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Cloudera CDP-3002 Exam Overview:
| Certification Vendor: | Cloudera |
| Exam Name: | CDP Data Engineer - Certification Exam |
| Exam Number: | CDP-3002 |
| Certificate Validity Period: | 3 years |
| Related Certifications: | Cloudera Certified Data Engineer CDP Generalist |
| Exam Duration: | 90 minutes |
| Real Exam Qty: | 50 |
| Available Languages: | English |
| Exam Price: | $330 USD |
| Exam Format: | Multiple Choice, Multiple Response, Scenario-Based Questions |
| Passing Score: | 55% |
| Recommended Training: | Cloudera Data Engineering (DENG-100) Advanced Spark Performance Tuning (DENG-200) Apache Iceberg Fundamentals (DENG-152) |
| Exam Registration: | Cloudera Certification Portal |
| Sample Questions: | Cloudera CDP-3002 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 |
|---|---|---|
| Integration & Optimization | 5% | - Troubleshooting
|
| Data Storage & Modeling | 22% | - Apache Iceberg
|
| Deployment & Operations | 10% | - Security & Governance
|
| Workflow Orchestration | 15% | - Apache Airflow
|
| Apache Spark Development & Processing | 48% | - Performance Optimization
|
Cloudera CDP Data Engineer - Certification Sample Questions:
1. When writing a DataFrame to a CSV file, what potential issues should you consider and how can you address them?
A) All of the above
B) No specific issues need to be considered, as CSV is a simple format
C) Ensure proper handling of special characters and delimiters to avoid data corruption
D) Choose an appropriate compression format like Gzip to reduce file size
2. You are developing a PySpark application that processes a large dataset. Your application involves a transformation that requires shuffling data across the Executors. Which of the following responsibilities of the Spark Driver is most directly involved in this process?
A) Managing the lifecycle of Executor pods in Kubernetes.
B) Monitoring the progress and status of tasks on the Executors.
C) Managing the shuffling and aggregation of data across Executors.
D) Interpreting the user application and creating an execution plan.
3. When tuning Spark applications, why is it important to adjust the spark.executor.cores configuration?
A) To limit the maximum size of Spark's internal data structures
B) To determine the number of partitions created during shuffle operations
C) To specify the number of tasks that can be run in parallel on a single executor
D) To directly control the amount of memory available to each executor
4. What happens when a task in Airflow is marked as "skipped"?
A) The skipped task's downstream tasks are automatically executed.
B) The skipped task's downstream tasks are also skipped, unless their conditions for execution are met independently.
C) Airflow retries the skipped task until it succeeds.
D) The entire DAG is marked as failed, and no further tasks are executed.
5. Your Airflow DAG includes tasks that can potentially fail due to various reasons. How can you handle such failures and ensure the overall workflow continues as intended?
A) Configure the DAG to automatically retry failed tasks a specific number of times.
B) Implement custom logic within each task to handle potential errors and retry failed tasks manually.
C) All of the above
D) Utilize Airflow XCom to share information about failed tasks with downstream tasks for alternative processing.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: C | Question # 3 Answer: C | Question # 4 Answer: B | Question # 5 Answer: A,C |
By Constance
