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Databricks Data Engineer Associate — Practice Questions and Mock Exam

Prepare for DB-DEA with original practice questions and clear answer explanations. Ask Alex, your AI tutor, when you need more detail, use your results to identify topics to review, and practice your pacing with timed mock exams.

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45Mock exam questions

90minTime limit

Checked against Databricks · August 2026 · Current exam version

Overview

About the exam

The Databricks Certified Data Engineer Associate exam validates the ability to use the Databricks Data Intelligence Platform for core data engineering tasks. It covers ETL development with PySpark and SQL, data ingestion with Auto Loader, COPY INTO, and Lakeflow Connect, pipeline orchestration with Lakeflow Jobs, and governance and security through Unity Catalog. Candidates must demonstrate proficiency in Delta Lake, CI/CD for data pipelines, and troubleshooting production workloads.

This certification is designed for data engineers with six or more months of hands-on Databricks experience working with Spark SQL, PySpark, Delta Lake, and Databricks workflows. It demonstrates the ability to build and operate reliable, production-grade data pipelines on the platform.

Try the practice preview

Try five DB-DEA questions

Try five practice questions from the app’s current Databricks Certified Data Engineer Associate question bank, with answers and explanations.

Governance and Security1 / 5

A data engineer needs to access the Databricks REST API from a notebook. Which authentication method is recommended for automated production workloads?

AlexFull explanation from Alex

OAuth M2M with service principals is Databricks' recommended authentication for automated production workloads. Service principals are non-human identities that generate short-lived OAuth tokens via the client credentials flow, eliminating risks of long-lived credentials. Distractor analysis: Cluster-scoped IAM roles control cluster resource access, not API authentication. Azure AD user tokens are tied to interactive user sessions, unsuitable for unattended automation. PATs in secret scopes are simpler but long-lived and user-bound—if the user leaves or the PAT leaks, the pipeline is compromised. Best practice: store OAuth credentials using dbutils.secrets.get(), never hardcode tokens. Ref: docs.databricks.com/en/dev-tools/auth/oauth-m2m

Sourcedocs.databricks.com

Data Transformation and Modeling2 / 5

A data engineer writes a Structured Streaming query that joins a streaming DataFrame with a static DataFrame. Which type of join is this?

AlexFull explanation from Alex

Joining a streaming DataFrame with a regular (static) DataFrame produces a stream-static join. The static side is re-read on each micro-batch trigger, so updates to the static table are picked up automatically. No watermark or state management is required. Distractor analysis: Windowed join refers to time-window-based aggregations in stream processing, not a join type classification. Stateful stream join implies stream-stream joins, which require watermarks on both sides to bound state and prevent unbounded growth. Temporal lookup join is not a recognized Spark Structured Streaming term. Stream-static joins are ideal for enriching streaming data with slowly changing dimension tables. Ref: docs.databricks.com/en/transform/join

Sourcedocs.databricks.com

Implementing CI/CD3 / 5

Which statement about Databricks Repos is correct?

AlexFull explanation from Alex

Databricks Repos (now called Git folders) integrate with Git providers (GitHub, Azure DevOps, GitLab, Bitbucket) to sync notebooks and code with remote repositories. Users can clone, branch, commit, push, and pull—enabling standard Git workflows within Databricks. Distractor analysis: Repos do NOT enforce branch protection rules—those are configured in the Git provider settings. Repos do NOT store execution results—only source files are synced; run history stays in the workspace. Repos do NOT provide a built-in CI/CD runner—CI/CD is handled externally via GitHub Actions or Azure DevOps Pipelines, triggered by pushes to the remote. Ref: docs.databricks.com/en/repos/git-operations-with-repos

Sourcedocs.databricks.com

Data Ingestion and Loading4 / 5

What is the difference between a batch read (spark.read) and a streaming read (spark.readStream) of a Delta table?

AlexFull explanation from Alex

Delta batch reads load a table snapshot, including time-travel snapshots when a version or timestamp is specified. Delta streaming reads use Structured Streaming to process the initial snapshot and future commits as micro-batches, with checkpointed progress and options such as startingVersion or startingTimestamp. If the source table schema changes after a streaming read begins, Databricks documents that the query fails and usually must be restarted for compatible schema changes.

Sourcedocs.databricks.com

Databricks Intelligence Platform5 / 5

What does the CONVERT TO DELTA command do?

AlexFull explanation from Alex

CONVERT TO DELTA performs an in-place conversion of existing Parquet or Iceberg tables to Delta Lake format. It scans the existing data files, creates a _delta_log transaction log with initial commit entries, and makes the table queryable as Delta—without rewriting any data files. Syntax: CONVERT TO DELTA parquet.`path` [PARTITIONED BY (col type)]. For Iceberg tables with Parquet files, it reads the Iceberg manifest to build the Delta log. Incorrect—it does not migrate managed Hive tables to external tables. Incorrect—it converts TO Delta, not FROM Delta. Incorrect—it does not rewrite data files; the conversion is metadata-only. Avro and ORC are not supported by CONVERT TO DELTA. Ref: docs.databricks.com/en/sql/language-manual/delta-convert-to-delta.html

Sourcedocs.databricks.com

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Question pool

322 practice questions

Use the Pass-IT question pool to practice for DB-DEA. Mock exams are set to 45 questions in 90 minutes.

Pool details: DB-DEA

Objectives in the guide33 objectives listed in the official guide

across 7 domains in the official exam guide

Pool size322 questions

= The pool size is equivalent to 7 sets of 45 questions; this does not mean that each mock exam uses a separate set.

Recorded as checked against sources322 of 322

questions recorded as having their answer, options, and explanation checked against official Databricks documentation

How questions are made →

Exam domains

What's on the exam

Data transformation and modeling is the heaviest domain at 22%, just ahead of data ingestion and loading at 21% — together, nearly half the exam centers on the bronze-to-silver-to-gold pipeline: cleaning records with PySpark and SQL, then picking a landing method among COPY INTO, Auto Loader, or Lakeflow Connect. Working with Lakeflow Jobs (16%) and governance and security (15%) follow, covering DAG-based orchestration and Unity Catalog access rules.

Platform fundamentals, CI/CD, and troubleshooting each sit at 6–10%, testing narrower operational skills — compute selection, Git-based deployment through automation bundles, and reading Spark UI stage metrics for skew or memory spill, rather than the pipeline-building work that fills the rest of the exam.

Exam blueprint: DB-DEA

Databricks Intelligence Platform6%

Map the architecture behind Databricks' Delta Lake storage layer and its Unity Catalog governance layer, then match workloads to the right compute service by weighing cost against performance limits.

≈ 5 h

Data Ingestion and Loading21%

Enable batch, streaming, and incremental data ingestion patterns using tools such as COPY INTO, Auto Loader, and Lakeflow Connect into Unity Catalog-governed tables. Also covers prioritizing between ingestion methods based on volume, frequency, and governance needs, and ingesting semi-structured and unstructured data.

≈ 17 h

Data Transformation and Modeling22%

Clean and transform data through the bronze, silver, and gold layers using PySpark and SQL, including joins, deduplication, aggregation, and column and row manipulation. Also covers basic Spark tuning parameters and applying data quality checks to Silver and Gold datasets.

≈ 18 h

Working with Lakeflow Jobs16%

Orchestrate Lakeflow Jobs with retry logic and conditional branching, wire task dependencies into a DAG, and decide whether a job should fire on a schedule or in response to new data arriving.

≈ 13 h

Implementing CI/CD10%

Branch, commit, and open pull requests through Databricks' Git integration, promote the same codebase across environments with variable overrides, and package jobs, pipelines, and other assets for deployment using automation bundles and the CLI.

≈ 8 h

Troubleshooting, Monitoring, and Optimization10%

Compare current job run times against historical baselines to catch regressions, read the Lakeflow Jobs UI for pipeline health, pinpoint bottlenecks like skew and disk spilling in the Spark UI, and diagnose cluster or library failures using both Liquid Clustering and Databricks' predictive-optimization feature.

≈ 8 h

Governance and Security15%

Distinguish Unity Catalog's managed tables from externally referenced ones, grant or deny access at the right level of the hierarchy, and mask or filter sensitive rows and columns using both group-based rules and centralized ABAC policies.

≈ 12 h

Format

Exam format and question types

The exam draws 45 scored, multiple-choice questions from a 90-minute session, with room for unscored pilot content that doesn't count toward the score. Questions are scenario-based: reading bronze tables and writing clean silver output, choosing an ingestion method for a given source, or orchestrating a Lakeflow Jobs DAG with the right trigger type.

Question types: DB-DEA

Multiple Choice100%

Select the single answer that best meets the question’s requirements.

See Databricks for official question-format information. The shares shown describe the Pass-IT practice pool; they do not establish the proportions on the official exam.

Planning

Preparing for DB-DEA

The exam is delivered online with a remote proctor or at a test center, offered in English, Japanese, Portuguese (BR), and Korean. The credential holds for two years, and recertifying means passing the current version of the exam.

Preparation and logistics: DB-DEA

Preparation

Illustrative study time50–120 h

illustrative planning range: 50 h with relevant experience to 120 h when starting out; your needs may fall outside this range

LevelAssociate

Recommended backgroundNone required. 6+ months hands-on Databricks experience recommended.

Taking and maintaining the certification

DeliveryOnline proctored or test center

Retake policyNo mandatory waiting period. Retake fee applies.

Certification validity2 years

Recertification required every 2 years by taking the current exam version.

Official guideView on provider site

Study notes

Common pitfalls

Topics to review: DB-DEA

01Gold Layer Objects
Not distinguishing streaming tables, materialized views, and regular views for the Gold layer leads to wrong answers on questions about serving BI and analytics teams.
02Broadcast Join Tuning
Not knowing how autoBroadcastJoinThreshold and the other shuffle and parallelism settings affect a join's execution plan leads to wrong answers on transformation-tuning questions.
03Asset Bundle Config
Confusing environment-specific variable overrides in Automation Bundles with a hardcoded config leads to failed dev-to-prod promotion answers on CI/CD questions.
04Cluster Startup Failures
Not knowing how to distinguish library conflicts, out-of-memory errors, and cluster startup failures from Spark UI symptoms leads to wrong answers on troubleshooting questions.
05Compute Service Choice
Picking an oversized or undersized compute option for a workload's cost model, instead of matching the cluster type to the actual use case, costs points on platform-fundamentals questions.

Practice with questions and explanations in Pass-IT →

Questions

Frequently asked questions

How long is the Databricks Certified Data Engineer Associate exam?

The Databricks Certified Data Engineer Associate exam has 45 questions and a 90-minute time limit.

Which pitfalls should I review when preparing for Databricks Certified Data Engineer Associate?

Topics to review include Gold Layer Objects, Broadcast Join Tuning, Asset Bundle Config, Cluster Startup Failures, Compute Service Choice. Work through examples to check that you understand the distinctions and can explain your answer.

How is the Databricks Data Engineer Associate exam weighted?

Data transformation and modeling is the largest area at 22%, with ingestion and loading at 21% and Lakeflow jobs at 16%. Governance and security takes 15%, while CI/CD and troubleshooting sit at 10% each and platform basics at 6%. Ingestion and transformation together are more than two fifths of the exam.

Does the Data Engineer Associate certification expire?

Yes, after two years. Databricks recertifies by exam only, so you sit the then-current version rather than collecting continuing-education credits. Since the platform itself changes quickly, the resit is usually a real study effort rather than a formality.

Do you need experience before the Data Engineer Associate exam?

No certification is required to book it. Databricks recommends six or more months of hands-on platform work, and the catalog budget is about 80 hours. The exam names specific product surfaces such as Lakeflow, so documentation reading alone leaves gaps.

What comes after the Data Engineer Associate?

The Data Engineer Professional exam is the direct step up, with a 120-hour budget and questions that expect you to write Python and SQL rather than recognise it. The Generative AI Engineer Associate is the sideways move if your pipelines are starting to feed models. Neither requires the associate certification first.

What happens if you fail the Data Engineer Associate exam?

There is no mandatory waiting period before your next attempt. Databricks leaves the pacing to you, so the practical limit is how quickly you can close the gap that failed you.

One certification, 12 months

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