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Cloudera CDP Data Engineer - Certification Sample Questions:

1. You notice a significant performance overhead when persisting a large RDD to disk. What potential factors could contribute to this, and how can you mitigate them?

A) All of the above
B) Using HDFS as the storage location instead of local disks
C) Inefficient serialization format for the RDD data
D) Insufficient disk space on the cluster nodes

2. 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

3. You're implementing a data quality process for Iceberg tables in CDP Which of the following Iceberg features can help you enforce constraints and detect data anomalies? (Choose two)

A) Metadata tables
B) Snapshots
C) Manifest files
D) Partitioning
E) Iceberg table constraints (not yet fully supporteD.

4. You need to enable secure access to Iceberg tables in CDP, controlling permissions at the table, column, and row level. Which of the following approaches would you investigate?

A) Integrate with CDP's Kerberos-based authentication system.
B) Implement custom Spark IJDFs to enforce authorization logic.
C) Configure role-based access control (RBA directly within Iceberg table properties.
D) Leverage Apache Ranger's integration with Iceberg.

5. How can you utilize Spark SQL for complex data analysis involving joins and aggregations on large datasets?

A) By using Spark ML libraries for machine learning tasks on data.
B) By implementing custom logic in RDDs for data manipulation.
C) Both A and B.
D) By directly writing SQL queries against distributed data in Spark.

Solutions: