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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% | - Data Formats & Storage
|
| Topic 3: Deployment & Operations | 10% | - Security & Governance
|
| Topic 4: Integration & Optimization | 5% | - Hive & Spark Integration
|
| Topic 5: Workflow Orchestration | 15% | - Apache Airflow
|
Cloudera CDP Data Engineer - Certification Sample Questions:
Question 1
While working with Spark SQL, you encounter an error message stating "Unable to resolve table 'table_name"'. What could be the potential cause of this error?
A. The Spark application doesn't have sufficient permissions to access the table
B. The table doesn't exist in the Hive metastore
C. The table schema is not compatible with Spark SQL
D. All of the above
Question 2
Which of the following is a benefit of using broadcast variables in Spark for caching static lookup tables?
A. They increase the amount of data shuffle during joins.
B. They reduce network I/O by making data available locally on each node.
C. They reduce the reliability of Spark applications.
D. They are automatically cleaned up after each task.
Question 3
What are the potential trade-offs to consider when using checkpointing in Spark applications?
A. Checkpointing introduces overhead for storing and recovering data, impacting performance
B. All of the above
C. Checkpointing requires manual configuration and can be error-prone
D. Checkpointing always improves performance and has no drawbacks
Question 4
You're working with a real-time streaming application using Spark Streaming. How can you ensure that your application gracefully handles late-arriving data and maintains data consistency?
A. Use Spark's checkpointing functionality to recover from failures
B. Recompute the entire stream from scratch for each late record
C. Implement micro-batching with windowing and watermarking techniques
D. Ignore late-arriving data altogether
Question 5
In a meeting about Spark applications deployment on Kubernetes, your team discusses strategies for handling node failures. Which Kubernetes feature should be utilized to maintain high availability in case of node failures?
A Horizontal Pod Autoscaler
A. Pod Priority
B. Resource Quotas
C. ReplicaSets
Solutions:
| Question 1 Answer: D | Question 2 Answer: B | Question 3 Answer: A,B | Question 4 Answer: C | Question 5 Answer: A |



