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Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data Processing and Transformation | - Spark processing
|
| Data Storage and Modeling | - Data modeling
|
| Platform Operations | - Cluster and workload management
|
| Data Governance and Security | - Security frameworks
|
| Data Ingestion and Integration | - Data movement and pipelines
|
Cloudera CDP Data Engineer - Certification Sample Questions:
1. You need to design your Airflow DAG for data quality checks to be easily monitored and visualized. How can you achieve this?
A) Leverage Airflow's built-in metrics and monitoring features like the Airflow web UI to track check execution and results.
B) Implement custom logic to send detailed logs about the check results to external monitoring tools.
C) Utilize Airflow XCom to share the outcome of the checks (success/failurE. with downstream tasks for further processing.
D) All of the above
2. In a PySpark application running on Kubernetes, you want to enable dynamic allocation of Executors. Which configuration setting is essential to turn on this feature?
A) 'spark.kubernetes.dynamicAllocation.enabled'
B) 'spark.executor.instances'
C) 'spark.kubernetes.executor.dynamicAllocation'
D) 'spark.dynamicAllocation.enabled'
3. In optimizing join operations, what role does the Catalyst optimizer in Spark play, specifically regarding join strategies?
A) It disables all optimizations by default to provide consistent performance across different datasets.
B) It manually requires the developer to specify the join strategy for each operation.
C) It exclusively uses broadcast join for all operations to minimize execution time.
D) It dynamically selects the most appropriate join strategy based on the query execution plan.
4. How can "Explain Plan" help in optimizing query performance regarding data partitioning?
A) By revealing the encryption method used for partitioned data
B) By displaying the total size of all partitions
C) By showing the number of partitions created on the fly
D) By indicating whether the query is able to take advantage of partition pruning
5. In Apache Spark, which of the following is the most effective strategy for minimizing data shuffling across nodes in a cluster?
A) Using broadcast variables for small data
B) Increasing the number of partitions
C) Filtering data after a wide transformation
D) Decreasing the number of partitions
Solutions:
| Question # 1 Answer: A,B,C | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: A |






