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Cloudera CDP Data Engineer - Certification Sample Questions:

1. Your Iceberg table experiences frequent schema evolution, with new columns being added regularly. Which of the following might you need to adjust to maintain optimal performance?

A) Kubernetes resource requests for Spark executors
B) Iceberg metadata table pruning frequency
C) Spark shuffle partitions (spark.sql.shuffle.partitions)
D) The Iceberg table's write format (vl or v2)

2. Given a DataFrame containing product information with columns "product_id", "name", and "price", how can you filter and sort the DataFrame to only include products with a price greater than $50 and sort them by price in descending order?

A) Implement a custom function to filter and sort the data
B) Leverage chained conditions and sorting within the filter method
C) Use spark SQL's WHERE clause and ORDER BY statement
D) Use multiple filter and sort calls sequentially

3. You're designing a scalable and fault-tolerant Spark application for processing large-scale geospatial dat a. What additional considerations should you take into account beyond the general distributed processing principles?

A) Use a dedicated cluster specifically for geospatial processing tasks
B) Choose a larger virtual machine size for Spark executors
C) Leverage geospatial libraries like GeoSpark for efficient spatial operations
D) Implement custom partitioning strategies based on geospatial coordinates

4. You want to select specific columns from a Spark DataFrame and rename them. How can you achieve this in Spark SQL?

A) Modify the original DataFrame schema directly
B) Use Spark SQL's ALTER TABLE statement to modify the table schema
C) Use the select() method with column names and aliases within parentheses
D) Implement custom logic to iterate through the DataFrame and create a new one

5. You need to design a DAG that can be easily triggered based on external events or data availability. How can you achieve this functionality?

A) Rely on manual intervention to trigger the DAG whenever necessary.
B) Utilize Airflow sensors like FileSensor or S3KeySensor to wait for specific conditions before triggering the DAG.
C) Schedule the DAG to run continuously at a fixed interval.
D) Implement custom code within the DAG to actively check for external events or data availability.

Solutions: