Databricks Data Engineer Professional Exam Practice Questions, practice question pack
DATABRICKS · Data Engineer Professional

Databricks Data Engineer Professional Exam Practice Questions

339 questionsPDF by emailUpdated September 2026

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Three of the 339 questions in this pack

Question 1

An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code:

df = spark.read.format("parquet").load(f"/mnt/source/(date)")

Which code block should be used to create the date Python variable used in the above code block?

  1. date = spark.conf.get("date")
  2. input_dict = input() date= input_dict["date"]
  3. import sys date = sys.argv[1]
  4. date = dbutils.notebooks.getParam("date")
  5. dbutils.widgets.text("date", "null") date = dbutils.widgets.get("date")
Show answer and explanation

Correct answer: E. dbutils.widgets.text("date", "null") date = dbutils.widgets.get("date")

dbutils.widgets.get("date") When a job passes parameters to a notebook via the Databricks Jobs API, the notebook must use dbutils.widgets to retrieve them. The dbutils.widgets.text() call creates a widget with a default value, and dbutils.widgets.get() retrieves the parameter value passed by the upstream system. This is the standard Databricks pattern for job parameters in notebooks.

Why the other options are wrong

  • A. spark.conf.get() retrieves Spark configuration values, not job parameters passed by the Jobs API.
  • B. input() is for interactive user input and cannot access job parameters from the API.
  • C. sys.argv accesses command-line arguments, which are not used by the Jobs API for parameter passing in Databricks.
  • D. dbutils.notebooks.getParam() is used to pass parameters between notebooks, not from the Jobs API to a notebook.

Question 2

The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity. Each user should be able to execute workloads against their assigned clusters at any time of the day.

Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.

  1. "Can Manage" privileges on the required cluster
  2. Workspace Admin privileges, cluster creation allowed, "Can Attach To" privileges on the required cluster
  3. Cluster creation allowed, "Can Attach To" privileges on the required cluster
  4. "Can Restart" privileges on the required cluster
  5. Cluster creation allowed, "Can Restart" privileges on the required cluster
Show answer and explanation

Correct answer: D. "Can Restart" privileges on the required cluster

A user needs minimal permissions to start and attach to an already-configured cluster. The 'Can Restart' privilege allows a user to restart a terminated cluster and attach to it, which is exactly what's needed when a cluster auto-terminates after 30 minutes of inactivity. This permission is sufficient for the stated use case without requiring cluster creation or management capabilities.

Why the other options are wrong

  • A. 'Can Manage' privileges grant far more access than needed and violate the principle of least privilege.
  • B. Workspace Admin privileges and cluster creation allowed are excessive for simply attaching to an existing cluster.
  • C. Cluster creation allowed is unnecessary since the cluster is already configured; only restart and attach capabilities are needed.
  • E. Cluster creation allowed is not required when the cluster already exists and is pre- configured.

Question 3

When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

  1. Cluster: New Job Cluster; Retries: Unlimited; Maximum Concurrent Runs: Unlimited
  2. Cluster: New Job Cluster; Retries: None; Maximum Concurrent Runs: 1
  3. Cluster: Existing All-Purpose Cluster; Retries: Unlimited; Maximum Concurrent Runs: 1
  4. Cluster: New Job Cluster; Retries: Unlimited; Maximum Concurrent Runs: 1
  5. Cluster: Existing All-Purpose Cluster; Retries: None; Maximum Concurrent Runs: 1
Show answer and explanation

Correct answer: D. Cluster: New Job Cluster; Retries: Unlimited; Maximum Concurrent Runs: 1

Maximum Concurrent Runs: 1 For production Structured Streaming jobs, using a new job cluster with unlimited retries and a maximum of 1 concurrent run is optimal. The new job cluster is automatically created and terminated with each run, minimizing costs. Unlimited retries ensure recovery from transient failures, and limiting to 1 concurrent run prevents duplicate processing and ensures exactly-once semantics.

Why the other options are wrong

  • A. Unlimited concurrent runs can cause duplicate processing and higher costs without benefit.
  • B. No retries means the job fails permanently on any failure, which is unreliable for production.
  • C. All-purpose clusters continue running between jobs, consuming resources and costs even when idle.
  • E. All-purpose clusters continue running between jobs wasting resources, and no retries leaves the job vulnerable to transient failures.

See all 10 free questions Get the full pack, US$39

339 practice questions for Databricks Certified Data Engineer Professional, with full explanations.

Every question comes with the correct answer, the reasoning behind it, and a short note on why each wrong option is wrong. Work through it once with the answers, then again with the questions-only copy under exam conditions.

  • 339 questions mapped to the current Data Engineer Professional exam guide
  • Answers and explanations for every question, including the wrong options
  • A questions-only PDF for timed practice runs
  • Instant delivery by email the moment you check out
  • Free monthly updates for as long as the exam is live
  • Pass or your money back

A Data Engineer Professional attempt costs US$200. This pack is US$39, paid once.

Try 10 questions free before you buy.

Last updated September 2026 · 339 questions

What makes the Data Engineer Professional exam hard

Data Engineer Professional is a different exam from the Associate: around 60 questions in 120 minutes, almost all of them code, and the code is usually correct Spark that does the wrong thing. It is the Databricks exam with the lowest pass rate, because it tests the internals people skip when a pipeline already works, such as how Delta handles concurrent writes, what Structured Streaming does with a watermark, why a MERGE rewrote every file, and which Unity Catalog object a permission actually sits on.

Data processing is the heart of the exam: Structured Streaming end to end, triggers, checkpoints, watermarks, stream-static and stream-stream joins, Auto Loader at scale, Change Data Feed, slowly changing dimensions with MERGE, partitioning, liquid clustering, Z-ordering and OPTIMIZE, deletion vectors and the Delta transaction log.

Databricks tooling, data modelling, security and governance with Unity Catalog, monitoring and logging, and testing and deployment round out the guide. Databricks publishes and revises the weightings in the current exam guide, and the code in the questions follows the current runtime, so it pays to check the guide before booking.

About the exam

The Databricks Certified Data Engineer Professional validates advanced data engineering on the Databricks Data Intelligence Platform: Databricks tooling, data processing with Spark and Delta Lake, data modelling, security and governance, monitoring and logging, and testing and deployment. There are no formal prerequisites; Databricks recommends the Data Engineer Associate and at least a year of hands-on experience.

Exam topics

  • Databricks tooling: Lakeflow Jobs, Declarative Pipelines, API, CLI and Asset Bundles
  • Data processing: Structured Streaming, Delta Lake internals, incremental and CDC patterns
  • Data modelling and the medallion architecture at scale
  • Security and governance with Unity Catalog
  • Monitoring, logging and performance diagnosis
  • Testing and deployment

Databricks publishes section weightings in the current exam guide. About 60 scored multiple choice questions, 120 minutes, passing score not published by Databricks (commonly cited as 70%), US$200 per attempt, online proctored or test centre, certification valid for two years.

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Questions before you buy

What do I get when I buy the Databricks Data Engineer Professional pack?

339 practice questions as a PDF, each with the correct answer, a full explanation and a note on why the other options are wrong, plus a separate questions-only PDF for timed practice.

How quickly do I receive it?

Your PDF is prepared and sent to your email address after checkout, and you get a confirmation as soon as it is on its way.

Is there a free sample?

Yes. Ten questions from this pack, with answers and explanations, are free on this page and as a PDF, so you can judge the quality before you pay.

Are updates included?

Yes. The pack is updated every month for as long as the exam is live, and updates are free for everyone who has bought it.

What if I fail the exam?

We refund the pack. Sit the exam 7 to 30 days after buying, then send your official score report within 7 days of the exam date, as set out in the refund policy.

Can I share it with colleagues?

Each purchase is licensed to one person. For a team, school or training organisation, email support@certstash.com for a licence that fits.

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