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Databricks · DB-DEA · Associate

Databricks Data Engineer Associate — Practice Questions and Mock Exam

The Databricks Certified Data Engineer Associate exam validates the ability to use the Databricks Data Intelligence Platform for core data engineering tasks.

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90minPass-IT mock exam time limit

Checked against Databricks · September 2026 · Current exam version

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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

A data engineer moves a SQL-heavy nightly ETL job, with large joins and aggregations, from a job defined in the UI to a job created through the Jobs API. The UI version ran with Photon, but query plans from the API-created job clusters show no Photon operators. What should the data engineer change in the API request?

AlexFull explanation from Alex

Photon is the Databricks vectorized query engine that speeds up SQL and DataFrame workloads such as large joins, aggregations, and writes, with no code changes. In the UI, Databricks enables Photon by default for classic all-purpose compute, jobs compute, and classic pipelines through the Use Photon Acceleration checkbox. Requests to the Clusters API or Jobs API do not inherit that default, so the cluster specification has to set runtime_engine to PHOTON. Serverless compute and SQL warehouses have Photon enabled without this setting. Adaptive query execution is a separate optimizer feature that is enabled by default, and the access mode or ML runtime does not decide whether Photon runs. Exam tip: if a job behaves differently when created through an API, look for a default that the UI sets and the API does not.

Sourcedocs.databricks.com

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Conversation preview · DB-DEA

Stream-static joins

  1. You

    My streaming DataFrame joins a normal DataFrame. What type of join is that?

  2. Alex · AI tutor

    A stream-static join: one side is streaming and the other is static.

    Read the Databricks documentation ↗
  3. You

    Is it a stream-stream join because new events keep arriving?

  4. Alex · AI tutor

    No. The second input remains static. Classify the join by both input types.

    Read the Databricks documentation ↗

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DB-DEA · Question excerpt

Stream-static joins

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

Understand the correct answer.

Stream-static join

A join between a streaming DataFrame and a static DataFrame is a stream-static join.

Read the Databricks documentation ↗

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Exam domains

What you practice for DB-DEA.

Exam topics, relevant practice and sources you can check.

Choose platform resourcesSee the guide for weighting35 QuestionsPass-IT questions

Match Databricks architecture and compute to the workload.

  • Explain how the platform architecture, Delta Lake and Unity Catalog support data engineering.
  • Compare compute services by capabilities, limits and cost models and choose one for the workload.
Official Databricks objectives
Ingest and load dataSee the guide for weighting49 QuestionsPass-IT questions

Choose reliable paths from source systems to governed tables.

  • Compare batch, streaming and incremental patterns and select Auto Loader, Lakeflow Connect, partner connectors or other methods by volume, frequency, data type and governance needs.
  • Use COPY INTO to load cloud-storage files incrementally into Unity Catalog tables.
  • Configure Auto Loader schema enforcement and evolution with directory listing or file notifications.
  • Configure standard or managed Lakeflow Connect ingestion for enterprise, semi-structured and unstructured sources into governed Delta tables.
  • Use local-file imports or notebook JDBC, ODBC and REST clients to land data in storage or tables, scheduling the work with Lakeflow Jobs.
Official Databricks objectives
Transform and model dataSee the guide for weighting118 QuestionsPass-IT questions

Turn bronze data into dependable silver and gold datasets.

  • Clean nulls and standardize types with PySpark or SQL, deduplicate and aggregate data, and write silver tables.
  • Combine DataFrames with joins or unions, including broadcast and cross joins, and reshape columns, rows and arrays.
  • Adjust shuffle partitions, parallelism, memory and broadcast thresholds and measure performance again.
  • Choose and build gold tables, streaming tables, views or materialized views for analytics in Unity Catalog.
  • Apply quality checks and validation rules to silver and gold datasets.
Official Databricks objectives
Orchestrate work with Lakeflow JobsSee the guide for weighting23 QuestionsPass-IT questions

Arrange pipeline tasks and start them when their inputs are ready.

  • Build notebook, SQL, dashboard and pipeline tasks with dependencies in a directed task graph.
  • Add retries, branching and loops to control pipeline execution.
  • Configure scheduled, file-arrival or table-update triggers and choose time-based or data-driven starts according to availability and dependencies.
Official Databricks objectives
Manage development and deploymentSee the guide for weighting17 QuestionsPass-IT questions

Promote the same code through development, test and production.

  • Use Databricks Git Folders to create or switch branches, commit and push changes and open pull requests.
  • Set environment-specific bundle variables and overrides while retaining one codebase.
  • Package and deploy jobs, declarative pipelines and other assets with Declarative Automation Bundles, using Databricks CLI to validate, deploy and manage them in CI/CD.
Official Databricks objectives
Monitor and troubleshoot pipelinesSee the guide for weighting44 QuestionsPass-IT questions

Use run history and execution metrics to find operational problems.

  • Compare job duration with historical baselines and inspect statuses, task dependencies, blockers and failure rates in Lakeflow Jobs.
  • Read Spark stage metrics to identify skew, shuffling and disk spilling.
  • Explain how Liquid Clustering and predictive optimization support performance.
  • Diagnose compute startup failures, conflicting libraries and out-of-memory errors.
Official Databricks objectives
Govern tables and accessSee the guide for weighting36 QuestionsPass-IT questions

Manage table ownership and control which data each principal can see.

  • Distinguish managed and external Unity Catalog tables and perform creation, modification, deletion and conversion operations.
  • Use UI and SQL controls to grant, revoke or deny access for users, groups and service principals at the appropriate hierarchy level.
  • Apply column masking and row security by group, and explain how Unity Catalog ABAC policies centralize these controls for sensitive data.
Official Databricks objectives
Question formats you can practice
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Select one answer.

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Frequently asked questions

Your preparation

How much study time should I plan for DB-DEA?

For planning, Pass-IT estimates roughly 50–120 hours. Relevant experience may put you toward the lower end; starting from scratch may put you toward the higher end. Your needs may fall outside this range. This is not a time requirement from Databricks or a promise that you will be exam-ready within that time.

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.

Using Pass-IT

What language are Pass-IT questions in?

Questions in the app are currently in English. Translations of the sample questions on this page apply only to this preview. Check with Databricks for the languages available for the official exam.

How long does my DB-DEA access last?

Access covers this certification for 12 months. You pay once, with no automatic renewal.

When does the money-back guarantee apply?

The guarantee refunds the Pass-IT purchase price for this certification. You need eligible paid access and a Readiness Score of at least 80% from your practice results when you take the exam. This score can only reach 80% after at least 50 answered questions and is not a validated probability of passing. Take the exam during your access period and submit official proof of failing within 14 days. Guarantee terms

About the DB-DEA exam

Where can I take the DB-DEA exam?

Online proctored or test center.

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.

Does the Data Engineer Associate certification expire?

Yes, after two years from the date it is awarded. You can renew by passing the current version of the exam or the shorter renewal exam. The renewal exam has 25 scored questions and a 60-minute time limit, with online proctoring only. You are eligible for the renewal exam if your Databricks Certified Data Engineer Associate credential has expired or expires within two months.

What happens if you fail the Data Engineer Associate exam?

If you fail, you must wait 14 days before retaking the exam. The same wait applies between every further attempt. Databricks sets no limit on the number of retakes.

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Guarantee terms

The guarantee refunds the Pass-IT purchase price for this certification. You need eligible paid access and a Readiness Score of at least 80% from your practice results when you take the exam. This score can only reach 80% after at least 50 answered questions and is not a validated probability of passing. Take the exam during your access period and submit official proof of failing within 14 days.

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