Syllabi change, and so do exams — Cloudera CDP Data Engineer - Certification is no exception. LatestCram refreshes its CDP-3002 practice questions throughout 2026, and 365 days of free updates keep all 320 questions aligned with the current exam.
Cloudera CDP-3002 Exam Overview:
| Certification Vendor: | Cloudera |
|---|---|
| Exam Name: | CDP Data Engineer - Certification Exam |
| Exam Number: | CDP-3002 |
| Passing Score: | 55% |
| Certificate Validity Period: | 3 years |
| Exam Format: | Multiple Response, Multiple Choice, Scenario-Based Questions |
| Real Exam Qty: | 50 |
| Related Certifications: | Cloudera Certified Data Engineer CDP Generalist |
| Exam Duration: | 90 minutes |
| Available Languages: | English |
| Exam Price: | $330 USD |
| Recommended Training: | Advanced Spark Performance Tuning (DENG-200) Cloudera Data Engineering (DENG-100) Apache Iceberg Fundamentals (DENG-152) |
| Exam Registration: | Cloudera Certification Portal |
| Sample Questions: |  |
| Exam Way: | Online Proctored (remote), no onsite option |
| Pre Condition: | No mandatory prerequisites; recommended experience: 1+ year designing/developing data pipelines with Cloudera tools, Spark, and Airflow |
| Official Syllabus URL: | https://www.cloudera.com/certification/cdp-data-engineer.html |
Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Spark Development & Processing | 48% | - Spark Streaming & Structured Streaming
|
| Topic 2: Data Storage & Modeling | 22% | - Apache Iceberg
|
| Topic 3: Deployment & Operations | 10% | - Security & Governance
|
| Topic 4: Integration & Optimization | 5% | - Hive & Spark Integration
|
| Topic 5: Workflow Orchestration | 15% | - Pipeline Development
|
Common Questions About the Cloudera CDP-3002 Exam, Answered
The CDP-3002 exam, officially titled CDP Data Engineer - Certification Exam, is the qualifying exam for the CDP Data Engineer certification, which sits at the Professional level. Earning it shows employers that you have the skills the credential stands for, and it is a solid step forward on a Cloudera career path. It also connects to Cloudera Certified Data Engineer, CDP Generalist, so the effort you put in now keeps paying off as you advance.
The CDP-3002 exam gives you 50 questions to complete within 90 minutes. Do the math before exam day: divide the total time by the question count to set a steady per-question pace, and flag any item that stalls you so you can return to it after securing the easier points. The most reliable way to build that rhythm is a full timed practice test under the same limit, which is exactly what LatestCram's test engines are designed for.
The passing score for Cloudera CDP Data Engineer - Certification is 55%, and the official registration fee is $330 USD. Keep in mind that a retake means paying that fee again in full, which makes an honest self-check worthwhile: before you book, sit a timed practice test and make sure you are consistently scoring above the passing line with some margin to spare.
According to the official requirements: No mandatory prerequisites; recommended experience: 1+ year designing/developing data pipelines with Cloudera tools, Spark, and Airflow. Eligibility rules do change from time to time, so confirm the current details on the official exam page before you register.
Registration for the CDP-3002 exam is handled through the official channels below:
The exam is delivered in the following format: Online Proctored (remote), no onsite option. Pick a date that leaves room for at least one full timed practice test beforehand.
Cloudera recommends the following official training options for candidates preparing for Cloudera CDP Data Engineer - Certification:
- Cloudera Data Engineering (DENG-100)
- Advanced Spark Performance Tuning (DENG-200)
- Apache Iceberg Fundamentals (DENG-152)
Courses build the foundation, but they work best alongside question practice. That is where LatestCram comes in: the 320 practice questions in our CDP-3002 package let you rehearse the exam format at your own pace and see exactly where you stand.
Yes — LatestCram offers a free PDF demo of the Cloudera CDP Data Engineer - Certification material, so you can review the question style and answer quality before you spend anything. After purchase, you also receive 365 days of free updates, and if your product expires you can extend the update service at a 50% discount from your member zone.
If you take the corresponding exam within 60 days of your purchase and do not pass, you are covered by the 100% Money Back Guarantee under clear conditions. To claim a full refund, submit a scan of your exam enrollment slip and your official Score Report PDF within two days of the exam; requests are processed within seven days. Note that sitting the exam within three days of purchase is not eligible, purchases that were never followed by an exam sitting do not qualify, free materials and expired orders are excluded, and the candidate name must match the payer name. Prefer an exchange instead? You can swap your product for two free exam packages of equal value and keep your update service. Delivery is immediate: your CDP-3002 material is ready for instant download and is also emailed to you within one minute of payment — contact customer service if nothing arrives within two hours. There is no limit on the number of computers you can install it on.
The current Cloudera CDP Data Engineer - Certification syllabus is organized into 5 exam domains. The first three are:
- Integration & Optimization (5%)
- Workflow Orchestration (15%)
- Apache Spark Development & Processing (48%)
For the full domain and subtopic breakdown, see the Exam Topics section above — that is the outline your LatestCram practice questions are mapped to.
Cloudera CDP Data Engineer - Certification Sample Questions:
In Apache Airflow, what is a DAG?
- A. A programming language used for creating data pipelines
- B. A type of operator used for executing Python functions
- C. A collection of tasks with directional dependencies
- D. A visualization tool for monitoring pipeline execution
Correct Answer: C 🗳️
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Discuss the trade-offs between using wide tables (many columns) and narrow tables (few columns) in Spark and the implications for data processing efficiency.
- A. Spark can optimize data access patterns for both wide and narrow tables equally.
- B. Both A and C.
- C. Wide tables require more storage and may lead to performance overhead due to data serialization.
- D. Wide tables offer better performance for filtering operations, while narrow tables excel in aggregations.
Correct Answer: B 🗳️
Explanation: Only visible for LatestCram members. You can sign-up / login (it's free).
You're building an Airflow DAG that involves multiple data processing tasks. How can you handle task dependencies and ensure the tasks execute in the correct order?
- A. Use the Airflow UI to visually connect the tasks with arrows, indicating their execution order.
- B. Define the tasks directly within the DAG code using Python operators and set their dependencies manually.
- C. Leverage XCom to share data between tasks and define dependencies based on the data availability.
- D. All of the above
Correct Answer: D 🗳️
Explanation: Only visible for LatestCram members. You can sign-up / login (it's free).
Your Airflow DAG involves tasks that require access to confidential data like passwords or API keys. How can you securely manage and access these credentials within the DAG?
- A. Store the credentials directly within the DAG code, assuming limited access to the codebase.
- B. Utilize environment variables to store the credentials and access them within the tasks.
- C. Implement a custom secret management solution outside of Airflow.
- D. All of the above
Correct Answer: B,C 🗳️
Explanation: Only visible for LatestCram members. You can sign-up / login (it's free).
You're building an Airflow ETL pipeline that involves data validation checks. How can you integrate these checks into the pipeline and handle potential failures?
- A. Configure the DAG to send email notifications upon validation failures for manual intervention.
- B. Implement the validation checks within custom Python operators and raise exceptions upon failure, triggering downstream tasks to handle the error.
- C. Leverage XCom to share the validation results with downstream tasks for further processing.
- D. Use the FailoverOperator to automatically skip downstream tasks if the validation check fails.
Correct Answer: B 🗳️
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