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[General] Latest CDP-3002 Test Blueprint & CDP-3002 Reliable Test Forum

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【General】 Latest CDP-3002 Test Blueprint & CDP-3002 Reliable Test Forum

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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q153-Q158):NEW QUESTION # 153
Your Spark application involves complex data transformations requiring multiple shuffles. How can you leverage Spark's in- memory caching mechanisms to improve performance?
A Use rdd.persist(StorageLevel.MEMORY_ONLY) for all intermediate RDDs
  • A. Leverage Spark SQL caching with materialized views
  • B. Tune Spark configuration parameters like spark.storage.memoryFraction
  • C. Implement custom caching logic using in-memory data structures
Answer: B
Explanation:
While custom caching B might be possible, Spark offers efficient solutions. Persisting intermediate RDDs in memory using StorageLevel.MEMORY_ONLY allows for faster access during subsequent shuffles. Additionally, materializing views in Spark SQL caches frequently used query results, improving performance for subsequent queries with the same data.

NEW QUESTION # 154
Which Airflow feature allows you to template your tasks, enabling dynamic generation of task parameters such as table names for data quality checks?
  • A. Jinja Templating
  • B. Variables
  • C. Airflow Plugins
  • D. XComs
Answer: A
Explanation:
Jinja Templating is a powerful feature in Apache Airflow that allows you to dynamically generate task parameters, including SQL queries, file paths, and other task arguments. This is especially useful for data quality checks where you might need to pass different table names or SQL queries to a task based on the execution date or other workflow parameters.

NEW QUESTION # 155
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. Spark uses the same persistence mechanisms as batch processing for streaming data
  • C. Streaming data is automatically persisted to HDFS for historical analysis
  • D. Spark leverages micro-batching and checkpointing for persistence in streaming applications
Answer: D
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 # 156
Why is it recommended to use the DataFrame API over RDDs for most data processing tasks in Spark?
  • A. DataFrames provide more fine-grained control over partitioning and parallelism.
  • B. DataFrames automatically optimize queries using the Catalyst optimizer and Tungsten execution engine.
  • C. RDDs are deprecated and will be removed in future versions of Spark.
  • D. DataFrames require less memory and compute resources compared to RDDs.
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 # 157
Your Spark application encounters performance issues when reading data from a large Hive table. What potential optimization techniques can you explore?
  • A. Use a different file format for the Hive table, like CSV, for faster parsing
  • B. Increase the number of Spark executors without further optimization
  • C. Leverage partition pruning to only read relevant data from the table
  • D. Implement custom data compression logic within Spark for improved storage efficiency
Answer: C
Explanation:
While increasing executors A might help, it's not the most targeted approach. Changing file format B might have downsides. Partition pruning C allows Spark to only access relevant data partitions based on the query, significantly reducing the amount of data scanned and improving efficiency. Custom compression D adds complexity and might not be the first optimization to consider.

NEW QUESTION # 158
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