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Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deployment & Operations | 10% | - CDP Data Engineering Service
|
| Topic 2: Data Storage & Modeling | 22% | - Data Formats & Storage
|
| Topic 3: Apache Spark Development & Processing | 48% | - Spark Streaming & Structured Streaming
|
| Topic 4: Integration & Optimization | 5% | - Hive & Spark Integration
|
| Topic 5: Workflow Orchestration | 15% | - Pipeline Development
|
Cloudera CDP Data Engineer - Certification Sample Questions:
1. When tuning Spark applications, why is it important to adjust the spark.executor.cores configuration?
A) To directly control the amount of memory available to each executor
B) To determine the number of partitions created during shuffle operations
C) To limit the maximum size of Spark's internal data structures
D) To specify the number of tasks that can be run in parallel on a single executor
2. 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) Set depends_on_past=True for all tasks
B) Use the ]] and [[ operators to set task dependencies
C) Use the SequentialExecutor
D) Use the parallelism parameter in the airflow.cfg file
3. You're building an Airflow DAG to extract data from a database table that is constantly updated. How can you implement incremental extraction to avoid processing the entire table each time the DAG runs?
A) Implement a custom Python script to track the last processed record and use it to filter the data during subsequent runs.
B) Leverage the Previous execution operator to access the execution date of the previous DAG run and use it as a filter in the extraction query.
C) Configure the database connection to only retrieve newly added data.
D) Use the File sensor to check for the presence of a new data file and trigger the DAG only when the file appears.
4. What does setting the Spark configuration parameter 'spark.sql.shuffle.partitions' impact?
A The default level of parallelism for joins and aggregations
A) The memory allocation for executor instances
B) The compression codec used for shuffle files
C) The serialization format of data
5. You're tasked with deploying a new Airflow DAG to production. What are some key considerations for ensuring a smooth and successful deployment?
A) All of the above
B) Thoroughly test the DAG in a staging environment before deploying it to production.
C) Utilize infrastructure as code (laC. tools like Terraform to manage Airflow deployment and configuration in a consistent and repeatable manner.
D) Deploy the DAG directly to the production environment without any testing or staging phase.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: B | Question # 3 Answer: B | Question # 4 Answer: C | Question # 5 Answer: A,B,C |
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