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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q139-Q144):

NEW QUESTION # 139
You want to schedule your ETL pipeline to run daily at 5:00 AM. How can you configure the DAG's scheduling?

A. Define a custom Python function to schedule the DAG at the desired time.
B. Use the cron expression "daily at 5:00".
C. Set the schedule_interval parameter to "0 5
D. Utilize Airflow triggers to initiate the DAG execution at 5:00 AM daily.

Answer: C

Explanation:
Option A leverages the cron expression format in the schedule_interval parameter to specify daily execution at 5:00 AM. While other options might be used in specific scenarios, this is the most common and straightforward approach.

NEW QUESTION # 140
What are the potential trade-offs to consider when using checkpointing in Spark applications?

A. Checkpointing always improves performance and has no drawbacks
B. Checkpointing requires manual configuration and can be error-prone
C. All of the above
D. Checkpointing introduces overhead for storing and recovering data, impacting performance

Answer: C,D

Explanation:
While checkpointing offers benefits, it comes with trade-offs. It adds overhead for storing and recovering data, potentially impacting performance B. Configuring checkpoints effectively is crucial to avoid unnecessary overhead. Option A is incorrect, and while configuration is involved, it's not inherently error-prone C when done carefully.

NEW QUESTION # 141
You are deploying a Spark application on Kubernetes and need to specify the amount of memory allocated to each Executor. In your PySpark code, which configuration setting will you use?

A. 'spark.driver.memory'
B. 'spark.executor.memory'
C. 'spark.executor.instances'
D. 'spark.executor.memoryoverhead'

Answer: B

Explanation:
The configuration 'spark.executor.memory' is used to specify the amount of memory to be allocated to each Spark Executor in a Spark application. This setting is crucial for ensuring efficient memory management for the Executors running in Kubernetes pods.

NEW QUESTION # 142
How do bucketed tables in Hive improve query performance?

A. By organizing data into fixed-size segments, or buckets, based on a hash function of a column
B. By encrypting data to reduce 1/0 operations
C. By replicating data across multiple nodes to improve access speed
D. By compressing data automatically to reduce storage space

Answer: A

Explanation:
Bucketed tables in Hive improve query performance by organizing data into fixed-size segments, or buckets, based on a hash function of a column. This organization enables more efficient data access for operations like joins and aggregations by reducing the amount of data that needs to be scanned and allowing for more efficient distribution of data across the cluster.

NEW QUESTION # 143
Which of the following commands is used to install PySpark in your development environment?

A. npm install pyspark
B. brew install pyspark
C. yarn add pyspark
D. pip install pyspark

Answer: D

Explanation:
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'.

NEW QUESTION # 144
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