CDP-3002 EXAM FREE VCE DUMPS & NEWEST RELIABLE CDP-3002 TEST BLUEPRINT PASS SUCCESS

CDP-3002 Exam Free Vce Dumps & Newest Reliable CDP-3002 Test Blueprint Pass Success

CDP-3002 Exam Free Vce Dumps & Newest Reliable CDP-3002 Test Blueprint Pass Success

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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q297-Q302):

NEW QUESTION # 297
You need to read data from a Hive table into a Spark DataFrame. Which approach would be the most efficient?

  • A. Use the spark.read.parquet("/path/to/hive/table") method directly
  • B. Convert the Hive table to a managed table and then use spark.read.table("table_name")
  • C. Use the FROM table_name") method
  • D. Leverage Spark SQL capabilities with SELECT FROM table_name

Answer: D

Explanation:
While other options might work, option C is the most efficient and recommended approach. Spark SQL can directly access data from Hive tables using standard SQL syntax, transparently handling schema translation and ensuring optimal performance.


NEW QUESTION # 298
You're using Iceberg for streaming data ingestion. A critical use case requires exactly-once processing guarantees. Which considerations are most important for achieving this? (Choose two)

  • A. Carefully configuring the streaming source to handle retries and failures
  • B. Disabling Iceberg's snapshot isolation feature.
  • C. Using a data format like JSON, which is less prone to corruption than Parquet.
  • D. Leveraging a CDC (Change Data Captur tool integrated with Iceberg.
  • E. Enabling Iceberg's transactional writes

Answer: A,E

Explanation:
A). Iceberg transactions are key for exactly-once guarantees within the table itself.
B). The streaming source needs to cooperate to ensure no duplicate or missing data even with retries.
C). Data format has little impact on exactly-once; it's more about source reliability.
D). Snapshot isolation is desirable and unrelated to exactly-once.
E). CDC can be part of the solution but doesn't guarantee exactly-once on its own.


NEW QUESTION # 299
Which feature of Apache Avro facilitates dynamic schema inference during data serialization and deserialization?

  • A. Manual schema declaration
  • B. Schema versioning via external services
  • C. Strong data typing
  • D. Embedded schema within data files

Answer: D

Explanation:
Apache Avro facilitates dynamic schema inference through the embedding of schemas within data files. This feature allows each data file to carry its schema, enabling seamless serialization and deserialization processes without the need for external schema definitions. The embedded schema ensures that the data structure is always known, simplifying data processing and integration tasks.


NEW QUESTION # 300
You need to design an Airflow DAG that waits for a specific file to become available before proceeding with the downstream tasks. How can you achieve this dependency?

  • A. Configure the source system to notify Airflow when the file is ready for processing.
  • B. Use the File sensor operator to check for the file's existence and trigger downstream tasks upon its arrival.
  • C. Schedule the DAG to run periodically, hoping the file becomes available eventually.
  • D. Implement a custom loop within a Python operator to continuously check for the file until it appears.

Answer: B

Explanation:
The File sensor operator specifically addresses the need to wait for a file before proceeding in an Airflow DAG. Option A ensures that the downstream tasks only start after the desired file is present.


NEW QUESTION # 301
In the context of caching data for reuse in Spark, how does the Tungsten project contribute to enhancing memory management and execution efficiency?

  • A. By enforcing strict data typing to reduce runtime type checking overhead
  • B. By optimizing Spark's internal memory management and data processing operations
  • C. By rewriting Spark operations in a lower-level language for direct memory access
  • D. By providing a graphical interface for memory management

Answer: B

Explanation:
The Tungsten project contributes to enhancing memory management and execution efficiency in Spark by optimizing Spark's internal memory management and data processing operations. Tungsten introduces binary in-memory data representation and explicit control over memory allocation, reducing the overhead associated with garbage collection and improving cache efficiency. This optimization supports more efficient execution of operations and better utilization of system resources, particularly for tasks involving large datasets and complex computations.


NEW QUESTION # 302
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