Important Databricks Certified Data Engineer Professional Exam Questions

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Databricks Certified Data Engineer Professional Exam

Attempt the Data Engineer Professional practice test and solve real exam-like Databricks Certified Data Engineer Professional questions to prepare efficiently and increase your chances of success. Our Databricks Certified Data Engineer Professional practice questions match the actual Databricks Certified Data Engineer Professional exam format, helping you enhance confidence and improve performance. With our Databricks Certified Data Engineer Professional practice exam software, you can analyze your performance, identify weak areas, and work on them effectively to boost your final Data Engineer Professional exam score.

Vendor:	Databricks
Exam Name:	Databricks Certified Data Engineer Professional
Registration Code:	Databricks-Certified-Professional-Data-Engineer
Related Certification:	Databricks Data Engineer Professional Certification
Exam Audience:	Data Engineers, big data professionals,

Total Questions

215

Last Updated

24-08-2026

Exam Duration

120 MINUTES

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Question: 1

A junior developer complains that the code in their notebook isn't producing the correct results in the development environment. A shared screenshot reveals that while they're using a notebook versioned with Databricks Repos, they're using a personal branch that contains old logic. The desired branch named dev-2.3.9 is not available from the branch selection dropdown.

Which approach will allow this developer to review the current logic for this notebook?

AUse Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9

BUse Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.

CUse Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch

DMerge all changes back to the main branch in the remote Git repository and clone the repo again

EUse Repos to merge the current branch and the dev-2.3.9 branch, then make a pull request to sync with the remote repository

Answer: B

Explanation:

This is the correct answer because it will allow the developer to update their local repository with the latest changes from the remote repository and switch to the desired branch. Pulling changes will not affect the current branch or create any conflicts, as it will only fetch the changes and not merge them. Selecting the dev-2.3.9 branch from the dropdown will checkout that branch and display its contents in the notebook. Verified Reference: [Databricks Certified Data Engineer Professional], under ''Databricks Tooling'' section; Databricks Documentation, under ''Pull changes from a remote repository'' section.

Question: 2

A data team is implementing an append-only Delta Lake pipeline that processes both batch and streaming data. They want to ensure that schema changes in the source data are automatically incorporated without breaking the pipeline.

Which configuration should the team use when writing data to the Delta table?

AignoreChanges = false

BmergeSchema = true

CoverwriteSchema = true

DvalidateSchema = false

Answer: B

Explanation:

When writing data to Delta Lake tables, the option mergeSchema = true allows automatic evolution of the table schema by merging new columns or data types introduced in the incoming data with the existing table definition. This ensures pipelines remain resilient to upstream schema changes, especially in append-only or streaming ingestion scenarios. The Databricks documentation specifies that this option should be enabled for schema-on-write flexibility, while overwriteSchema replaces the existing schema entirely and ignoreChanges applies only to CDC operations. validateSchema ensures consistency and does not handle evolution. Therefore, B (mergeSchema = true) is the correct configuration for handling automatic schema evolution safely.

Question: 3

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?

AUse a service principal with OAuth token federation.

BUse a service principal ID and its OAuth client secret.

CUse a service principal and its Personal Access Token.

DUse a shared user account and its OAuth client secret.

Answer: A

Explanation:

The most secure and enterprise-recommended authentication method for Databricks automation is OAuth token federation with service principals.

This configuration allows service principals (non-human identities) to authenticate using temporary OAuth access tokens from a trusted identity provider (such as Azure AD or AWS IAM federation). These tokens are short-lived and scoped, significantly reducing credential exposure risks.

By contrast, static client secrets (B) or PATs (C) are long-lived and require periodic manual rotation, increasing security vulnerability. Shared user accounts (D) violate least-privilege and auditability principles. Therefore, A provides the strongest, most compliant authentication model for automated CLI and CI/CD workflows.

Question: 4

A data engineer wants to automate job monitoring and recovery in Databricks using the Jobs API. They need to list all jobs, identify a failed job, and rerun it.

Which sequence of API actions should the data engineer perform?

AUse the jobs/list endpoint to list jobs, check job run statuses with jobs/runs/list, and rerun a failed job using jobs/run-now.

BUse the jobs/get endpoint to retrieve job details, then use jobs/update to rerun failed jobs.

CUse the jobs/list endpoint to list jobs, then use the jobs/create endpoint to create a new job, and run the new job using jobs/run-now.

DUse the jobs/cancel endpoint to remove failed jobs, then recreate them with jobs/create and run the new ones.

Answer: A

Explanation:

The Databricks Jobs REST API provides several endpoints for automation. The correct monitoring and rerun flow uses three specific calls:

GET /api/2.1/jobs/list -- Lists all available jobs within the workspace.

GET /api/2.1/jobs/runs/list -- Returns all runs for a specific job, including their current state (e.g., TERMINATED: FAILED).

POST /api/2.1/jobs/run-now -- Immediately triggers a rerun of the specified job.

This sequence aligns with Databricks' prescribed automation model for job observability and recovery. Using jobs/update modifies metadata but does not rerun jobs, and jobs/create is only used for creating new jobs, not rerunning failed ones. Cancelling and recreating jobs introduces unnecessary duplication. Therefore, option A is the correct automated recovery workflow.

Question: 5

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(name='trips_data_quarantine')def trips_data_quarantine():return (spark.readStream.table('raw_trips_data').filter(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@dlt.expect_all_or_drop(rules)def trips_data_quarantine():return spark.readStream.table('raw_trips_data')

D@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)))

Answer: A

Explanation:

The requirement is that both valid (good) and invalid (bad) records must be captured and available separately for downstream processing. Invalid records should not simply be dropped; they must be quarantined in a dedicated table.

In Databricks Lakeflow Declarative Pipelines (DLT), this is achieved by creating separate output tables:

One table for valid records (Silver table) that pass the expectations.

Another quarantine table that explicitly captures records failing the expectations.

Option A correctly implements this by:

Declaring a DLT table trips_data_quarantine.

Using .filter(expr(quarantine_rules)) to isolate invalid records (records where latitude or longitude is NULL).

This ensures analysts can query both good records (from the main Silver pipeline table) and bad records (from the quarantine table).

Why not the others?

B: Uses @dlt.expect_or_drop, which drops invalid records instead of quarantining them. This violates the requirement that quarantined data should be available.

C: Same as B, but applies expectations in bulk with expect_all_or_drop. Again, bad data is dropped, not quarantined.

D: Adds an is_quarantined flag in the same table. While it marks bad records, it does not separate them into a distinct quarantine table as required by the business use case.

Therefore, Option A is the only solution aligned with Databricks documentation for quarantining invalid data into a dedicated table while keeping valid data in the main pipeline.

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