PDF Version DemoCloudera CDP-3002 Exam Overview:
| Certification Vendor: | Cloudera |
| Exam Name: | Cloudera CDP Data Engineer Certification Exam |
| Exam Number: | CDP-3002 |
| Real Exam Qty: | Approximately 50–60 |
| Exam Duration: | 120 minutes |
| Exam Price: | USD $195 (may vary by region) |
| Exam Format: | Multiple choice, Scenario-based questions, Performance-based tasks |
| Available Languages: | English |
| Certificate Validity Period: | 2 years |
| Related Certifications: | Cloudera Data Platform (CDP) certifications Cloudera Data Analyst Cloudera Administrator |
| Passing Score: | 70% |
| Recommended Training: | Cloudera CDP Hands-on Labs Cloudera Data Engineering Training |
| Exam Registration: | Cloudera Certification Portal Pearson VUE Registration |
| Sample Questions: | Cloudera CDP-3002 Sample Questions |
| Exam Way: | Online proctored exam (typically delivered via Pearson VUE or Cloudera's certification platform) |
| Pre Condition: | No formal prerequisites required. Recommended: hands-on experience with Cloudera Data Platform, Spark, and SQL-based analytics. |
| Official Syllabus URL: | https://www.cloudera.com/certification.html |
Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data Ingestion and Integration | - Batch and streaming ingestion
|
| Data Storage and Modeling | - Data modeling
|
| Data Governance and Security | - Security frameworks
|
| Platform Operations | - Cluster and workload management
|
| Data Processing and Transformation | - SQL analytics engines
|
Cloudera CDP Data Engineer - Certification Sample Questions:
1. Which of the following is a benefit of using broadcast variables in Spark for caching static lookup tables?
A) They are automatically cleaned up after each task.
B) They reduce network I/O by making data available locally on each node.
C) They reduce the reliability of Spark applications.
D) They increase the amount of data shuffle during joins.
2. You're tasked with optimizing an existing Airflow DAG that processes large datasets daily. The DAG has multiple tasks, some of which frequently fail due to memory constraints on the worker nodes. Which approach would best mitigate this issue without upgrading hardware?
A) Use the Pool feature to limit the number of concurrent memory-intensive tasks.
B) Implement the Retry operator to automatically retry failed tasks.
C) Increase the priority_weight of memory-intensive tasks to ensure they run first.
D) Split the DAG into multiple smaller DAGs to reduce the load on workers.
3. How can you utilize Spark's lineage tracking feature to improve the efficiency of your data pipelines?
A) Leverage lineage information to identify and recompute only the affected data after failures
B) Manually track the lineage of each transformation for debugging purposes
C) Spark's lineage tracking doesn't offer any performance benefits
D) Use lineage tracking to optimize the order of transformations for better performance
4. In the context of packaging a PySpark application, what is the purpose of the 'requirements.txt' file?
A) To list the environment variables needed for the application.
B) To specify the Python version required for the application.
C) To list all the third-party dependencies required by the application.
D) To define the Spark version compatible with the application.
5. Which of the following strategies would NOT be recommended for managing skewed data during join operations in Spark?
A) Salting the keys to distribute the data more evenly across partitions.
B) Applying a filter to remove outliers that cause data skewness before joining.
C) Increasing the number of partitions to distribute the skewed data more evenly.
D) Using a broadcast join assuming one dataset is small enough to fit into memory.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A | Question # 3 Answer: A | Question # 4 Answer: C | Question # 5 Answer: B |
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