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Cloudera CDP Data Engineer - Certification Sample Questions:

1. Which of the following is a critical consideration when deciding between using a sort merge join and a shuffle hash join in a distributed data processing system like Spark?

A) The relative size of the datasets and the available memory on each executor
B) The network latency between nodes in the cluster
C) The version of the Spark cluster being used
D) The availability of secondary indexes on the join keys

2. 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) By periodically rebalancing data across buckets using custom scripts or tools.
D) Limiting the number of tenants allowed to create bucketed tables.

3. What is the recommended way to handle dependencies between data quality checks in Apache Airflow to ensure that checks are performed in a specific sequence?

A) Explicitly set task dependencies using the set_upstream or set_downstream methods.
B) Use the depends_on_past parameter in each data quality check task.
C) Use the SequentialExecutor for the Airflow environment.
D) Implement each data quality check as a separate DAG.

4. You're working with a large dataset that needs to be partitioned and processed in chunks to improve efficiency. How can you achieve this using Airflow operators?

A) Configure the source system to provide the data pre-partitioned for efficient processing.
B) Implement a custom Python script to handle partitioning and then use the BashOperator to execute the script within the DAG.
C) Use the Split Operator to divide the data into smaller subsets and chain them with downstream processing tasks.
D) Leverage the File transform operator to partition the data based on specific criteria within the operator itself.

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

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