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Cloudera CDP Data Engineer - Certification Sample Questions:

1. You're provisioning a new Cloudera Data Engineering (CDE. virtual cluster. Which of the following factors should you consider when choosing an appropriate instance type for Iceberg workloads? (Choose two)

A) The expected size of your Iceberg tables
B) The type of storage (SSD vs. HDD. needed for optimal performance
C) The complexity of your SQL queries and transformations
D) Whether you require GPU acceleration for machine learning tasks integrated with Iceberg
E) The network bandwidth required by your Iceberg jobs

2. Which technologies are typically involved in schema inference processes in Cloudera Data Platform (CDP)?

A) Manual scripting
B) Traditional RDBMS
C) Apache NiFi
D) Apache Spark
E) Apache Avro

3. You are working with a large dataset in PySpark and notice that certain operations are being executed repeatedly, leading to performance issues. Which of the following approaches should you adopt to optimize these operations?

A) Use the 'df.persist()' method.
B) Increase the number of partitions using 'df.repartition()'.
C) Write the data to a CSV file and then read it back.
D) Use the method.

4. Why are partitioned tables beneficial in Hive for large datasets?

A) They automatically encrypt data based on the partition key.
B) They allow for faster query processing by scanning only relevant partitions.
C) They ensure that all data is stored in a single file, simplifying access.
D) They decrease the security of the data.

5. If a Spark Driver pod in Kubernetes is reaching its CPU limit and experiencing performance issues, what is the most appropriate first action?

A) Increase the CPU limits in the pod's YAML configuration.
B) Decrease the memory limits in the pod's YAML configuration.
C) Restart the Kubernetes cluster.
D) Increase the CPU requests in the pod's YAML configuration.

Solutions: