Free DB-DEA Databricks Lakehouse Platform Practice Test 2026 — Databricks Data Engineer Associate Questions

Last updated: May 2026 · Aligned with the current Databricks DB-DEA exam · 18% of the exam

This free DB-DEA Databricks Lakehouse Platform practice test covers Databricks lakehouse architecture, workspace, clusters, notebooks, Databricks SQL, and platform fundamentals. Each question includes a detailed explanation with real-world Databricks lakehouse context — perfect for DB-DEA exam prep.

Key Topics in DB-DEA Databricks Lakehouse Platform

10 Free DB-DEA Databricks Lakehouse Platform Practice Questions with Answers

Each question below includes 4 answer options, the correct answer, and a detailed explanation. These are real questions from the FlashGenius DB-DEA question bank for the Databricks Lakehouse Platform domain (18% of the exam).

Sample Question 1 — Databricks Lakehouse Platform

A data engineering team has a notebook that loads raw files into Delta tables every night at 2:00 AM. No users need to interact with the cluster during the run, and the team wants to avoid paying for compute when the pipeline is idle. What is the most appropriate way to run this workload?

  1. A. Attach the notebook to an all-purpose cluster that remains available for developers
  2. B. Create a Databricks Job that runs the notebook on job compute (Correct answer)
  3. C. Run the notebook from a SQL warehouse because it starts quickly for scheduled tasks
  4. D. Store the notebook in Databricks Repos so it can execute automatically each night

Correct answer: B

Explanation: Job compute is the best fit for scheduled production workloads because it is commonly created for the job run and terminated afterward. That matches the team's need for an unattended nightly pipeline and reduced idle cost.

Sample Question 2 — Databricks Lakehouse Platform

A notebook connects to an external database using a username and password currently written directly in the code. During a security review, the team is told to remove credentials from notebooks while keeping the pipeline functional. What should the team do next?

  1. A. Move the credentials into a separate notebook and import that notebook into the pipeline
  2. B. Store the credentials in a Databricks secret and reference the secret from the notebook (Correct answer)
  3. C. Save the credentials in a workspace folder with restricted permissions
  4. D. Attach the notebook to a SQL warehouse so the credentials are no longer visible in code

Correct answer: B

Explanation: Databricks secret management is the appropriate place to store sensitive credentials. The notebook can reference the secret at runtime without embedding the username and password directly in code.

Sample Question 3 — Databricks Lakehouse Platform

A team runs many short production jobs every 15 minutes. The jobs use job compute correctly, but startup time is still affecting SLA targets. The team does not want to keep a cluster running continuously. What is the best platform feature to add?

  1. A. A cluster pool for the job compute (Correct answer)
  2. B. An always-on all-purpose cluster
  3. C. A SQL warehouse for the ETL notebooks
  4. D. Manual notebook execution from the workspace

Correct answer: A

Explanation: Cluster pools can reduce startup latency by reusing pre-provisioned instances. That helps recurring short-lived jobs start faster while still allowing the team to use job compute rather than keeping a cluster running all day.

Sample Question 4 — Databricks Lakehouse Platform

A data engineer can open a notebook and attach compute successfully, but a query against a governed table fails with an access error. The workspace itself is available, and the compute is running normally. Which area should the administrator review first?

  1. A. Databricks Repos permissions
  2. B. Unity Catalog permissions (Correct answer)
  3. C. Cluster pool configuration
  4. D. Job schedule settings

Correct answer: B

Explanation: Unity Catalog provides centralized governance for data assets and permissions. If the user can access the workspace and compute but cannot query a governed table, reviewing Unity Catalog permissions is the most appropriate first step.

Sample Question 5 — Databricks Lakehouse Platform

A production pipeline runs every hour and usually finishes in 10 minutes, but input size varies significantly from one run to the next. The team needs unattended scheduling, fast startup, automatic scale changes during heavy runs, and minimal cost when the pipeline is idle. Which setup best fits these requirements?

  1. A. A Databricks Job using job compute configured with autoscaling and backed by a cluster pool (Correct answer)
  2. B. An all-purpose cluster kept running all day so each hourly run starts immediately
  3. C. A SQL warehouse that executes the pipeline because SQL endpoints scale automatically
  4. D. A fixed-size job cluster without autoscaling or a pool

Correct answer: A

Explanation: The scenario combines several requirements: scheduled production execution, low startup latency, variable workload size, and low idle cost. A Databricks Job on job compute addresses scheduled unattended runs, autoscaling handles changing workload demand, and a cluster pool reduces startup latency without requiring always-on compute.

Sample Question 6 — Databricks Lakehouse Platform

A BI team needs to run dashboard queries against curated tables in Databricks throughout the day. They only need SQL access and do not run notebook-based ETL code. Which Databricks resource is the best fit for this workload?

  1. A. An all-purpose compute cluster
  2. B. A SQL warehouse (Correct answer)
  3. C. Job compute
  4. D. A Databricks Repo

Correct answer: B

Explanation: A SQL warehouse is the best fit because it is optimized for SQL analytics and BI-style querying. The scenario is specifically about dashboard queries and SQL-only access, not general Spark engineering tasks or interactive development. An all-purpose cluster is intended for interactive analysis and development, job compute is for automated production workloads, and Repos are for Git-based version control rather than query execution.

Sample Question 7 — Databricks Lakehouse Platform

A data engineering team has a pipeline that runs every night at 2:00 AM with no interactive users. They want a Databricks compute option that supports automated execution without paying to keep development resources running all day. Which option should they choose?

  1. A. All-purpose compute
  2. B. SQL warehouse
  3. C. Job compute (Correct answer)
  4. D. A workspace folder

Correct answer: C

Explanation: Job compute is the best choice because it is commonly used for automated, scheduled, or triggered production workloads and is more cost-efficient than keeping interactive compute running continuously. The scenario explicitly describes a nightly pipeline with no interactive usage, which aligns with job compute rather than all-purpose compute or SQL warehouses.

Sample Question 8 — Databricks Lakehouse Platform

Several engineers need to collaboratively develop Databricks notebooks and related code while tracking changes in Git. Which Databricks feature best supports this requirement?

  1. A. Databricks Repos (Correct answer)
  2. B. SQL warehouses
  3. C. Job compute
  4. D. Unity Catalog schemas

Correct answer: A

Explanation: Databricks Repos is the correct choice because it supports Git-based version control for collaborative development. The requirement is specifically about engineers working together on code and tracking changes, which is exactly the purpose of Repos. SQL warehouses provide analytics compute, job compute runs production tasks, and Unity Catalog schemas are governance objects rather than development version-control tools.

Sample Question 9 — Databricks Lakehouse Platform

A team currently asks an engineer to open a notebook each morning and run three data preparation steps in sequence. The team now wants these steps to run automatically every day, with task dependencies and production scheduling. What is the most appropriate Databricks-native approach?

  1. A. Keep the notebook and ask analysts to run it from a shared folder on schedule
  2. B. Use Databricks Jobs or Workflows to schedule and orchestrate the tasks (Correct answer)
  3. C. Move the notebook into a SQL warehouse so it can be scheduled there
  4. D. Use an all-purpose cluster and leave it running so the tasks are always available

Correct answer: B

Explanation: Databricks Jobs or Workflows is the best answer because it is the platform feature used to schedule and orchestrate production tasks with dependencies. The scenario explicitly requires automated daily execution and ordered steps, which goes beyond simply storing code in a notebook. Keeping notebooks for development is fine, but production orchestration should be handled by Jobs or Workflows.

Sample Question 10 — Databricks Lakehouse Platform

A company has multiple Databricks workspaces. The data platform team wants one centralized way to govern access to tables so permissions are not managed separately in each workspace folder structure. Which Databricks capability best addresses this requirement?

  1. A. Unity Catalog using catalogs and schemas for centralized governance (Correct answer)
  2. B. Workspace folders because they organize notebooks and assets by team
  3. C. All-purpose compute because it can be shared across users
  4. D. Databricks Repos because Git tracks changes to files

Correct answer: A

Explanation: Unity Catalog is the correct answer because it provides centralized governance for data assets using a hierarchy that includes catalogs and schemas. The requirement is about centrally managing access to tables, which is a governance function, not a workspace organization or development feature. Workspace folders and Repos help organize code and assets, but they do not provide centralized table-level governance.

How to Study DB-DEA Databricks Lakehouse Platform

Combine these DB-DEA Databricks Lakehouse Platform practice questions with the official Databricks Academy materials and hands-on practice in a Databricks Community Edition workspace. The DB-DEA exam emphasizes applied knowledge of PySpark, Spark SQL, and Delta Lake, so always relate concepts back to real notebooks and jobs you've built.

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