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Cloudera CDP Data Engineer - Certification Sample Questions:

1. In the context of dynamic partitioning in Hive, what challenge does the use of too many dynamic partitions in a single load operation present?

A) It enhances data security by segmenting data into finer-grained partitions.
B) It can lead to an excessive number of small files, negatively impacting HDFS performance.
C) It automatically disables the execution of map-reduce jobs.
D) It simplifies the management of Hive metadata, reducing the load on the NameNode.

2. You need to update several rows in a large Iceberg table. Which of the following approaches would likely be the most efficient?

A) Read the entire table, make changes in Spark, and overwrite the table.
B) Employ Iceberg's time travel feature to revert to an older snapshot and reapply changes.
C) Use the Iceberg MERGE INTO syntax for row-level updates.
D) Use the Iceberg DELETE and INSERT operations together.

3. You encounter an error during a data quality check within your Airflow DAG. How can you access detailed information about the error to aid in troubleshooting?

A) The Airflow logs (scheduler, worker) might contain relevant error messages from the failing task.
B) All of the above
C) The Airflow web UI task details page might display error messages related to the failed check.
D) You can directly inspect the custom Python script used for the check to identify potential issues.

4. How does Apache NiFi support schema inference during data flow management?

A) Utilizing external schema repositories exclusively for schema management
B) Through processors that automatically detect data formats and schemas
C) By enforcing static schemas for all data flows
D) By manually specifying schemas for each data source

5. You're working with a Spark application that processes streaming data in real-time. How does Spark handle persistence in this context?

A) Streaming data cannot be persisted due to its continuous nature
B) Spark uses the same persistence mechanisms as batch processing for streaming data
C) Streaming data is automatically persisted to HDFS for historical analysis
D) Spark leverages micro-batching and checkpointing for persistence in streaming applications

Solutions: