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Cloudera CDP-3002 Exam Overview:
| Certification Vendor: | Cloudera |
| Exam Name: | Cloudera CDP Data Engineer Certification Exam |
| Exam Number: | CDP-3002 |
| Related Certifications: | Cloudera Data Platform (CDP) certifications Cloudera Data Analyst Cloudera Administrator |
| Available Languages: | English |
| Exam Duration: | 120 minutes |
| Passing Score: | 70% |
| Real Exam Qty: | Approximately 50–60 |
| Exam Format: | Multiple choice, Scenario-based questions, Performance-based tasks |
| Exam Price: | USD $195 (may vary by region) |
| Certificate Validity Period: | 2 years |
| Recommended Training: | Cloudera CDP Hands-on Labs Cloudera Data Engineering Training |
| Exam Registration: | Pearson VUE Registration Cloudera Certification Portal |
| 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 Governance and Security | - Security frameworks
|
| Data Processing and Transformation | - SQL analytics engines
|
| Data Ingestion and Integration | - Batch and streaming ingestion
|
| Data Storage and Modeling | - Data modeling
|
| Platform Operations | - Cluster and workload management
|
Cloudera CDP Data Engineer - Certification Sample Questions:
1. You notice a significant performance overhead when persisting a large RDD to disk. What potential factors could contribute to this, and how can you mitigate them?
A) All of the above
B) Inefficient serialization format for the RDD data
C) Using HDFS as the storage location instead of local disks
D) Insufficient disk space on the cluster nodes
2. You want to select specific columns from a Spark DataFrame and rename them. How can you achieve this in Spark SQL?
A) Modify the original DataFrame schema directly
B) Implement custom logic to iterate through the DataFrame and create a new one
C) Use the select() method with column names and aliases within parentheses
D) Use Spark SQL's ALTER TABLE statement to modify the table schema
3. For automating the deployment of Spark applications within a Cloudera Data Engineering (CDE. environment using the CDE CLI, what is the primary consideration to ensure seamless integration with existing CI/CD pipelines?
A) Converting all Spark code to be Kubernetes-native before deployment
B) Utilizing the cde job create command with appropriate flags for version control integration
C) Embedding CDE CLI commands within pipeline scripts and managing credentials securely
D) Ensuring all Spark applications are containerized before deployment
4. How does Spark handle data shuffling during distributed processing?
A) By transferring only required data between executors
B) Spark doesn't perform data shuffling
C) By storing all data on a single node
D) By broadcasting all data to each executor
5. You're working with an Airflow DAG that performs data quality checks on sensitive dat a. How can you ensure data security during the checks?
A) Implement data masking techniques to obfuscate sensitive information during the checks.
B) All of the above
C) Store sensitive data like thresholds and comparison values directly within the DAG code.
D) Utilize environment variables to store sensitive data and access them within the PythonOperator.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: C | Question # 3 Answer: C | Question # 4 Answer: A | Question # 5 Answer: A,D |
By Dana
