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Cloudera CDP Data Engineer - Certification Sample Questions:

1. You are deploying a Spark application in a Kubernetes environment. Your application is designed to process large datasets using Spark's data frame API. You have created a Docker image for your Spark application. Which of the following 'kubectl* commands should you use to deploy your Spark application onto the Kubernetes cluster?

A) *kubectl apply -f spark-app.yamr
B) 'kubectl expose deployment my-spark-app --type=LoadBalancer -port=808ff
C) *kubectl config set-context -current -namespace=my-spark-app'
D) 'kubectl create deployment my-spark-app --image=my-spark-app-image'

2. 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) Use the File sensor to check for the presence of a new data file and trigger the DAG only when the file appears.
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) Implement a custom Python script to track the last processed record and use it to filter the data during subsequent runs.

3. For automating the deployment of Spark applications within a Cloudera Data Engineering (CDE. environment using the CDE CLI, what is the primary consideration to ensure seamless integration with existing CI/CD pipelines?

A) Ensuring all Spark applications are containerized before deployment
B) Embedding CDE CLI commands within pipeline scripts and managing credentials securely
C) Utilizing the cde job create command with appropriate flags for version control integration
D) Converting all Spark code to be Kubernetes-native before deployment

4. You're building an Airflow DAG that consists of multiple interdependent ETL pipelines. How can you ensure they execute in the correct order and avoid conflicts?

A) Implement a custom script to manage the execution order of the pipelines.
B) Utilize Airflow sub-DAGs to group related tasks and define dependencies between them.
C) Schedule each pipeline separately with appropriate scheduling intervals.
D) Run all pipelines simultaneously, assuming they are independent.

5. An Iceberg job fails with an "out of memory" error. Which Spark configuration changes might help? (Choose two)

A) Decrease the size of Spark partitions (spark.sql.shuffle.partitions)
B) Increase driver memory (spark.driver.memory)
C) Increase executor memory (spark.executor.memory)
D) Enable Spark broadcast joins
E) Upgrade to a larger Kubernetes pod size.

Solutions: