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Cloudera CDP Data Engineer - Certification Sample Questions:

1. What is the impact of query vectorization in Cloudera's Optimization Framework?

A) It slows down query execution by adding complexity
B) It encrypts query results for security
C) It enables the execution of SQL commands
D) It improves query performance by processing batches of rows together

2. Which Spark component is responsible for managing the execution of tasks on worker nodes?

A) Spark Core
B) spark SQL
C) Spark Executor
D) Spark Driver

3. A PySpark application is facing performance issues due to uneven distribution of data across the nodes. Which approach would best help in resolving this issue?

A) Convert the DataFrame to an RDD and then back to a DataFrame.
B) Use 'df.collect()'.
C) Apply 'df.coalesce(Y to reduce the number of partitions.
D) Employ to redistribute data based on a key.

4. Explain the concept of lineage tracking in Spark and its benefits for fault tolerance and debugging.

A) Lineage tracks the dependencies between data transformations, enabling efficient re-execution on failures.
B) Lineage allows for caching intermediate results, improving performance.
C) It creates a log of all operations performed on data, aiding in debugging issues.
D) Both A and B.

5. What command and parameters should be used to update an existing Spark job in the Cloudera Data Engineering (CDE. service to increase its executor memory using the CDE CLI?

A) cde spark submit -update -name my-spark-job -executor-memory 6G
B) cde job update -name my-spark-job --conf spark.executor.memory=6g
C) cde job config -name my-spark-job -set spark.executor.memory=6g
D) cde job run -edit my-spark-job -conf spark.executor.memory=6g

Solutions: