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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q13-Q18):
NEW QUESTION # 13
How does Spark achieve fault tolerance during distributed processing?
Answer: C
Explanation:
Spark employs a combination of data lineage tracking, checkpointing, and task re-execution to ensure job completion even in the event of node failures.
NEW QUESTION # 14
You need to design a DAG that can be easily monitored and visualized for performance insights. How can you achieve this?
Answer: A,B
NEW QUESTION # 15
Which Spark SQL join strategy is selected by default when the Catalyst optimizer cannot determine the size of tables involved in a join?
Answer: A
Explanation:
SortMergeJoin is the default join strategy selected by Spark's Catalyst optimizer when it cannot determine the size of the tables involved in the join. This strategy is generally more scalable than other join algorithms, as it does not rely on one table fitting into memory (like BroadcastHashJoin) and is more efficient than ShuffleHashJoin for large datasets.
NEW QUESTION # 16
In Hive, what impact does setting the hive.exec.dynamic.partition.mode to nonstrict have on dynamic partitioning operations?
Answer: D
Explanation:
Setting hive.exec.dynamic.partition.mode to nonstrict in Hive configuration allows for the creation of new partitions dynamically based on the input data values during data insertion operations. This mode removes the restriction that requires at least one static partition to be specified in the query, thereby providing greater flexibility in managing and organizing data into partitions based on the actual values encountered at runtime.
NEW QUESTION # 17
Why is it recommended to use the DataFrame API over RDDs for most data processing tasks in Spark?
Answer: B
Explanation:
The recommendation to use DataFrames (or Datasets) over RDDs is primarily due to the performance optimization benefits offered by the Catalyst optimizer and Tungsten execution engine. These components automatically optimize Spark SQL queries, improving execution efficiency and performance. DataFrames provide a higher-level abstraction with optimized storage and execution plans, which RDDs lack. Option A is incorrect as DataFrames and RDDs offer different levels of control over partitioning and parallelism. Option C is misleading; RDDs are not deprecated and continue to be a core feature of Spark for scenarios requiring fine-grained control over distributed computing. Option D oversimplifies the comparison; while DataFrames can be more efficient due to optimization, the primary advantage is not just reduced resource usage but also the automatic query optimization.
NEW QUESTION # 18
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