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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q318-Q323):

NEW QUESTION # 318
What is the primary purpose of the Cost-Based Optimizer (CBO) in the context of Cloudera's Optimization Framework?

A. To minimize the amount of data processed
B. To choose the data storage format
C. To distribute data evenly across the cluster
D. To decide the best execution plan based on the cost of operations

Answer: D

Explanation:
The Cost-Based Optimizer (CBO) aims to select the most efficient execution plan for a query based on the cost associated with various operations. It considers factors like data size, data locality, and the computational cost of operations to optimize the query execution.

NEW QUESTION # 319
Consider the following code snippet:# Sample DataFrame (assuming it exists) df = spark.createDataFrame(...)
# Attempt to add a new column with a case-when expression (fix the error) df = df.withColumn("category", F.when(df["price"] ] 100, "Expensive").otherwise("Cheap")) df.show() What is the error in this code, and how can it be fixed?

A. There is no error in the code snippet.
B. The error is attempting to modify the original DataFrame in-place. Fix: Use df.withColumn to create a new DataFrame with the added column.
C. The error is missing parentheses around the conditions in the when function. Fix: F.when((df["price"] ] 100), "Expensive").otherwise("Cheap")
D. The error is using the wrong syntax for case-when expressions. Fix: Use SQL-like syntax with CASE WHEN and END.

Answer: B

Explanation:
The error lies in attempting to modify the original DataFrame in-place. The correct approach is to use withColumn to create a new DataFrame with the added column, as shown in the fixed code:df = df.withColumn("category", ] 100, "Expensive").otherwise("Cheap"))

NEW QUESTION # 320
When deploying a packaged PySpark application using 'spark-submit', which option is used to include the packaged dependencies?

A. --py-files
B. --packages
C. -files
D. --jars

Answer: A

Explanation:
The '-py-files' option is used with 'spark-submit' to include additional Python files or packages (like .zip, .egg, or .py files) necessary for the application, such as the packaged dependencies in a wheel file.

NEW QUESTION # 321
You are developing a PySpark application that processes a large dataset. Your application involves a transformation that requires shuffling data across the Executors. Which of the following responsibilities of the Spark Driver is most directly involved in this process?

A. Monitoring the progress and status of tasks on the Executors.
B. Interpreting the user application and creating an execution plan.
C. Managing the shuffling and aggregation of data across Executors.
D. Managing the lifecycle of Executor pods in Kubernetes.

Answer: C

Explanation:
The Spark Driver is responsible for managing operations that involve shuffling data. In this scenario, where the PySpark application includes a transformation requiring data shuffling, the Driver coordinates the shuffling and aggregation of data across different Executors to ensure correct execution of the transformation.

NEW QUESTION # 322
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 # 323
......

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