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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q296-Q301):

NEW QUESTION # 296
What does setting the Spark configuration parameter spark.sql.shuffle.partitions impact?

A. The default level of parallelism for joins and aggregations
B. The serialization format of data
C. The memory allocation for executor instances
D. The compression codec used for shuffle files

Answer: A

Explanation:
The spark.sql.shuffe.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 # 297
How can you leverage Spark Streaming for real-time data processing and analytics?

A. By defining a streaming DataFrame with a window function.
B. By implementing custom logic for data ingestion, transformation, and output.
C. Both A and B.
D. By utilizing Structured Streaming with Kafka as the source and sink.

Answer: C

Explanation:
Spark Streaming offers two primary approaches: defining streaming DataFrames with window functions for micro-batching and utilizing Structured Streaming for end-to-end processing pipelines with sources like Kafka.

NEW QUESTION # 298
What challenge does schema inference aim to address when dealing with big data ecosystems?

A. The variety and complexity of data formats and structures
B. The need for large storage spaces to hold data schemas
C. Ensuring that all data is encrypted according to its inferred schema
D. Reducing the computational power required for data analysis

Answer: A

Explanation:
Schema inference primarily addresses the challenge of dealing with the variety and complexity of data formats and structures inherent in big data ecosystems. By automatically determining the structure of data, schema inference allows for more flexible and efficient processing of diverse datasets without the need for predefined schemas, thus tackling the issue of data heterogeneity.

NEW QUESTION # 299
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. Streaming data is automatically persisted to HDFS for historical analysis
C. Spark leverages micro-batching and checkpointing for persistence in streaming applications
D. Spark uses the same persistence mechanisms as batch processing for streaming data

Answer: C

Explanation:
Unlike batch processing A, streaming data is continuous. Spark utilizes micro-batching C to break down the stream into small, manageable batches, allowing for persistence techniques like updateStateByKey or checkpoint to be applied within these batches. Option B is incorrect as persistence is possible, and D is not always the case.

NEW QUESTION # 300
You need to update several rows in a large Iceberg table. Which of the following approaches would likely be the most efficient?

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

Answer: A

Explanation:
Iceberg supports record-level DELETE and INSERT operations for targeted updates. Options A and C are very inefficient for large tables. MERGE INTO is available, but less efficient than direct deletes and inserts.

NEW QUESTION # 301
......

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