Every unsuccessful attempt at the Databricks-Certified-Data-Engineer-Associate exam costs another full registration fee, not to mention weeks of lost momentum. Before risking that, candidates throughout 2026 are validating their readiness with the Databricks Certified Data Engineer Associate practice questions from ActualCollection.
Databricks Databricks-Certified-Data-Engineer-Associate Exam Overview:
| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Data Engineer Associate Exam |
| Exam Number: | Databricks-Certified-Data-Engineer-Associate |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 60 |
| Exam Price: | $200 USD |
| Related Certifications: | Databricks Certified Data Analyst Associate |
| Available Languages: | English |
| Exam Duration: | 90 minutes |
| Passing Score: | 70% |
| Exam Format: | Multiple Select, Multiple Choice |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online proctored or in-person testing center |
| Pre Condition: | Recommended: 6+ months of experience with Databricks and Apache Spark |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/data-engineer-associate |
Databricks Databricks-Certified-Data-Engineer-Associate Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Spark SQL and DataFrames | 15-20% | - Aggregate and group data - Write and execute Spark SQL queries - Join and union DataFrames - Handle null values and data quality |
| Python for Data Engineering | 10-15% | - Implement user-defined functions (UDFs) - Work with Spark APIs in Python - Use PySpark for data processing |
| Lakehouse Platform Concepts | 10-15% | - Understand the Lakehouse architecture and its benefits - Explain data governance and security concepts - Describe key Databricks Lakehouse platform components |
| Apache Spark Data Processing Fundamentals | 20-25% | - Apply transformations and actions on DataFrames - Create and use Spark DataFrames - Work with structured data types (arrays, maps, structs) - Use Spark SQL for data processing |
| Data Pipeline Architecture | 15-20% | - Design data pipelines for batch and streaming - Understand ELT vs ETL patterns - Implement incremental data processing - Monitor and optimize pipeline performance |
| Delta Lake Fundamentals | 20-25% | - Create and manage Delta tables - Explain Delta Lake features and benefits - Understand ACID transactions and time travel - Write to and read from Delta tables |
Databricks Databricks-Certified-Data-Engineer-Associate Certification Exam Q&A
Databricks Certified Data Engineer Associate is an official Databricks exam, registered under the code Databricks-Certified-Data-Engineer-Associate. A passing score earns you the Databricks Certification certification, positioned at the Associate level. The credential also connects to Databricks Certified Data Analyst Associate, so it can anchor a broader certification path. Because Databricks designs its exams around real job tasks, holding this certification signals practical skill rather than memorized theory.
Candidates face 60 questions inside a 90 minutes window on the Databricks Certified Data Engineer Associate exam. That ratio leaves little slack, which is why pacing deserves as much practice as the content itself. Learn to budget your minutes, park stubborn questions instead of wrestling them, and rehearse under a real clock: a few timed runs in the ActualCollection test engine will make the official time limit feel routine rather than threatening.
The passing bar for Databricks Certified Data Engineer Associate is set at 70%, and registering for the exam officially costs $200 USD. There is no reduced price for a second try: fail, and you pay $200 USD in full again. That makes honest self-testing the cheapest insurance available, so hold off on booking until your ActualCollection practice scores sit clearly above the passing line, attempt after attempt.
Recommended: 6+ months of experience with Databricks and Apache Spark
Vendor policies are revised from time to time, so double-check the eligibility details before registering on the official exam page.
Absolutely. A free PDF demo of the Databricks Certified Data Engineer Associate questions is available at ActualCollection, so you can inspect the quality and formatting before any money changes hands. Once you buy, updates are free for 365 days, and when that period runs out you can extend the update service at 50% off the regular price.
ActualCollection offers a 100% money-back guarantee with specific conditions. If you take the Databricks Certified Data Engineer Associate exam within 60 days of purchase and fail, you may claim a full refund, provided the exam matches your product. Sitting the exam within 3 days of purchase disqualifies a claim, as do downloaded-but-unused products, free materials, and expired orders; the candidate name must also match the payer name. To file, submit a scanned enrollment slip and the official Score Report PDF within 2 days of the exam, and the claim is processed within 7 days. If you prefer, you can skip the refund and instead receive two other exam products of equal value at no charge while keeping the update service on your original purchase.
As for delivery: it is immediate. Your files become downloadable the moment payment completes and are also emailed to you within one minute. If nothing shows up within 2 hours, contact customer service. You may install the software on an unlimited number of computers.
Databricks Certified Data Engineer Associate is divided into 6 official domains. Among the headline areas are Apache Spark Data Processing Fundamentals (20-25%), Python for Data Engineering (10-15%), and Data Pipeline Architecture (15-20%). Scroll up to the exam topics section for the full breakdown, and use it as a checklist: any line you cannot confidently explain deserves another round of practice.
Databricks Certified Data Engineer Associate Sample Questions:
Question 1
An organization needs to share a dataset stored in its Databricks Unity Catalog with an external partner who uses a different data platform that is not Databricks. The goal is to maintain data security and ensure the partner can access the data efficiently. Which method should the data engineer use to securely share the dataset with the external partner?
A. Databricks-to-Databricks Sharing
B. Using Delta Sharing with the open sharing protocol
C. Exporting data as CSV files and emailing them
D. Using a third-party API to access the Delta table
Question 2
A data engineer needs to process SQL queries on a large dataset with fluctuating workloads. The workload requires automatic scaling based on the volume of queries, without the need to manage or provision infrastructure. The solution should be cost-efficient and charge only for the compute resources used during query execution. Which compute option should the data engineer use?
A. Databricks Jobs
B. Databricks SQL Analytics
C. Databricks Runtime for ML
D. Serverless SQL Warehouse
Question 3
A data engineer is writing Spark code to group sales data by region and calculate total revenue for each region. Which Spark DataFrame transformation performs grouping operations?
A. filter
B. select
C. groupBy
D. orderBy
Question 4
A data engineer has a Job that has a complex run schedule, and they want to transfer that schedule to other Jobs.
Rather than manually selecting each value in the scheduling form in Databricks, which of the following tools can the data engineer use to represent and submit the schedule programmatically?
A. There is no way to represent and submit this information programmatically
B. Cron syntax
C. pyspark.sql.types.TimestampType
D. datetime
E. pyspark.sql.types.DateType
Question 5
A data engineer is debugging a Python notebook in Databricks that processes a dataset using PySpark. The notebook fails with an error during a DataFrame transformation. The engineer wants to inspect the state of variables, such as the input DataFrame and intermediate results, to identify where the error occurs. Which tool should the engineer use to debug the notebook and inspect the values of variables like DataFrames?
A. Use the Spark UI to analyze the execution plan and identify stages where the job failed
B. Use the Ganglia UI to monitor cluster resource usage and identify hardware issues
C. Use the Python Notebook Interactive Debugger to set breakpoints and inspect variable values in real-time
D. Use the Databricks CLI to download and analyze driver logs for detailed error messages
Solutions:
| Question 1 Answer: B | Question 2 Answer: D | Question 3 Answer: C | Question 4 Answer: B | Question 5 Answer: C |





