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Cloudera CDP Data Engineer - Certification Sample Questions:

1. In the context of caching data for reuse in Spark, how does the Tungsten project contribute to enhancing memory management and execution efficiency?

A) By optimizing Spark's internal memory management and data processing operations
B) By enforcing strict data typing to reduce runtime type checking overhead
C) By providing a graphical interface for memory management
D) By rewriting Spark operations in a lower-level language for direct memory access

2. Your project involves integrating Spark with a NoSQL database, MongoDB. You need to write a DataFrame 'df into a MongoDB collection named 'orders'. Which PySpark code snippet correctly achieves this?

A)

B)

C)

D)

3. You need to join a Spark DataFrame with a Hive table. How can you achieve this efficiently?

A) Load the Hive table data into the DataFrame and then perform an in-memory join
B) Use Spark SQL syntax with the JOIN clause, specifying the join type and condition
C) Convert the Hive table to a temporary table and then perform the join with the DataFrame
D) Implement custom logic using Spark's RDD operations to join the data

4. In a Kubernetes environment, how does Kubernetes ensure fault tolerance when a pod running a Spark task fails?

A) Using a load balancer for master components
B) By using a Standby Master setup
C) Through Pod Replication with ReplicaSets
D) By checkpointing RDDs

5. In Apache Airflow, what is a DAG?

A) A visualization tool for monitoring pipeline execution
B) A collection of tasks with directional dependencies
C) A programming language used for creating data pipelines
D) A type of operator used for executing Python functions

Solutions: