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  • Total Questions: 285
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Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Governance7%- Data lineage and metadata tracking
- Unity Catalog management
- Policy enforcement
Topic 2: Data Ingestion & Acquisition7%- Schema inference and evolution
- Connecting to diverse data sources
- Auto Loader and streaming ingestion
Topic 3: Data Sharing and Federation5%- Cross-workspace and cross-cloud access
- Unity Catalog data sharing
Topic 4: Data Transformation, Cleansing, and Quality10%- Data validation and quality checks
- Handling missing or inconsistent data
- Standardization and normalization
Topic 5: Developing Code for Data Processing using Python and SQL22%- Batch and incremental processing logic
- Data transformation and aggregation
- Integration with Databricks APIs and tools
Topic 6: Debugging and Deploying10%- CI/CD and DevOps practices
- Troubleshooting pipelines and errors
- Deployment using bundles, CLI, and APIs
Topic 7: Ensuring Data Security and Compliance10%- Access control and permissions
- Compliance standards implementation
- Data encryption and masking
Topic 8: Data Modelling6%- Delta Lake table design
- Medallion Architecture implementation
- Schema design and management
Topic 9: Cost & Performance Optimisation13%- Cluster configuration and scaling
- Query optimization and caching
- Storage optimization (partitioning, Z-order, indexing)
Topic 10: Monitoring and Alerting10%- Setting up alerts and notifications
- Performance and health monitoring
- Pipeline observability and logging

Databricks-Certified-Professional-Data-Engineer FAQ for Candidates Who Mean It

The Databricks-Certified-Professional-Data-Engineer exam is Databricks's formal test for the Databricks Certified Professional Data Engineer certification, a credential at the Professional level. It verifies that you can apply the Databricks Certified Professional Data Engineer objectives in realistic, scenario-driven situations — the difference between having read about the work and being able to do it. It also connects to a broader certification path covering Databricks Certified Associate Data Engineer, so the study effort compounds beyond a single exam. That is why preparing through realistic questions beats re-reading chapters every time.

The Databricks Certified Professional Data Engineer blueprint spans 10 domains, led by Data Ingestion & Acquisition (7%), Data Transformation, Cleansing, and Quality (10%), and Cost & Performance Optimisation (13%). Treat the percentages as a study budget: the heavier the domain, the larger its share of your week. The complete topic list sits in the exam topics section above — build your plan on it, not on guesswork.

The Databricks Certified Professional Data Engineer exam packs 59 questions into 120 minutes. Turn that ratio into a plan before test day: minutes divided by questions gives your per-item budget, and anything consuming double its budget gets marked and revisited at the end. Practice the rhythm against a real timer — every ActualTestsIT engine session enforces one — until finishing with spare review time is your default.

Databricks Certified Professional Data Engineer requires 70% to pass, and every attempt costs USD 200 — retakes included, at full price. The smart economics are simple: drill with ActualTestsIT practice questions until your timed scores clear the mark across several consecutive sessions, then book the seat. Over-preparation is cheaper than re-registration.

Databricks lists the Databricks Certified Professional Data Engineer question formats as: Multiple Choice. Format shapes strategy — locate the actual question sentence inside long scenario stems before reading details, and double-check every selection on multi-answer items. ActualTestsIT's 285 practice questions exercise these formats so thoroughly that on exam day, only the content is unfamiliar.

You can sit the Databricks-Certified-Professional-Data-Engineer exam in English, Japanese, Portuguese (Brazil), Korean. Choose whichever language keeps your comprehension fastest under the clock — slow reading is a hidden tax on every question. Testing in English? Daily sessions with ActualTestsIT's English Databricks-Certified-Professional-Data-Engineer practice questions double as focused terminology training.

The certification earned through the Databricks Certified Professional Data Engineer exam stays valid for 2 years. Put the expiry date on your calendar the day you pass, and research the recertification path before it looms — a planned renewal is an errand, while an expired credential is a project. Databricks sets these policies, so verify the current rules on the official certification page as your window approaches.

All three versions carry the same 285 expert-written questions — you are choosing a study environment, not a content tier:

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Plenty of candidates mix formats — the PDF for reading, an engine for timed rehearsal.

Yes. ActualTestsIT's free Databricks Certified Professional Data Engineer PDF demo contains real sample questions with complete verified answers, so you can judge the quality yourself. Every purchase includes 365 days of free updates, and renewing after expiry costs 50% of the regular price from your member zone. Inspect first — material this confident has nothing to hide.

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Databricks Certified Professional Data Engineer Sample Questions:

A data engineer uses Structured Streaming to ingest transaction data from a bronze Delta table. Data-quality issues have been identified: transaction values can sometimes be negative, and those rows must be routed to a separate quarantine table. Good data has low-latency requirements because downstream systems use it, whereas bad data will be analyzed only periodically and has no production dependencies. The quarantine job must be implemented so it cannot affect the production processes that rely on good data, while minimizing the job's cost.
How should the quarantine process be implemented to meet these requirements?

  • A. The existing streaming job for the good data should be updated to incorporate the quarantining of the bad data. A new boolean column called quarantine should be added to the DataFrame, and its value should be set to true if the transaction value is less than 0 and false if the transaction value is greater than or equal to 0. Processing and storing all the data together will save costs.
  • B. The existing streaming job for the good data should be updated to incorporate the quarantining of the bad data. Inside a foreachBatch function, the DataFrame should be filtered so that records with a transaction value greater than or equal to 0 are written to the good data table and records with a transaction value less than 0 are written to a quarantine table. Try/Catch can be added around the writes in the foreachBatch function so that the stream cannot fail.
  • C. The streaming job for the good data needs to be modified to filter out records with a transaction value less than 0 before writing and should not share compute with other processes. The streaming job for the quarantine data needs to filter out records with a transaction value greater than or equal to 0 before writing and should be implemented on a separate small cluster and run only once a day to minimize cost.
  • D. The streaming job for the good data needs to be modified to filter out records with a transaction value less than 0 before writing. The streaming job for the quarantine data needs to filter out records with a transaction value greater than or equal to 0 before writing. Both should run as separate streams on the same cluster to minimize cost.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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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.

  • A. "Can Manage" privileges on the required cluster
  • B. "Can Restart" privileges on the required cluster
  • C. Cluster creation allowed. "Can Attach To" privileges on the required cluster
  • D. Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster
  • E. Cluster creation allowed. "Can Restart" privileges on the required cluster
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Explanation: Only visible for ActualTestsIT members. You can sign-up / login (it's free).

A query is taking too long to run. After investigating the Spark UI, the data engineer discovered a significant amount of disk spill. The compute instance being used has a core-to-memory ratio of 1:2.
What are the two steps the data engineer should take to minimize spillage? (Choose 2 answers)

  • A. Choose a compute instance with a higher core-to-memory ratio.
  • B. Choose a compute instance with more disk space.
  • C. Increase spark.sql.files.maxPartitionBytes.
  • D. Choose a compute instance with more network bandwidth.
  • E. Reduce spark.sql.files.maxPartitionBytes.
Reveal Solution  Discussion  0

Correct Answer: A,E  🗳️

Explanation: Only visible for ActualTestsIT members. You can sign-up / login (it's free).

Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?

  • A. libraries
  • B. fs
  • C. jobs
  • D. configure
  • E. workspace
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Explanation: Only visible for ActualTestsIT members. You can sign-up / login (it's free).

A data engineer is performing a join operating to combine values from a static userlookup table with a streaming DataFrame streamingDF.
Which code block attempts to perform an invalid stream-static join?

  • A. userLookup.join(streamingDF, ["user_id"], how="right")
  • B. streamingDF.join(userLookup, ["userid"], how="inner")
  • C. streamingDF.join(userLookup, ["user_id"], how="outer")
  • D. userLookup.join(streamingDF, ["userid"], how="inner")
  • E. streamingDF.join(userLookup, ["user_id"], how="left")
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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