Cloudera CDP Data Engineer - Certification Sample Questions:

1. How does Spark achieve fault tolerance during distributed processing?

A) By implementing automatic checkpointing of intermediate results
B) By replicating data across all nodes in the cluster
C) null
D) By restarting failed tasks on different nodes

2. What is the impact of setting the Spark configuration spark.sql.autoBroadcastJoinThreshold to -1?

A) It automatically selects the optimal threshold for broadcasting based on the cluster's current workload.
B) It sets an unlimited threshold for broadcasting tables, which may cause out-of-memory errors.
C) It increases the threshold for choosing which table to broadcast in a join, potentially improving join performance.
D) It disables the broadcast join feature, forcing all joins to be shuffled joins.

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

4. You're debugging a slow-running Spark job writing a large Iceberg table. Which optimization techniques could improve performance? (Choose three.

A) Repartitioning the DataFrame before writing to Iceberg
B) Disabling Spark's adaptive query execution
C) Converting the DataFrame to RDD for Iceberg writes
D) Using the Z-Order clustering option in Iceberg
E) Filtering data as early as possible in the Spark transformation pipeline

5. What is the role of a Spark driver in a distributed processing job?

A) Stores and processes intermediate data
B) Coordinates tasks across the cluster
C) Manages communication between executors and workers
D) Performs computations on individual data partitions

Solutions: