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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. For a dataset that is used multiple times in different transformations but is not large enough to warrant disk storage, which caching level in Apache Spark is most appropriate?

A) MEMORY AND DISK SER
B) MEMORY ONLY
C) DISK ONLY
D) MEMORY ONLY SER

2. You want to write the results of a Spark DataFrame back to a Hive table. How can you achieve this efficiently?

A) Use the DataFrame.write.saveAsTable("table_name") method
B) Use the hiveContext.sql("lNSERT INTO table_name SELECT FROM df') method
C) Implement a custom function to write individual rows to the Hive table
D) Convert the DataFrame to a temporary table and then use HiveQL operations

3. Why is it recommended to use the DataFrame API over RDDs for most data processing tasks in Spark?

A) DataFrames automatically optimize queries using the Catalyst optimizer and Tungsten execution engine.
B) DataFrames provide more fine-grained control over partitioning and parallelism.
C) RDDs are deprecated and will be removed in future versions of Spark.
D) DataFrames require less memory and compute resources compared to RDDs.

4. When performing a bucketed join between two tables, what must be true for the join to be executed as a map-side join, thereby maximizing performance?

A) Both tables must be partitioned, not bucketed, on the join columns.
B) Only one table needs to be bucketed, regardless of the bucket count.
C) Both tables must be bucketed on the join columns with a different number of buckets.
D) Both tables must be bucketed on the join columns with the same number of buckets and the same hash function.

5. You want to perform an Iceberg table join in CDP using Spark SQL, but you notice it's much slower than expected. What could be some of the reasons? (Choose two)

A) You're joining on a column with low cardinality (few distinct values).
B) Iceberg version mismatch between Spark and CDP.
C) Spark is using nested loop joins instead of broadcast hash joins due to table sizes.
D) One of the tables isn't partitioned effectively.
E) Spark's dynamic query execution is enabled.

Solutions: