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Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Spark Development & Processing | 48% | - Spark Architecture & Execution Model
|
| Topic 2: Deployment & Operations | 10% | - CDP Data Engineering Service
|
| Topic 3: Integration & Optimization | 5% | - Hive & Spark Integration
|
| Topic 4: Workflow Orchestration | 15% | - Apache Airflow
|
| Topic 5: Data Storage & Modeling | 22% | - Distributed Persistence
|
Cloudera CDP Data Engineer - Certification Sample Questions:
Question 1
In the context of Airflow, what is the purpose of the "XCom" feature?
A. To manage dependencies and package installations across tasks.
B. To trigger external systems when a DAG reaches a specific state.
C. To execute commands across different operating systems.
D. To enable communication between different tasks within the same DAG.
Question 2
You're experimenting with Iceberg table formats (vl and v2). Which of the following statements is true regarding their differences?
A. V2 supports new data types like UUIDs, which are unavailable in V1.
B. V2 uses manifest lists instead of manifest files for tracking data files.
C. V2 tables are generally less performant than V1 tables due to added metadata overhead.
D. V2 introduces mandatory partitioning, while V1 allows for unpartitioned tables.
Question 3
How can you ensure that a set of tasks in an Airflow DAG are executed in parallel after a specific initial task is completed?
A. Use the parallelism parameter in the airflow.cfg file
B. Set depends_on_past=True for all tasks
C. Use the SequentialExecutor
D. Use the ]] and [[ operators to set task dependencies
Question 4
An Airflow DAG is designed to ingest data from multiple sources, transform it, and load it into a data warehouse. The transformation step is resource-intensive and should not run during peak hours (9 AM to 5 PM). How can you configure the DAG to meet this requirement?
A. Utilize the BranchPythonOperator to dynamically skip the transformation task during peak hours.
B. Set the max_active_runs parameter to limit executions during peak hours.
C. Use the time_sensor operator to delay the transformation task until off-peak hours.
D. Configure the DAG's schedule interval and use the TimeDelta sensor for precise timing.
Question 5
In a multi-tenant Hive environment, how can administrators mitigate the impact of skewed data distributions across bucketed tables to maintain consistent query performance?
A. Disabling bucketing features entirely to prevent skewed distributions.
B. Enforcing a uniform bucket size at the HDFS level, irrespective of the data distribution.
C. Limiting the number of tenants allowed to create bucketed tables.
D. By periodically rebalancing data across buckets using custom scripts or tools.
Solutions:
| Question 1 Answer: D | Question 2 Answer: B | Question 3 Answer: D | Question 4 Answer: A | Question 5 Answer: D |
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