Cloudera Cloudera Certification CDP-3002

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Cloudera CDP Data Engineer - Certification Sample Questions:

1. Which Spark configuration parameter should be increased to improve performance when dealing with large broadcast variables?

A) 'spark.driver.memory'
B) 'spark.executor.memory'
C) 'spark.default.parallelism'
D) 'spark.broadcast.blockSize'

2. You want to use Spark to perform aggregations on data stored in Hive tables. How can you achieve this efficiently and seamlessly?

A) Implement custom UDFs (User-Defined Functions) in Spark for complex aggregations
B) Use HiveQL's aggregation capabilities and then convert the results back to a Spark DataFrame
C) Write custom aggregation logic using Spark functions and loop through the entire DataFrame
D) Leverage Spark SQL's built-in aggregation functions like SUM and COUNT

3. You're working with a complex DataFrame containing nested structures (e.g., arrays of structs). How can you access and manipulate data within these nested structures?

A) Directly access elements using their position within the nested structure
B) Convert the nested data into a simpler format like a single-level DataFrame
C) Leverage Spark SQL's built-in functions like explode and struct
D) Implement custom recursive functions to navigate through the nested structure

4. What is the best practice for handling DAG dependencies in Apache Airflow when one DAG's output is another DAG's input?

A) Directly call one DAG from another using the PythonOperator.
B) Use the TriggerDagRunOperator to trigger one DAG from another upon completion.
C) Use the ExternalTaskSensor to wait for a task in another DAG to complete.
D) Manually trigger the dependent DAG after the first DAG completes.

5. After running a PySpark job, you want to analyze the performance and identify potential bottlenecks. Which tool should you use for this purpose?

A) Hadoop YARN ResourceManager.
B) PySpark DataFrame API.
C) Spark Web I-Jl.
D) PySpark SQL CLI.

Solutions: