top of page

Databricks Certified Data Engineer Associate practice exam

Databricks Certified Data Engineer Associate

Timed practice exams for the Databricks Certified Data Engineer Associate on the real exam's 90 minute limit. Databricks states 45 scored questions and up to 10 unscored items, and a full practice exam on the site serves 55. Every question is original and every option is explained. Your score is broken down by the sections Databricks publishes, so you see what to study next. The question bank holds hundreds of practice questions.

Exam facts

  • Time limit: 90 minutes [1]
  • Questions: 45 scored multiple-choice questions, plus up to 10 unscored items [2]
  • Passing score: Not published by Databricks [3]
  • Exam fee: USD 200, plus applicable taxes [2]
  • Exam languages (real exam): English, Japanese, Brazilian Portuguese and Korean [2]
  • Validity: 2 years [2]
  • Delivery: Online or test center [1]

Tokens: 1 for $4.99, 5 for $14.99, 10 for $24.99 or 25 for $49.99. One token runs a full timed Databricks Certified Data Engineer Associate practice exam, and every shorter session on that exam is then free. The Unlimited pass is $149.99 for a year of every practice session on every exam.

Get tokens

Exam sections and weights

Databricks publishes these sections and objectives for Databricks Certified Data Engineer Associate, quoted here as Databricks words them. [2]

Section 1: Databricks Intelligence Platform, 6% of the exam

  • "Understand the core components of the Databricks Platform, such as its architecture, Delta Lake, and Unity Catalog."
  • "Understand Databricks Platform's compute services, including their characteristics, limitations, and cost models, and select the most suitable option for each workload use case."

Section 2: Data Ingestion and Loading, 21% of the exam

  • "Enable and detail data ingestion patterns, including batch, streaming, and incremental loading, and import data from sources such as local files, Lakeflow Connect standard connectors, and Lakeflow Connect managed connectors."
  • "Use the COPY INTO command to incrementally load files from cloud object storage (ADLS/S3/GCS) into Unity Catalog-governed tables."
  • "Use Auto Loader with schema enforcement and schema evolution in batch modes (for example, directory listing or file notification) to land data into Unity Catalog-governed tables."
  • "Configure Lakeflow Connect to reliably ingest data from diverse enterprise sources into Unity Catalog-governed tables."
  • "Use JDBC/ODBC or REST clients in notebooks to land data into cloud storage or directly into Unity Catalog-governed tables, usually orchestrated and scheduled with Lakeflow Jobs."
  • "Prioritize between Auto Loader, Lakeflow Connect (standard and managed connectors), partner connectors, and other ingestion methods based on technical requirements such as data volume, ingestion frequency, data types, and governance needs with Unity Catalog."
  • "Ingest semi-structured and unstructured data (for example, JSON and nested data) via Lakeflow Connect and other managed connectors into Unity Catalog-governed Delta tables."

Section 3: Data Transformation and Modeling, 22% of the exam

  • "Implement data cleaning by reading bronze tables with PySpark/SQL, cleaning nulls, standardizing data types, and writing to new silver tables."
  • "Combine DataFrames with operations such as Inner join, left join, broadcast join, multiple keys, cross join, union, and union all."
  • "Manipulate columns, rows, and table structures by adding, dropping, splitting, renaming column names, applying filters, and exploding arrays."
  • "Perform data deduplication operations and aggregate operations on DataFrames, such as count, approximate count distinct, and mean, summary."
  • "Understand the basic tuning parameters (spark.sql.shuffle.partitions, spark.default.parallelism, spark.executor/driver.memory, spark.sql.autoBroadcastJoinThreshold) and re-measure the performance."
  • "Understand the difference between, and how to build, Gold layer objects such as materialized views, views, streaming tables, and tables for BI and analytics teams in Unity Catalog."
  • "Apply data quality checks and validation rules to ensure reliable Silver and Gold datasets."

Section 4: Working with Lakeflow Jobs, 16% of the exam

  • "Implement control flows (retries and conditional tasks such as branching and looping) using Lakeflow Jobs for pipeline orchestration"
  • "Configure common tasks (notebook, SQL query, dashboard, and pipeline tasks) and their dependencies using Lakeflow Jobs and its DAG-based task graph"
  • "Implement job schedules using Lakeflow Jobs with an understanding of trigger types (scheduled, file arrival, and table update)"
  • "Choose between time-based and data-driven triggers based on data availability and pipeline dependencies."

Section 5: Implementing CI/CD, 10% of the exam

  • "Manage your code development workflow within the Databricks workspace UI, including creating and switching between branches in Databricks Git folders (formerly Databricks Repos), committing and pushing changes, and creating pull requests using Databricks Git integration."
  • "Understand environment-specific configuration using Declarative Automation Bundle (formerly Databricks Asset Bundles) variables and overrides while promoting the same codebase across dev, test, and prod targets."
  • "Deploy Declarative Automation Bundles (formerly Databricks Asset Bundles) to package, configure, and promote Lakeflow Jobs, Lakeflow Declarative Pipelines, and other workspace assets across dev, test, and prod environments."
  • "Understand the Databricks CLI to validate, deploy, and manage Declarative Automation Bundles (formerly Databricks Asset Bundles) and other workspace assets in automated CI/CD workflows."

Section 6: Troubleshooting, Monitoring, and Optimization, 10% of the exam

  • "Identify trends in job performance using the Lakeflow Jobs run history view to compare current execution times against historical baselines."
  • "Use the Lakeflow Jobs UI to monitor pipeline health by interpreting job statuses, viewing DAG-based task graphs to spot upstream blockers, and tracking pipeline run times and failure rates."
  • "Identify common performance bottlenecks such as data skew, shuffling, and disk spilling by interpreting stage-level metrics in the Spark UI."
  • "Understand the features of Liquid Clustering and predictive optimization."
  • "Diagnose cluster startup failures, library conflicts, and out-of-memory issues."

Section 7: Governance and Security, 15% of the exam

  • "Differentiate between managed and external tables in Unity Catalog and perform basic operations (create, modify, delete, and convert between managed and external tables) on them."
  • "Configure access controls using the UI and SQL by applying GRANT, REVOKE, and DENY privileges to principals (users, groups, and service principals) at appropriate levels of the security hierarchy."
  • "Understand column-level masking and row-level security to restrict data visibility based on user groups."
  • "Understand Unity Catalog ABAC policies to centrally control row-level filtering and column masking for sensitive data."

Sample questions

Three questions from the Databricks Certified Data Engineer Associate practice bank, with the answer and why each option is right or wrong.

Question 1

A platform team runs two Databricks workspaces in the same AWS region, one for ingestion and one for analytics. Both groups must see the same catalogs and tables, and the team wants to manage data access controls once for both workspaces. How should the team set up Unity Catalog?

  • A. Create a metastore for each workspace and copy the tables between them every night.
  • B. Create a metastore for each workspace and grant the same privileges in both.
  • C. Keep the shared tables in the ingestion workspace's DBFS root and point the analytics workspace at that path.
  • D. Link both workspaces to a single Unity Catalog metastore.

Answer: D. Metastores attach to workspaces. Link one metastore to every workspace in a region that needs the same catalogs and the same access controls. [4]

  • A. Wrong. Each metastore holds its own three-level namespace, so two metastores give the workspaces two sets of catalogs, and the nightly copy only duplicates tables between them. The team needs one data view and one place to manage access.
  • B. Wrong. Matching grants in two metastores still means managing access twice, on two copies of the objects. The requirement is one set of controls for both workspaces.
  • C. Wrong. Storing and accessing data through the DBFS root is a deprecated pattern. It also gives the team no Unity Catalog access controls to manage once.
  • D. Right. One metastore can be linked to several workspaces in the same region, so both workspaces get the same data view and the team manages access across all linked workspaces in one place.

Question 2

A finance analyst receives a .zip archive holding four CSV files, about 600 MB in total, and needs them in a Unity Catalog managed table by the afternoon. The analyst has no cloud storage access and plans to use the Create or modify a table using file upload page, but the archive cannot be loaded there. What should the analyst do?

  • A. Repackage the files as a .tar archive and upload that.
  • B. Extract the four CSV files and upload them together.
  • C. Rename the archive with a .csv extension and upload it.
  • D. Split the archive into two smaller .zip files and upload both.

Answer: B. The file upload page takes up to 10 supported files under 2 GB in total and does not accept compressed archives, so extract the files first. [5]

  • A. Wrong. Compressed files such as zip and tar files are not supported on the upload page, so a .tar archive fails the same way the .zip did.
  • B. Right. The upload page takes up to 10 files at a time when their total size is under 2 GB, and CSV is a supported type. Four extracted CSV files at about 600 MB fit inside both limits.
  • C. Wrong. The content would still be a compressed archive, and the page takes only CSV, TSV, JSON, Avro, Parquet or text files. A new extension does not make the archive a CSV file.
  • D. Wrong. Smaller archives are still compressed files, and the upload page does not support compressed files at any size.

Question 3

Bronze orders store amount as STRING. Most values look like 129.99, but a few hold text a clerk typed, such as n/a. The silver SQL job runs with ANSI mode on, must keep running when it meets those values, and must store them as NULL for review while keeping the cents of every valid amount. Which expression should the data engineer use for the silver column?

  • A. `try_cast(amount AS DECIMAL(3,2))`
  • B. `cast(amount AS DECIMAL(10,2))`
  • C. `try_cast(amount AS DECIMAL(10,2))`
  • D. `try_cast(amount AS STRING)`

Answer: C. try_cast returns NULL for a value that is not well formed or doesn't fit the target type, where cast raises an error. [6]

  • A. Wrong. A value that can't fit within the domain of the target type comes back as NULL, and 129.99 doesn't fit in DECIMAL(3,2). Valid amounts would be lost, and the stem needs them kept.
  • B. Wrong. try_cast is the variant that tolerates values that are not well formed. cast raises the invalid data error on n/a, so the job stops, and the stem says it must keep running.
  • C. Right. try_cast returns NULL in place of an invalid data error when a value is not well formed, so n/a becomes NULL and the job keeps running, while DECIMAL(10,2) keeps the cents of 129.99.
  • D. Wrong. The result takes the target type, so amount stays STRING. The silver column needs numbers with cents, not text.

Practice for Databricks Certified Data Engineer Associate on ExamSens.ai

ExamSens.ai is not authorized, endorsed or sponsored by Databricks.

References

[1] https://www.databricks.com/learn/certification/data-engineer-associate

[2] https://www.databricks.com/sites/default/files/2026-10/Databricks-Certified-Data-Engineer-Associate-Exam-Guide-August-2026.pdf

[3] https://www.databricks.com/learn/certification/faq

[4] https://docs.databricks.com/aws/en/getting-started/high-level-architecture

[5] https://docs.databricks.com/aws/en/ingestion/create-or-modify-table

[6] https://docs.databricks.com/aws/en/sql/language-manual/functions/try_cast

bottom of page