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Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Storage & Modeling | 22% | - Apache Iceberg
|
| Deployment & Operations | 10% | - CDP Data Engineering Service
|
| Apache Spark Development & Processing | 48% | - Performance Optimization
|
| Integration & Optimization | 5% | - Hive & Spark Integration
|
| Workflow Orchestration | 15% | - Pipeline Development
|
Cloudera CDP Data Engineer - Certification Sample Questions:
Question #1
Which of the following is a benefit of using broadcast variables in Spark for caching static lookup tables?
A. They reduce network I/O by making data available locally on each node.
B. They increase the amount of data shuffle during joins.
C. They are automatically cleaned up after each task.
D. They reduce the reliability of Spark applications.
Question #2
How can you leverage the Cloudera Data Engineering service to monitor the performance and health of your data pipelines?
A. Utilize the built-in monitoring capabilities of the Cloudera Data Engineering service, including dashboards and alerts.
B. Manually review log files generated by individual pipeline steps.
C. Integrate the service with a separate external monitoring tool.
D. Utilize custom scripting to collect and analyze pipeline metrics.
Question #3
In the context of Cloudera's Optimization Framework, what is the purpose of dynamic partition pruning?
A. To partition data dynamically based on query execution plans
B. To dynamically eliminate unnecessary partitions from a query plan based on runtime statistics
C. To update partition metadata in real-time
D. To increase the size of partitions dynamically based on data volume
Question #4
When tuning Spark applications, why is it important to adjust the spark.executor.cores configuration?
A. To specify the number of tasks that can be run in parallel on a single executor
B. To limit the maximum size of Spark's internal data structures
C. To determine the number of partitions created during shuffle operations
D. To directly control the amount of memory available to each executor
Question #5
In a Kubernetes environment, you want to restrict the communication to your Spark application pods to only allow traffic from pods in a specific namespace. Which Kubernetes feature would you use to implement this?
A. Network Policies
B. Service Mesh
C. StatefulSets
D. Deployments
Solutions:
| Question #1 Answer: A | Question #2 Answer: A | Question #3 Answer: B | Question #4 Answer: A | Question #5 Answer: A |
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