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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q317-Q322):

NEW QUESTION # 317
What is the purpose of partitioning data in Spark?

A. To optimize data visualization
B. To enable parallel processing across multiple nodes
C. To improve data compression efficiency
D. To enforce data access control

Answer: B

Explanation:
Partitioning divides data into smaller, independent subsets that can be processed concurrently by different executors, leading to faster processing times.

NEW QUESTION # 318
What does setting the Spark configuration parameter 'spark.sql.shuffle.partitions' impact?
A The default level of parallelism for joins and aggregations

A. The memory allocation for executor instances
B. The serialization format of data
C. The compression codec used for shuffle files

Answer: B

Explanation:
The 'spark.sql.shuffle.partitions' configuration parameter sets the number of partitions to use when shuffling data for joins or aggregations, which directly impacts the level of parallelism and the performance of these operations. A high number of partitions can lead to smaller tasks, potentially improving parallelism but at the cost of increased scheduling overhead. Conversely, too few partitions can lead to fewer, larger tasks, possibly causing out-of-memory errors or underutilizing the cluster.

NEW QUESTION # 319
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. RDDs are deprecated and will be removed in future versions of Spark.
C. DataFrames provide more fine-grained control over partitioning and parallelism.
D. DataFrames require less memory and compute resources compared to RDDs.

Answer: A

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 # 320
How does Spark achieve fault tolerance during distributed processing?

A. By implementing automatic checkpointing of intermediate results
B. By replicating data across all nodes in the cluster
C. null
D. By restarting failed tasks on different nodes

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 # 321
You need to securely store sensitive data within your Spark application and access it only from authorized nodes. How can you leverage Cloudera security features to achieve this?

A. Store sensitive data directly in HDFS without encryption
B. Implement custom encryption/decryption logic within your application
C. Leverage Cloudera Knox Gateway for secure access to Spark applications
D. Use Cloudera Sentry for role-based access control and data masking

Answer: C,D

Explanation:
Storing data without encryption A is insecure. While custom encryption B is possible, it adds complexity and potential security risks. Combining Cloudera Sentry's access control with data masking and Knox Gateway's secure authentication ensures that only authorized users can access sensitive data within your Spark application.

NEW QUESTION # 322
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