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

2. How can you implement a data quality check in Apache Airflow that verifies the row count of a table does not decrease from the previous DAG run?

A) Store the row count from the previous run in Airflow Variables and compare it using a Pythonoperator.
B) Utilize the PreviousDagRunSensor with a custom Python function for comparison.
C) Implement a custom SqlSensor that checks the row count against a stored value in XComs.
D) Use the BranchPythonOperator to branch the workflow based on the row count comparison logic.

3. When optimizing join operations in a distributed data processing environment, why is it important to co-locate join keys?

A) To increase the storage capacity required for join operations
B) To ensure data integrity by preventing data loss during network transmission
C) To minimize data shuffle by ensuring related data is on the same node
D) To enhance data encryption methods for secure joins

4. In a CI/CD pipeline, what is a key consideration when integrating Cloudera Data Engineering (CDE. service API calls for deploying Spark jobs, specifically regarding security?

A) Incorporating API token rotation and secure secret management practices
B) Hardcoding API keys in the pipeline scripts
C) Using generic Spark Docker images for all jobs to avoid API customization
D) Disabling TLS for API endpoints to simplify integration

5. You have a DataFrame containing sales data with columns "product_id", "customer id", and "amount". How can you efficiently calculate the total sales per customer?

A) Implement a custom function to group and sum the sales
B) Utilize spark SQL's GROUP BY and SUM functions
C) Use a loop to iterate through the DataFrame and accumulate the sales for each customer
D) Leverage Spark's machine learning library (MLIiB. for aggregation

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