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Cloudera CDP-3002 Simulation Questions | New CDP-3002 Exam Pattern
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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q190-Q195):
NEW QUESTION # 190
In a PySpark application running on Kubernetes, if one of the Executors fails to execute a task due to a node failure, what action does the Spark Driver take?
- A. It immediately shuts down the entire application.
- B. It reschedules the task on a different Executor.
- C. It requests Kubernetes to allocate more resources to the failed Executor.
- D. It pauses the application until the failed Executor is back online.
Answer: B
Explanation:
When a task fails on an Executor, possibly due to a node failure, the Spark Driver's responsibility is to reschedule the task on a different Executor. This is part of the Driver's task monitoring and execution management duties, ensuring the application's continued execution despite individual task failures.
NEW QUESTION # 191
Consider the following code snippet:# Sample DataFrame (assuming it exists) df = spark.createDataFrame(...)
# Attempt to explode a nested array column (fix the error)
df_exploded = df.withColumn("items", F.explode(df["items"]))
df_exploded.show()
What is the error in this code, and how can it be fixed?
- A. The error is attempting to modify the original DataFrame in-place. Fix: Use a separate variable to store the exploded DataFrame.
- B. The error is using withColumn instead of a dedicated method for exploding arrays. Fix: Use df.withColumnExploded. (This function doesn't exist in Spark)
- C. The error is missing parentheses around the column name in the explode function. Fix: F.explode(df("items"))
- D. There is no error in the code snippet.
Answer: A
NEW QUESTION # 192
As a data engineer, you are working with PySpark to analyze data stored in an HDFS cluster. You need to read a CSV file into a Spark DataFrame. Which of the following code snippets correctly reads the data from HDFS into a DataFrame?
- A.

- B.

- C.

- D.

Answer: B
Explanation:
The correct way to read a CSV file from HDFS into a Spark DataFrame in PySpark is using 'spark.read.format("csv").load("hdfs:///path/to/data.csv")*. This method specifies the format as CSV and loads the file from the specified HDFS path.
NEW QUESTION # 193
When using Cloudera's Command Line Interface (CLI), which of the following tasks can be performed?
- A. Modifying the source code of Cloudera Manager.
- B. Only data ingestion tasks can be automated.
- C. Cluster monitoring and diagnostics cannot be performed.
- D. Managing users and setting permissions on HDFS.
Answer: D
Explanation:
Using Cloudera's CLI allows for a wide range of administrative tasks, including managing users and setting permissions on HDFS. This capability is crucial for maintaining security and governance within a big data environment, enabling administrators to efficiently manage access controls and permissions from the command line without the need for direct interaction with the Cloudera Manager UI.
NEW QUESTION # 194
You're given a DataFrame containing information about flights, including columns "origin", "destination", and "delay_minutes". How can you find the top 5 origin airports with the most delayed flights on average?
- A. Leverage Spark SQL's RANK function along with windowing to identify top 5 origins
- B. Use groupBy and avg on "delay_minutes", then sort by the average in descending order and limit to top 5
- C. Use Spark's machine learning library (MLIiB. for ranking and classification
- D. Implement a custom function to calculate average delays for each origin and then sort and filter
Answer: B
Explanation:
Option A provides a straightforward and efficient approach. Here's the code:top_delayed_origins_df = df.groupBy("origin").agg(avg("delay_minutes").alias("avg_delay")) sort("avg_delay", ascending=FalsE. .1imit(5) top_delayed_origins_df.show()
NEW QUESTION # 195
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