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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. Which of the following statements best describes the process of schema inference in the context of big data processing?

A) Encrypting data based on its type.
B) Assigning random data types to columns in unstructured data.
C) Automatically determining the data structure of a dataset.
D) Manually defining the schema for each dataset before processing.

2. You are deploying a Spark application on Kubernetes and need to specify the amount of memory allocated to each Executor. In your PySpark code, which configuration setting will you use?

A) 'spark.driver.memory'
B) 'spark.executor.memoryoverhead'
C) 'spark.executor.instances'
D) 'spark.executor.memory'

3. In the context of Spark, what is a potential downside of indiscriminate use of data caching, especially with the MEMORY_AND DISK storage level?

A) It can lead to reduced fault tolerance due to reliance on in-memory storage.
B) It enhances data security by storing intermediate results in encrypted form.
C) It can decrease network traffic by reducing the need for data shuffling.
D) It may increase execution time due to overheads from frequent disk 1/0 operations.

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) Use the time_sensor operator to delay the transformation task until off-peak hours.
B) Set the max_active_runs parameter to limit executions during peak hours.
C) Configure the DAG's schedule interval and use the TimeDelta sensor for precise timing.
D) Utilize the BranchPythonOperator to dynamically skip the transformation task during peak hours.

5. You have a PySpark application packaged as 'MyPySparkApp-0. I-py3-none-any.whl'. In your 'app.py', you utilize a function from an external library, 'numpy', listed in your 'requirements.txt'. How should you deploy this application to ensure 'numpy' is available at runtime?

A) Upload 'app.py' and manually install 'numpy' on all nodes before submitting using 'spark-submit app.py'.
B) Upload both 'app.py' and 'MyPySparkApp-0.1-py3-none-any.whl' and submit using 'spark-submit --py-files MyPySparkApp-0.1-py3-none- any.whl app.py'.
C) Upload 'MyPySparkApp-0.1-py3-none-any.whl' only and submit using 'spark-submit --py-files MyPySparkApp-0.1-py3-none-any.whl'.
D) Upload 'app.py' only and submit using 'spark-submit app.py'.

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