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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q312-Q317):

NEW QUESTION # 312
What are the potential challenges associated with schema inference in data processing pipelines?

A. Handling complex nested structures and arrays
B. Performance overhead due to schema discovery
C. The need for manual schema updates
D. Increased storage costs for schema metadata
E. Inaccuracies in inferred schemas leading to data processing errors

Answer: A,B,E

Explanation:
Schema inference can introduce performance overhead as the system needs to analyze the data to determine its structure. Inaccuracies in the inferred schema may occur, especially with complex data types or when the data does not follow a consistent format, leading to potential errors in data processing. Handling complex nested structures and arrays can also present challenges, as the inference mechanism must correctly identify these elements within the data.

NEW QUESTION # 313
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. Use Cloudera Sentry for role-based access control and data masking
B. Implement custom encryption/decryption logic within your application
C. Store sensitive data directly in HDFS without encryption
D. Leverage Cloudera Knox Gateway for secure access to Spark applications

Answer: A,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 # 314
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. DataFrames provide more fine-grained control over partitioning and parallelism.
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: 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 # 315
Which tool or API is primarily used for monitoring and inspecting the performance of Spark applications in real-time?
A Spark History Server

A. Apache Ambari
B. Hadoop YARN ResourceManager UI
C. Spark Web UI

Answer: B

Explanation:
The Spark Web UI is a monitoring tool that provides information about the execution of a Spark application. It gives insights into the scheduler stages and tasks, executor usage, storage usage, and environmental settings. It is accessible while the application is running and is the primary tool for real-time performance monitoring. The Spark History Server helps in inspecting the application performance after it has completed. Hadoop YARN ResourceManager UI and Apache Ambari are more generally used for cluster management and monitoring, not specifically for real-time Spark application performance.

NEW QUESTION # 316
How can you use Apache Airflow to ensure a data quality check stops the workflow if it fails, without failing subsequent tasks that are not dependent on the data quality check?

A. Use the ShortCircuitOperator with a condition that returns False if the check fails.
B. Use the BranchPythonOperator to split the workflow conditionally.
C. Set trigger_rule='all_done' on subsequent tasks.
D. Use the TaskGroup with trigger_rule='one_failed' for the data quality check task.

Answer: A

Explanation:
The ShortCircuitOperator in Apache Airflow allows a task to conditionally skip following tasks in the workflow based on a condition. If the data quality check fails (i.e., the condition returns FalsE. , subsequent tasks directly dependent on it will be skipped, but it will not affect other parts of the workflow that do not depend on this check.

NEW QUESTION # 317
......

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Pdf CDP-3002 Dumps | CDP-3002 Valid Exam Voucher

Real Cloudera CDP-3002 Dumps PDF - Achieve Success In Exam

Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q312-Q317):

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