200+ Free Databricks Data Engineer Professional Practice Questions

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Question 1

Score: 0/0

A silver-layer SQL transform must retain exactly one latest order record per `order_id` using `updated_at`. Which Spark SQL pattern is the most reliable?

GROUP BY `order_id` and keep `MAX(updated_at)` without returning the full row

Use `ROW_NUMBER()` over `PARTITION BY order_id ORDER BY updated_at DESC` and filter to `rn = 1`

Use `DISTINCT order_id` after selecting all columns

Sort the table by `updated_at` and apply `LIMIT 1`

to track

2026 Statistics

Key Facts: Databricks Data Engineer Professional Exam

Scored Questions

Databricks

120 min

Time Limit

Databricks

$200

Exam Fee

Databricks

2 years

Validity

Databricks

Online/Test Center

Delivery

Databricks

22%

Largest Domain

Developing Code

As of March 10, 2026, the current Databricks Data Engineer Professional exam page lists 59 scored questions, a 120-minute time limit, a $200 registration fee, online or test-center delivery, 2-year validity, and 10 weighted sections led by Developing Code for Data Processing using Python and SQL (22%) and Cost & Performance Optimisation (13%). Databricks does not publicly publish a fixed passing score on the current exam page.

Sample Databricks Data Engineer Professional Practice Questions

Try these sample questions to test your Databricks Data Engineer Professional exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 200+ question experience with AI tutoring.

1A silver-layer SQL transform must retain exactly one latest order record per `order_id` using `updated_at`. Which Spark SQL pattern is the most reliable?

A.GROUP BY `order_id` and keep `MAX(updated_at)` without returning the full row

B.Use `ROW_NUMBER()` over `PARTITION BY order_id ORDER BY updated_at DESC` and filter to `rn = 1`

C.Use `DISTINCT order_id` after selecting all columns

D.Sort the table by `updated_at` and apply `LIMIT 1`

Explanation: A window function with `ROW_NUMBER()` returns one deterministic latest row per business key while preserving the rest of the row values. Aggregating only `MAX(updated_at)` does not by itself identify the complete winning record, and `DISTINCT` or `LIMIT` does not solve per-key deduplication.

2You need to classify rows with a few regex-based rules in PySpark and keep the job optimizable by Catalyst. What should you do?

A.Implement the logic with a Python UDF

B.Convert the DataFrame to pandas first

C.Use built-in column expressions such as `when`, `regexp_extract`, and `rlike`

D.Collect the data to the driver and loop in Python

Explanation: Built-in Spark SQL and DataFrame expressions stay in the optimized execution plan and avoid Python serialization overhead. Python UDFs and driver-side processing usually reduce performance and make plans harder for Spark to optimize.

3In Lakeflow Declarative Pipelines, when should a dataset be modeled as a streaming table instead of a materialized view?

A.When the source is append-oriented or streaming and the target should be updated incrementally

B.When every refresh should fully recompute the dataset from scratch

C.Only when the target is stored outside Unity Catalog

D.Only when the table is smaller than 1 GB

Explanation: Streaming tables are designed for incremental processing from continuously arriving data and maintain streaming semantics across updates. Materialized views are better when the engine should recompute query results from current source state rather than track a stream.

4A structured streaming job writes to Delta through `foreachBatch`. The job may be restarted after failures, and duplicate output must be avoided. Which design is best?

A.Write in append mode without a checkpoint

B.Collect the micro-batch to the driver before writing

C.Delete the target and replay the full source after every restart

D.Use a stable checkpoint and make each micro-batch idempotent, for example with `MERGE` on a business key

Explanation: A stable checkpoint preserves streaming progress, and an idempotent sink pattern prevents duplicate effects if a batch is replayed. Blind append writes or manual full reloads are riskier and usually more expensive in production.

5A CDC feed contains operation codes `I`, `U`, and `D`. Which `MERGE` logic is required to correctly apply deletes into a Delta target?

A.Overwrite the target table on every batch

B.Add a `WHEN MATCHED AND op = 'D' THEN DELETE` clause

C.Ignore delete rows and rely on `VACUUM`

D.Union source rows to the target and run `DROP DUPLICATES`

Explanation: A CDC merge must explicitly translate delete operations into row removals in the target. Overwrite or union-based approaches discard history or produce incorrect current-state results for incremental pipelines.

6A 20 MB dimension table is joined to a 4 TB fact DataFrame in PySpark. Which approach usually produces the most efficient plan?

A.Repartition both sides to 2,000 partitions before the join

B.Cache both DataFrames regardless of reuse

C.Broadcast the dimension table during the join

D.Convert the dimension to a Python dictionary and look up values in a UDF

Explanation: Broadcasting a small dimension table usually avoids a large shuffle and is a standard optimization for fact-to-dimension enrichment. Driver-side dictionaries and unnecessary repartitioning typically scale poorly in distributed workloads.

7A Lakeflow Declarative Pipelines project has bronze, silver, and gold datasets in one pipeline. How should downstream dependencies be expressed so Databricks can manage refresh order automatically?

A.Reference upstream datasets directly inside downstream dataset definitions

B.Create separate Workflows jobs for every dependency edge

C.Trigger notebooks manually with `dbutils.notebook.run`

D.Schedule the gold layer last and assume upstream layers are finished

Explanation: Declarative dependencies let the pipeline engine understand lineage and compute the correct execution graph. Manual orchestration of internal dataset ordering adds complexity and removes the advantages of a declarative pipeline model.

8You need 15-minute event-time aggregations from a streaming source while limiting state growth and allowing late data up to 2 hours. Which design is correct?

A.Use processing-time windows only

B.Window by event time and set a 2-hour watermark

C.Use complete mode with no watermark

D.Sort every micro-batch on the driver before writing

Explanation: Event-time windows plus a watermark define both the aggregation boundary and the allowed lateness, which keeps state bounded. Processing-time windows ignore the actual event timestamp, and no-watermark designs can grow state indefinitely.

9A Spark SQL join must treat two NULL business-key values as equal. Which operator should you use in the join condition?

A.=

B.LIKE

C.<=>

D.IS DISTINCT FROM

Explanation: The null-safe equality operator `<=>` treats `NULL <=> NULL` as true, which is different from standard `=` semantics. That makes it useful when business rules require nulls to match during joins or merge conditions.

10Your team wants reusable PySpark cleansing steps that can be unit tested and chained cleanly across multiple DataFrames. Which pattern fits best?

A.Duplicate the logic in every notebook

B.Collect each DataFrame to pandas and transform locally

C.Hide the logic inside widget default values

D.Wrap transformations in functions and apply them with `DataFrame.transform(...)`

Explanation: Encapsulating DataFrame logic in functions improves reuse, readability, and testability without abandoning distributed execution. Copy-pasted notebook code and local pandas conversions make maintenance and scaling harder.

About the Databricks Data Engineer Professional Exam

The Databricks Certified Data Engineer Professional exam measures advanced, production-focused data engineering skills on the Databricks Data Intelligence Platform. The public Databricks exam page emphasizes secure, reliable, and cost-effective ETL pipelines, complex data processing with Python and SQL, streaming workloads, workflow orchestration, observability, governance, CI/CD, and deployment tooling such as the Databricks CLI, REST API, and Asset Bundles.

Questions

59 scored questions

Time Limit

120 minutes

Passing Score

Databricks does not publicly publish a fixed passing score

Exam Fee

$200 (Databricks / Kryterion)

Databricks Data Engineer Professional Exam Content Outline

22%

Developing Code for Data Processing using Python and SQL

Author reliable Spark SQL and PySpark logic for batch and streaming pipelines, Delta workloads, and robust production-grade data processing patterns.

Data Ingestion & Acquisition

Implement ingestion patterns such as Auto Loader, CDC, schema evolution handling, and repeatable acquisition flows for raw and incremental data.

10%

Data Transformation, Cleansing, and Quality

Apply standardization, deduplication, expectations, and transformation logic that produces trusted silver and gold datasets.

Data Sharing and Federation

Share governed data products with Delta Sharing, query external systems with Lakehouse Federation, and design secure external access patterns.

10%

Monitoring and Alerting

Observe jobs and pipelines with system tables, run history, metrics, and alerting workflows so failures are detected and triaged quickly.

13%

Cost & Performance Optimisation

Tune storage layout, file sizing, Photon usage, compute choices, clustering, and workload design for efficient, scalable pipeline execution.

10%

Ensuring Data Security and Compliance

Enforce least privilege, secrets management, network and data protection controls, auditing, and compliance-aware platform usage.

Data Governance

Use Unity Catalog catalogs, schemas, lineage, tags, and governed sharing to manage discoverability, stewardship, and policy enforcement.

10%

Debugging and Deploying

Debug failures, package projects, and deploy production solutions with workflows, the Databricks CLI, REST API, Repos, and Asset Bundles.

Data Modelling

Design medallion and analytics-ready models that support maintainable downstream consumption and performant business-facing datasets.

How to Pass the Databricks Data Engineer Professional Exam

What You Need to Know

Passing score: Databricks does not publicly publish a fixed passing score
Exam length: 59 questions
Time limit: 120 minutes
Exam fee: $200

Keys to Passing

Complete 500+ practice questions
Score 80%+ consistently before scheduling
Focus on highest-weighted sections
Use our AI tutor for tough concepts

Databricks Data Engineer Professional Study Tips from Top Performers

1Study from the official section weights first, because Developing Code and Cost & Performance Optimisation together account for over a third of the exam.

2Practice both Spark SQL and PySpark on Delta tables, including merges, streaming, schema evolution, and recovery scenarios instead of memorizing syntax in isolation.

3Use Unity Catalog hands-on for permissions, lineage, external locations, tags, and governed sharing so governance and security questions map to lived workflows.

4Build at least one deployment flow with Databricks Asset Bundles, the CLI, jobs, and environment-specific configuration before you sit for the exam.

5Review performance mechanics such as file compaction, clustering choices, Photon, partition strategy, and serverless-versus-classic tradeoffs under realistic workloads.

6Use system tables, run history, alerts, and pipeline logs to debug failures so monitoring and deployment topics feel operational rather than theoretical.

Frequently Asked Questions

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

The current Databricks exam page lists 59 scored questions. Databricks also states that exams may include unscored items for statistical use, and that extra time is already factored in for that content.

How long is the Databricks Data Engineer Professional exam?

Databricks currently lists a 120-minute time limit for the professional data engineer exam. The exam is proctored and the current public page says you can take it online or at a test center.

What are the current Databricks Data Engineer Professional domain weights?

As of March 10, 2026, Databricks lists 10 weighted sections: Developing Code for Data Processing using Python and SQL (22%), Data Ingestion & Acquisition (7%), Data Transformation, Cleansing, and Quality (10%), Data Sharing and Federation (5%), Monitoring and Alerting (10%), Cost & Performance Optimisation (13%), Ensuring Data Security and Compliance (10%), Data Governance (7%), Debugging and Deploying (10%), and Data Modelling (6%).

Does Databricks publish a fixed passing score for the professional exam?

The current public Databricks exam page and exam guide do not publish a fixed passing score for this exam. When preparing, it is safer to target strong accuracy across all weighted sections instead of planning around an unofficial score estimate.

Are there formal prerequisites for Databricks Data Engineer Professional?

Databricks lists no formal prerequisites, but says related training is highly recommended. The same page also recommends hands-on experience performing the data engineering tasks covered in the exam guide.

What changed in the current Databricks professional exam version?

The current public Databricks professional page emphasizes the Databricks Data Intelligence Platform, Lakeflow Spark Declarative Pipelines, Databricks Compute including serverless, Unity Catalog, Asset Bundles, and both online and test-center delivery. As of March 10, 2026, no separate 2026 regulatory change notice was found beyond the current public exam page and the November 30, 2025 exam guide now in force.