FAST DOWNLOAD CLOUDERA LATEST CDP-3002 TEST VOUCHER ARE LEADING MATERIALS & HOT CDP-3002: CDP DATA ENGINEER - CERTIFICATION EXAM

Fast Download Cloudera Latest CDP-3002 Test Voucher Are Leading Materials & Hot CDP-3002: CDP Data Engineer - Certification Exam

Fast Download Cloudera Latest CDP-3002 Test Voucher Are Leading Materials & Hot CDP-3002: CDP Data Engineer - Certification Exam

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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q60-Q65):

NEW QUESTION # 60
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. Increased storage costs for schema metadata
  • D. Inaccuracies in inferred schemas leading to data processing errors
  • E. The need for manual schema updates

Answer: A,B,D

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 # 61
Which of the following is TRUE about using Explain Plans for performance tuning?

  • A. They can help identify operations that may become performance bottlenecks
  • B. They provide detailed network latency information
  • C. Explain Plans always show the actual runtime of a query
  • D. They are only useful for queries that involve joins

Answer: A

Explanation:
Explain Plans are valuable for identifying potential performance bottlenecks in a query, such as expensive joins or full table scans. They do not show actual runtime or detailed network latency and are useful for more than just queries involving joins.


NEW QUESTION # 62
In the context of schema inference, which component of the Apache Spark ecosystem plays a crucial role in enabling the exploration of semi-structured data?

  • A. RDD (Resilient Distributed Dataset)
  • B. DataFrame API
  • C. Spark SQL
  • D. Spark Streaming

Answer: B

Explanation:
The DataFrame API in Apache Spark is instrumental in schema inference and exploring semi-structured data. DataFrames provide a higher-level abstraction that allows Spark to automatically infer the schema of the data based on its structure, enabling more efficient processing and analysis of semi-structured data compared to RDDs, which are lower-level and require more explicit structure definition.


NEW QUESTION # 63
How can you dynamically generate tasks in Apache Airflow?

  • A. By using Python loops to create tasks during DAG parsing
  • B. By using the Variable feature to store dynamic configurations
  • C. By defining a Python function inside the DAG file
  • D. By using the Jinja templating engine

Answer: A

Explanation:
You can dynamically generate tasks in Apache Airflow by using Python loops within the DAG definition file. This allows for the programmatic creation of tasks based on dynamic inputs or configurations. While Jinja templating, Python functions, and Variables can influence task behavior and configurations, the direct method for dynamically generating tasks is through Python loops during DAG parsing.


NEW QUESTION # 64
In a meeting about Spark applications deployment on Kubernetes, your team discusses strategies for handling node failures. Which Kubernetes feature should be utilized to maintain high availability in case of node failures?
A Horizontal Pod Autoscaler

  • A. Pod Priority
  • B. ReplicaSets
  • C. Resource Quotas

Answer: A

Explanation:
ReplicaSets in Kubernetes are designed to maintain a stable set of replica Pods running at any given time, making them suitable for handling node failures and maintaining high availability. This ensures that if a node fails, Kubernetes can automatically redistribute the pods to other available nodes.


NEW QUESTION # 65
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