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Dense paragraphs put you to sleep; well-built questions wake you up. ActualTestsIT's senior industry experts wrote Databricks Certified Professional Data Engineer practice questions so that every Databricks-Certified-Professional-Data-Engineer objective is learned by doing, not skimming.
Databricks Databricks-Certified-Professional-Data-Engineer Exam Overview:
| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Professional Data Engineer Exam |
| Exam Number: | Databricks-Certified-Professional-Data-Engineer |
| Real Exam Qty: | 59 |
| Exam Price: | USD 200 |
| Exam Format: | Multiple Choice |
| Passing Score: | 70% |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 120 minutes |
| Available Languages: | Japanese, Portuguese (Brazil), English, Korean |
| Related Certifications: | Databricks Certified Associate Data Engineer |
| Recommended Training: | Databricks Data Engineer Professional Training |
| Exam Registration: | Databricks Official Certification Registration |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online proctored or onsite test center |
| Pre Condition: | No mandatory prerequisites; 1+ year hands-on experience and related training highly recommended |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/data-engineer-professional |
Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Modelling | 6% | - Medallion Architecture implementation - Delta Lake table design - Schema design and management |
| Ensuring Data Security and Compliance | 10% | - Compliance standards implementation - Data encryption and masking - Access control and permissions |
| Data Transformation, Cleansing, and Quality | 10% | - Handling missing or inconsistent data - Standardization and normalization - Data validation and quality checks |
| Cost & Performance Optimisation | 13% | - Storage optimization (partitioning, Z-order, indexing) - Query optimization and caching - Cluster configuration and scaling |
| Developing Code for Data Processing using Python and SQL | 22% | - Integration with Databricks APIs and tools - Batch and incremental processing logic - Data transformation and aggregation |
| Data Governance | 7% | - Policy enforcement - Data lineage and metadata tracking - Unity Catalog management |
| Data Sharing and Federation | 5% | - Cross-workspace and cross-cloud access - Unity Catalog data sharing |
| Data Ingestion & Acquisition | 7% | - Connecting to diverse data sources - Auto Loader and streaming ingestion - Schema inference and evolution |
| Monitoring and Alerting | 10% | - Pipeline observability and logging - Setting up alerts and notifications - Performance and health monitoring |
| Debugging and Deploying | 10% | - CI/CD and DevOps practices - Troubleshooting pipelines and errors - Deployment using bundles, CLI, and APIs |
Databricks Certified Professional Data Engineer Questions, Answered by Experts
In plain terms, the Databricks-Certified-Professional-Data-Engineer exam is Databricks's way of verifying that you deserve the Databricks Certified Professional Data Engineer certification, a credential at the Professional level. It tests the Databricks Certified Professional Data Engineer objectives through applied, scenario-driven questions — the kind that expose the gap between "read about it" and "can do it." It also sits within a broader certification ecosystem that includes Databricks Certified Associate Data Engineer, so the effort compounds across your career. That's why a review system built around realistic questions — practice, error correction, self-improvement — beats textbook re-reading every time.
The Databricks Certified Professional Data Engineer exam blueprint spans 10 domains, led by Ensuring Data Security and Compliance (10%), Data Governance (7%), and Monitoring and Alerting (10%). Treat the weightings as your study budget: domains carrying more of the score deserve more of your week. The full topic list is in the exam topics section above — plan against it, not against guesswork.
The Databricks Certified Professional Data Engineer exam allows 120 minutes for 59 questions. Before test day, turn that into a rhythm: minutes divided by questions equals your per-item budget, and any item burning double its budget gets flagged and revisited at the end. Rehearse the rhythm under a real timer — every ActualTestsIT test engine session runs one — and finishing with review time becomes your norm, not your hope.
Passing the Databricks Certified Professional Data Engineer exam takes 70%, and each attempt costs USD 200 — including every retake, at full price. The financially sound strategy is arriving over-prepared: drill with ActualTestsIT practice questions until your timed scores clear the passing mark across several consecutive sessions, then register. You're not being cautious; you're declining to pay for the same exam twice.
Databricks lists the Databricks Certified Professional Data Engineer formats as: Multiple Choice. Formats aren't trivia — they change how you should read. For long scenario stems, locate the actual question sentence first; for multi-answer items, verify every option you select. ActualTestsIT's 217 practice questions cover these formats so thoroughly that on exam day, only the content is new.
You can take the Databricks-Certified-Professional-Data-Engineer exam in English, Japanese, Portuguese (Brazil), Korean. Choose the language where your comprehension is fastest under time pressure — in a timed exam, slow reading is a hidden tax on every question. Testing in English? Daily work with ActualTestsIT's English Databricks-Certified-Professional-Data-Engineer practice questions doubles as targeted vocabulary training.
The certification earned via the Databricks Certified Professional Data Engineer exam stays valid for 2 years. Mark the expiry date early and research the recertification path before it looms — a planned renewal is an errand, an expired credential is a project. Since Databricks sets and updates these policies, confirm the current requirements on the official certification page when your window approaches.
Every version contains the same 217 expert-written questions — you're choosing a study environment:
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Most candidates end up combining formats — PDF for reading, an engine for timed rehearsal.
Yes. ActualTestsIT's free Databricks Certified Professional Data Engineer PDF demo contains real sample questions with verified answers, so you can evaluate the quality before paying. Every purchase includes 365 days of free updates, and renewing after that costs 50% of the regular price from your member zone. Test-drive first — confident material has nothing to hide.
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Stop wrestling the textbook and start training for the exam: download the free Databricks-Certified-Professional-Data-Engineer demo, and let ActualTestsIT's 217 practice questions turn your Databricks Certified Professional Data Engineer preparation into a system that works.
Databricks Certified Professional Data Engineer Sample Questions:
A data pipeline uses Structured Streaming to ingest data from kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka_generated timesamp, key, and value. Three months after the pipeline is deployed the data engineering team has noticed some latency issued during certain times of the day.
A senior data engineer updates the Delta Table ' s schema and ingestion logic to include the current timestamp (as recoded by Apache Spark) as well the Kafka topic and partition. The team plans to use the additional metadata fields to diagnose the transient processing delays:
Which limitation will the team face while diagnosing this problem?
- A. Updating the table schema will invalidate the Delta transaction log metadata.
- B. Updating the table schema requires a default value provided for each file added.
- C. Spark cannot capture the topic partition fields from the kafka source.
- D. New fields not be computed for historic records.
Correct Answer: D 🗳️
Explanation: Only visible for ActualTestsIT members. You can sign-up / login (it's free).
The security team is exploring whether or not the Databricks secrets module can be leveraged for connecting to an external database.
After testing the code with all Python variables being defined with strings, they upload the password to the secrets module and configure the correct permissions for the currently active user. They then modify their code to the following (leaving all other variables unchanged).
Which statement describes what will happen when the above code is executed?
- A. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the encoded password will be saved to DBFS.
- B. The connection to the external table will succeed; the string " redacted " will be printed.
- C. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the password will be printed in plain text.
- D. The connection to the external table will succeed; the string value of password will be printed in plain text.
- E. The connection to the external table will fail; the string " redacted " will be printed.
Correct Answer: B 🗳️
Explanation: Only visible for ActualTestsIT members. You can sign-up / login (it's free).
A data engineering team is setting up deployment automation. To deploy workspace assets remotely using the Databricks CLI command, they must configure it with proper authentication.
Which authentication approach will provide the highest level of security ?
- A. Use a service principal and its Personal Access Token.
- B. Use a service principal ID and its OAuth client secret.
- C. Use a service principal with OAuth token federation.
- D. Use a shared user account and its OAuth client secret.
Correct Answer: C 🗳️
Explanation: Only visible for ActualTestsIT members. You can sign-up / login (it's free).
A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?
- A. Credential validation errors while pulling data from an external system.
- B. Task queueing resulting from improper thread pool assignment.
- C. Spill resulting from attached volume storage being too small.
- D. Skew caused by more data being assigned to a subset of spark-partitions.
- E. Network latency due to some cluster nodes being in different regions from the source data
Correct Answer: D 🗳️
Explanation: Only visible for ActualTestsIT members. You can sign-up / login (it's free).
A data engineer is creating a data ingestion pipeline to understand where customers are taking their rented bicycles during use. The engineer noticed that, over time, data being transmitted from the bicycle sensors fail to include key details like latitude and longitude. Downstream analysts need both the clean records and the quarantined records available for separate processing.
The data engineer already has this code:
import dlt
from pyspark.sql.functions import expr
rules = {
" valid_lat " : " (lat IS NOT NULL) " ,
" valid_long " : " (long IS NOT NULL) "
}
quarantine_rules = " NOT({}) " .format( " AND " .join(rules.values()))
@dlt.view
def raw_trips_data():
return spark.readStream.table( " ride_and_go.telemetry.trips " )
How should the data engineer meet the requirements to capture good and bad data?
- A. @dlt.table(partition_cols=[ " is_quarantined " , ])
@dlt.expect_all(rules)
def trips_data_quarantine():
return (
spark.readStream.table( " raw_trips_data " )
.withColumn( " is_quarantined " , expr(quarantine_rules))
) - B. @dlt.view
@dlt.expect_or_drop( " lat_long_present " , " (lat IS NOT NULL AND long IS NOT NULL) " ) def trips_data_quarantine():
return spark.readStream.table( " ride_and_go.telemetry.trips " ) - C. @dlt.table(name= " trips_data_quarantine " )
def trips_data_quarantine():
return (
spark.readStream.table( " raw_trips_data " )
.filter(expr(quarantine_rules))
) - D. @dlt.table
@dlt.expect_all_or_drop(rules)
def trips_data_quarantine():
return spark.readStream.table( " raw_trips_data " )
Correct Answer: C 🗳️
Explanation: Only visible for ActualTestsIT members. You can sign-up / login (it's free).
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