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Cloudera CDP Data Engineer - Certification Sample Questions:

1. You need to design your Airflow DAG for data quality checks to be scalable and manageable as the number of datasets and checks grows. How can you achieve this?

A) Leverage external configuration files (e.g., YAML or JSON) to define data quality checks and associated parameters.
B) Hardcode all data quality checks and data sources directly within the DAG code.
C) Utilize Airflow variables to store configuration details like data source paths and check thresholds.
D) Implement a modular design using sub-DAGs, where each sub-DAG encapsulates the data quality checks for a specific dataset.

2. How does the Cloudera Data Engineering service integrate with cloud storage solutions like Amazon S3 or Azure Blob Storage?

A) Requires manual configuration for each storage bucket.
B) Requires custom scripting for each cloud storage provider.
C) Utilizes built-in connectors for seamless access.
D) Not directly supported, requires external tools.

3. Consider a PySpark application that calculates the count of rows in a CSV file. The main application file is 'count_rows.py', and it uses a custom library 'data_utils.py'. After packaging, which 'spark-submit' command correctly submits this job including the custom library?

A) 'spark-submit --files data_utils.py count_rows.py'
B) 'spark-submit -py-files data utils.py count_rows.py'
C) 'spark-submit --jars data utils.py count_rows.py'
D) 'spark-submit --archives data_utils.py count_rows.py'

4. When leveraging Spark's DataFrame API for caching, what implicit optimization does Spark perform to enhance processing efficiency?

A) Predicate pushdown and other logical optimizations before physical execution
B) Automatic selection of the optimal storage level based on the DataFrame's size
C) Automatic conversion of all operations to map-reduce jobs
D) Inlining of functions to reduce the overhead of JVM method calls

5. How does Spark achieve fault tolerance during distributed processing?

A) null
B) By replicating data across all nodes in the cluster
C) By restarting failed tasks on different nodes
D) By implementing automatic checkpointing of intermediate results

Solutions: