for this incremental file ingestion?

   1. AA one-time COPY INTO run each Friday
   2. BAuto Loader with schema evolution enabled
   3. CA manual notebook that lists the folder and reads everything
   4. DDelta Sharing with a read-only share
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   Correct answer: B

   Auto Loader incrementally discovers new files and can evolve the schema as new fields appear, which suits continuous ingestion. A weekly COPY INTO misses timely loads, manual full reads reprocess everything, and Delta Sharing is for sharing data with other consumers.

2. Question 2Operate and optimize data pipelines

   A scheduled Databricks job must run a notebook every night and notify the on-call engineer if it fails. Which feature provides this?

   1. AA manual run each morning by an analyst who checks the job output
   2. BA personal reminder in a shared team calendar to check the job daily
   3. CA cell that sleeps until morning, then a person reruns the notebook
   4. DDatabricks Jobs with a schedule and failure notifications
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   Correct answer: D

   Databricks Jobs runs tasks on a schedule and can send notifications on failure, which automates operations. Calendar reminders, sleeping cells and manual runs rely on people and are not reliable.

3. Question 3Manage data governance and security

   A company uses Unity Catalog and wants analysts to see only the rows for their own region in a shared table. Which feature enforces this at query time?

   1. AA separate copy of the table for each analyst
   2. BA view that the analyst can edit and reshape
   3. CRow filters applied to the table in Unity Catalog
   4. DA comment that asks analysts to filter
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   Correct answer: C

   Row filters in Unity Catalog are enforced by the governance layer for each query, so users only see permitted rows. Copies multiply data, comments are not enforcement, and editable views allow users to remove the restriction.

4. Question 4Ingest and transform data

   A streaming pipeline in Databricks must write each micro-batch exactly once to a Delta table after a failure and restart. Which mechanism provides this guarantee?

   1. ACheckpoint location for the streaming query, combined with idempotent Delta writes
   2. BWriting with no checkpoint so the job restarts from scratch on every run
   3. CStoring the offsets in a notebook widget that is reset whenever the cluster restarts
   4. DDisabling transactions in the Delta table so writes skip the commit log and apply in place
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   Correct answer: A

   A checkpoint records progress so the query resumes from the last committed batch, and Delta transactions make writes idempotent. Without a checkpoint the job reprocesses data, disabling transactions removes atomicity, and widgets do not persist offsets reliably.

5. Question 5Operate and optimize data pipelines

   A multi-task job has a transformation task that must run only after an ingestion task succeeds, and a reporting task that must run after both. Which job feature defines these dependencies?

   1. ASeparate schedules for each task at random times
   2. BAsking the engineer to start each task by hand
   3. CCopying the code of all tasks into one cell
   4. DTask dependencies in the job definition
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   Correct answer: D

   Task dependencies in a multi-task job define the order and ensure each task waits for its upstream tasks. Random schedules, merged cells and manual starts do not enforce the required order.