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Cloudera CDP Data Engineer - Certification Sample Questions:

1. You're facing slow performance in your Spark application due to frequent shuffles. What are some strategies you can employ to optimize shuffle operations and improve performance?

A) Reduce the number of partitions in your RDDs
B) All of the above
C) Use a broadcast variable for small, frequently accessed data
D) Increase the number of executors in your cluster

2. You want to perform an Iceberg table join in CDP using Spark SQL, but you notice it's much slower than expected. What could be some of the reasons? (Choose two)

A) Spark is using nested loop joins instead of broadcast hash joins due to table sizes.
B) One of the tables isn't partitioned effectively.
C) Iceberg version mismatch between Spark and CDP.
D) Spark's dynamic query execution is enabled.
E) You're joining on a column with low cardinality (few distinct values).

3. You're tasked with deploying a new Airflow DAG to production. What are some key considerations for ensuring a smooth and successful deployment?

A) Deploy the DAG directly to the production environment without any testing or staging phase.
B) All of the above
C) Thoroughly test the DAG in a staging environment before deploying it to production.
D) Utilize infrastructure as code (laC. tools like Terraform to manage Airflow deployment and configuration in a consistent and repeatable manner.

4. Your ETL pipeline processes sensitive dat
a. How can you ensure data security within Airflow?

A) Implement encryption for sensitive data at rest and in transit.
B) Use environment variables to store sensitive credentials for accessing data sources.
C) Store passwords and other sensitive data directly within your Airflow code.
D) Grant everyone access to the Airflow web UI to monitor and manage the pipeline.

5. You are processing a large dataset using Spark and need to ensure that the results are available for subsequent stages without recomputing. Which approach achieves this efficiently?

A) Use rdd.persist() with the appropriate storage level based on your needs
B) Leverage Spark's automatic checkpointing mechanism
C) Implement custom logic to save the data to HDFS between stages
D) Store the data in a temporary table using Spark SQL

Solutions: