Free Databricks Data Engineer Associate practice exam 2026
7-day money-back guarantee — full refund within 7 days of purchase if you've completed under 20% of the questions. See pricing →
Certifications Tools Flashcards Career Paths Exam Guides Blog Pricing For Teams About
Try 20 questions free →

Free Databricks Data Engineer Associate practice exam 2026: 20 questions without signup

Start the free Databricks Data Engineer Associate practice exam

20 questions from the real bank, every option explained. No signup, no card.

Start free Databricks Data Engineer Associate practice exam →
7 domains 817 questions 90 min exam
Questions on the exam
45
Time allowed
90 minutes format →
Exam fee
$200 — vendor, checked October 6, 2026 detail →

817 practice exam questions for Databricks Certified Data Engineer Associate, grouped by exam domain. Every question below shows all four options, which one is correct, and why each of the other three is not — the wrong answers are where most candidates lose marks.

Not sure where you stand? Take the free 5-min Databricks Data Engineer Associate readiness check →

Databricks Data Engineer Associate certification: requirements, cost and exam format → ·  Databricks Data Engineer Associate exam format →  ·  Databricks Data Engineer Associate passing score →  · Databricks Data Engineer Associate exam cost →

Questions by domain

Sample questions

2: readings (integer columns, nullable): amount 2 NULL 2 7 NULL SQL: SELECT COUNT(*) FROM readings WHERE amoun

Data Transformation and Modeling Easy
The following tables contain exactly the rows shown, including duplicates. NULL represents a SQL null value.

readings (integer columns, nullable):
amount
2
NULL
2
7
NULL

SQL:
SELECT COUNT(*) FROM readings WHERE amount IS NULL

What single numeric value does this query return?
  1. 5
    Five is the total row count, but the WHERE predicate retains only rows with NULL amounts.
  2. 1
    The fixture contains two NULL amounts, and COUNT(*) counts every row satisfying the predicate.
  3. 2 ✓
    `IS NULL` matches both NULL rows, and COUNT(*) counts each qualifying row.
  4. 0
    The predicate explicitly selects rows whose amount is SQL NULL, so the two NULL rows are not excluded.
The trap
Counts only one of the two NULL rows. Confuses SQL NULL with an ordinary comparable value. Ignores the filtering predicate entirely.

All 180 Data Transformation and Modeling questions →

Use Auto Loader in a streaming query with an appropriate: Which implementation satisfies the requirement?

Data Ingestion and Loading Medium
A customer-events source writes new JSON files continuously to cloud storage. The pipeline must process arriving files incrementally during the day rather than rescan a fixed bounded directory each run. Which implementation satisfies the requirement?
  1. Use a Lakeflow Connect database connector without verifying source support
    A database connector is unsuitable for cloud files and requires supported source connectivity and connector prerequisites.
  2. Use Auto Loader in a streaming query with an appropriate checkpoint ✓
    File-based streaming incrementally discovers and processes arriving files while recording progress for continued execution.
  3. Run a batch query over the directory every night
    A nightly batch processes bounded snapshots and does not provide incremental daytime consumption of arriving files.
  4. Use `COPY INTO` as a one-time table load
    A one-time load does not establish the requested continuously running incremental processing behavior.
The trap
Confuses initial loading with ongoing ingestion. Selects a managed connector for the wrong source type. Uses scheduled bounded processing for continuous arrival.

All 172 Data Ingestion and Loading questions →

Every 15 minutes in UTC: What cadence does this schedule express?

Working with Lakeflow Jobs Easy
A device telemetry Lakeflow Job must run every 15 minutes, continuously, in UTC. It must not be configured as a file-arrival or table-update trigger. The following is a supported configuration fragment:schedule: quartz_cron_expression: "0 0/15 * * * ?" timezone_id: "UTC" pause_status: "UNPAUSED"What cadence does this schedule express?
  1. Every 15 minutes in UTC. ✓
    The Quartz expression fires at minute zero and every fifteen-minute interval, with UTC explicitly selected.
  2. Once every hour in UTC.
    An hourly schedule would not include the repeated fifteen-minute interval expression shown here.
  3. Whenever a telemetry file arrives.
    File-arrival triggers are data-driven; this configuration is an explicit time-based schedule.
  4. Whenever the telemetry source table changes.
    Table-update triggers respond to source updates, whereas the supplied configuration defines a recurring clock schedule.
The trap
Confuses table-update detection with cron scheduling. Reads the hour field without interpreting minute increments. Confuses a time trigger with a file-arrival trigger.

All 129 Working with Lakeflow Jobs questions →

Use `is_account_group_member` with a mapped region filter: Which implementation satisfies the requirement?

Governance and Security Medium
A shipment-tracking table contains rows for many regions. Analysts should see only rows for regions assigned to their account-level group, while the base table remains shared. Each account group maps to a region, required catalog, schema, and table privileges are already granted, and the policy must restrict rows at query time using Unity Catalog identity functions. Which implementation satisfies the requirement?
  1. Mask the region column for users outside the assigned group.
    A column mask changes values but does not remove rows from the result.
  2. Tag the table and rely on the tag to filter rows automatically.
    A tag alone supplies metadata and does not enforce row filtering.
  3. Use workspace-group membership without a region predicate.
    This uses the wrong identity scope and does not map group membership to a region.
  4. Use `is_account_group_member` with a mapped region filter. ✓
    The account-level membership function and region predicate together restrict returned rows to the user's assigned region.
The trap
Uses workspace identity and omits the required row condition. Confuses masking with row filtering. Treats metadata as enforcement.

All 123 Governance and Security questions →

Validation checks bundle configuration: What explains the result and what should CI run next?

Implementing CI/CD Hard
A team packages an application-log job with Declarative Automation Bundles. The bundle contains a root databricks.yml, a job resource, and targets named dev and prod. In CI, the team runs `databricks bundle validate -t prod`, but no workspace resource appears afterward. What explains the result and what should CI run next?
  1. Validation checks business data quality; CI should run `databricks bundle run -t prod` to create resources.
    Validation does not prove data quality, and run executes an existing resource rather than deploying the bundle configuration.
  2. Validation deploys only development resources; CI should change the target name before rerunning validation.
    The selected target controls configuration, but validate remains non-deploying for both development and production targets.
  3. Validation checks bundle configuration; CI should run `databricks bundle deploy -t prod` to apply resources. ✓
    The validate command checks configuration only; deploy applies declared bundle resources to the selected target workspace.
  4. Validation creates resources only after a job resource is manually started from the workspace.
    Validation does not stage resources for manual creation; deployment is the command that applies declared resources.
The trap
Confuses configuration validation with data testing and deployment. Misattributes the missing resources to target selection. Invents a manual activation step between validation and deployment.

All 82 Implementing CI/CD questions →

Cluster event logs: What should the engineer inspect first?

Troubleshooting, Monitoring, and Optimization Hard
A daily finance job has not started after several retries. The job’s Spark code has not changed, but cluster event logs show repeated pending-capacity messages before executors are created. What should the engineer inspect first?
  1. Rewrite the finance joins to use a broadcast hint before examining cluster events.
    Join strategy affects running stages, while the observed failure occurs before executors are created.
  2. Cluster event logs, queue time, and available capacity for the requested compute configuration. ✓
    Pending-capacity messages point to startup or capacity constraints, so cluster events and queue behavior are the first evidence.
  3. Collect the input data to the driver so the job can bypass the unavailable executor startup.
    Driver collection increases driver memory pressure and cannot bypass the compute capacity required to start the job.
  4. Increase shuffle partitions immediately, because startup capacity messages prove the transformations have too few tasks.
    Shuffle partition tuning affects task execution and does not explain failure to obtain initial cluster capacity.
The trap
Treats pre-startup failure as a join-performance issue. Applies execution tuning before compute startup diagnosis. Uses driver processing as an invalid startup workaround.

All 82 Troubleshooting, Monitoring, and Optimization questions →

ACID transactions and versioned table state: Which Delta Lake capability directly addresses this failure?

Databricks Intelligence Platform Medium
A daily finance pipeline writes the same payment adjustments concurrently from two tasks. With plain Parquet files, readers sometimes observe inconsistent intermediate results. Which Delta Lake capability directly addresses this failure?
  1. ACID transactions and versioned table state. ✓
    Delta Lake commits table changes transactionally and maintains versioned table state, preventing readers from observing partial committed updates.
  2. Job retries with checkpoint recovery after failed tasks.
    Retries and checkpoint recovery belong to pipeline execution and do not make concurrent table writes atomic for readers.
  3. Object-store versioning for deleted files and folders.
    Object-store versioning may preserve storage-object versions, but it does not provide Delta table transactions or a consistent table commit protocol.
  4. Partition replacement with file renames and object-store consistency.
    File replacement and object-store behavior alone do not provide Delta's transactional commit log and consistent table versions.
The trap
Confuses storage versioning with table transactions Confuses orchestration reliability with storage consistency Treats file operations as ACID transactions

All 49 Databricks Intelligence Platform questions →

Increase `spark.sql.shuffle.partitions`: What is the most appropriate first remediation?

Data Transformation and Modeling Medium
A workforce report performs a large DataFrame join followed by aggregation. The Spark UI shows long-running shuffle tasks and executor spills. The cluster has enough total resources, but the SQL shuffle partition count is very low.

Exhibit: The workload uses SQL/DataFrame operations, and AQE is enabled. What is the most appropriate first remediation?
  1. Collect the joined DataFrame to the driver before performing the aggregation.
    Collecting a large join result can exhaust driver memory rather than improve executor parallelism.
  2. Increase `spark.sql.shuffle.partitions`; compare representative runs and AQE. ✓
    Increasing the initial SQL shuffle parallelism addresses the stated low count, while comparison shows whether AQE and the change improve execution.
  3. Set `spark.default.parallelism` very high while leaving SQL shuffle settings unchanged.
    This setting primarily affects RDD defaults and does not directly correct SQL shuffle partitioning.
  4. Reduce the SQL shuffle partition count further.
    Fewer shuffle partitions increase work per partition and do not address the stated oversized tasks and spill caused by an already low count.
The trap
Applying coalescing when the observed problem requires more initial shuffle parallelism. Targets RDD defaults instead of the SQL setting. Moves a large result to the driver.

All 180 Data Transformation and Modeling questions →

Databricks Data Engineer Associate exam: the facts

How many questions are on the Databricks Data Engineer Associate exam?

45, as published by the exam vendor.

How long is the Databricks Data Engineer Associate exam?

90 minutes. Across 45 questions that is about 120 seconds per question.

What topics does the Databricks Data Engineer Associate exam cover?

7 domains: Data Transformation and Modeling, Data Ingestion and Loading, Working with Lakeflow Jobs, Governance and Security, Implementing CI/CD, Troubleshooting, Monitoring, and Optimization, Databricks Intelligence Platform. Weights: Data Transformation and Modeling 22%, Data Ingestion and Loading 21%, Working with Lakeflow Jobs 16%, Governance and Security 15%, Implementing CI/CD 10%, Troubleshooting, Monitoring, and Optimization 10%, Databricks Intelligence Platform 6%.

How many Databricks Data Engineer Associate practice exam questions does Certsqill have?

817, spread across 7 exam domains. Every one shows all options, which is correct, and why each of the others is not.

The Databricks data engineer certification exam in brief

The Databricks data engineering associate certification is the associate-level credential in Databricks’ data engineering track, below the Professional. The exam has 45 scored multiple-choice questions in 90 minutes, costs USD 200 per attempt plus applicable taxes, and follows the exam guide dated May 2026, with seven weighted sections led by Data Transformation and Modeling at 22% and Data Ingestion and Loading at 21%.

Exams may also include unscored items. Databricks does not publish a passing score — its FAQ explains that passing scores are set statistically and can change — so no practice percentage can be converted into a predicted result.

Is there an official Databricks practice exam?

No. Databricks states in its FAQ: “We currently do not offer any practice exams.” The exam guide includes a handful of sample questions, which Databricks notes are retired from a previous version of the exam. So every Databricks Certified Data Engineer Associate practice exam you find is independent, and its value depends on how closely it follows the May 2026 guide.

When you choose Databricks Certified Data Engineer Associate practice exams, check three things: that they cover all seven sections in proportion to their weights, that they use current names such as Lakeflow Jobs, Lakeflow Connect and Declarative Automation Bundles, and that every answer is explained.

How to use Databricks Certified Data Engineer Associate questions

Treat each of the Databricks Certified Data Engineer Associate questions you practise as a small lab. Read the scenario, decide which feature it is really about — Auto Loader or COPY INTO, a streaming table or a materialized view, a row filter or a column mask — and then read the explanation for every option, including the ones you ruled out.

Work section by section first, then take a full Databricks Data Engineer Associate certification practice exam of mixed questions in 90 minutes. Certsqill’s questions are expert-developed against the seven sections of the guide, every answer option has its own explanation, and the readiness score is shown per section. Paid access comes with a 7-day money-back guarantee.

Databricks Certified Data Engineer Associate dumps: what the terms say

Databricks Certified Data Engineer Associate dumps claim to contain live exam questions. Databricks’ certification terms treat exam questions and answers as confidential and list “using unauthorized materials (including brain-dump material and/or unauthorized publication of Exam questions)” as misconduct, alongside disseminating exam content by any means, including web postings.

The consequences in the same terms are serious: loss of all Databricks certifications, and being barred from taking any Databricks exam, permanently if Databricks so decides. A file sold as Databricks Data Engineer Associate certification dumps PDF therefore exists only if someone broke those terms — or it is invented and presented as real.

Why exam dumps fail even on their own terms

Databricks runs one live version of each exam and updates the guide when the exam changes; the current one covers the exam as of 4 May 2026. Databricks Certified Data Engineer Associate exam dumps collected earlier train you on an outline that may no longer exist, and you cannot check whether their answers were ever right.

Memorised answers also do not survive a reworded scenario. Questions written against the published guide, with explanations, teach the reasoning the exam tests — which is what still works when Databricks changes the wording.

Exam questions are not interview questions

Databricks data engineer interview questions are a different exercise. The certification checks foundational, associate-level tasks against a published guide; an interview tests how you have designed, run and debugged pipelines in practice. Databricks interview questions for senior data engineer roles usually go well beyond the associate exam — into architecture, cost and performance trade-offs, and production incidents.

Practice questions here are built for the exam. They help in an interview only insofar as they make you fluent in the platform features the exam covers.

Databricks Data Engineer Associate practice: questions people ask

Does Databricks offer a practice exam?

No. Databricks’ FAQ states that it currently does not offer any practice exams. Checked 9 October 2026.

Are Databricks Data Engineer Associate dumps allowed?

No. Databricks’ certification terms name brain-dump material as misconduct, with loss of all Databricks certifications and a possible permanent ban from Databricks exams. Checked 9 October 2026.

What about Databricks associate data engineer certification dumps on file-sharing sites?

They are covered by the same terms, and there is no way to check whether their answers match the current exam guide. Use questions written against the May 2026 guide instead.

Is a Databricks certification data engineer associate dumps file a good last-minute shortcut?

No. Besides breaking Databricks’ terms, it gives you no explanations, and the exam is updated whenever the guide changes. Checked 9 October 2026.

What is the passing score for the Databricks Data Engineer Associate exam?

Databricks does not publish passing scores, because they are set statistically and can change. Source: Databricks FAQ, checked 9 October 2026.

Would you pass Databricks Data Engineer Associate today?

Five minutes, and you get a score per domain — not one number, but which section to open tonight.

Test your Databricks Data Engineer Associate readiness — free
Certsqill Databricks Data Engineer Associate question bank · 817 questions across 7 domains · Every answer, right and wrong, comes with its own explanation.