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  1. Home
  2. Cloudera Certification
  3. CDP-3002 Exam
  4. Cloudera.CDP-3002.v2025-09-26.q117 Dumps
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Question 106

What are the potential challenges associated with schema inference in data processing pipelines?

Correct Answer: A,B,D
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.
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Question 107

Which of the following commands is used to install PySpark in your development environment?

Correct Answer: A
PySpark is a Python library and can be installed using pip, which is the package installer for Python. The correct command is 'pip install pyspark'.
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Question 108

How can "Explain Plan" help in optimizing query performance regarding data partitioning?

Correct Answer: B
An Explain Plan can demonstrate whether a query can benefit from partition pruning, which is a technique to skip over irrelevant partitions based on query conditions, thereby improving query performance by reducing the amount of data scanned.
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Question 109

How can you ensure that a set of tasks in an Airflow DAG are executed in parallel after a specific initial task is completed?

Correct Answer: D
The ]] (bitshift right) and (bitshift left) operators in Apache Airflow are used to define task dependencies within a DAG. To execute a set of tasks in parallel after an initial task, you can set the initial task to be upstream (using ]]) of all tasks intended to run in parallel. This ensures the parallel tasks only start after the completion of the initial task, leveraging Airflow's task dependency mechanism.
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Question 110

In Apache Airflow, what is the purpose of setting max_active_runs in a DAG's configuration?

Correct Answer: B
The max_active_runs setting in a DAG's configuration limits the number of DAG runs that can be executed concurrently. This is useful for controlling resource utilization and ensuring that the Airflow instance does not get overwhelmed by too many parallel executions of the same DAG.
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