Cloudera CDP-3002 試験概要:

Cloudera CDP-3002 試験シラバストピック:

Cloudera CDP Data Engineer - Certification 認定 CDP-3002 試験問題：

問題 #1
You've discovered that a production Iceberg table has several corrupted data files. Which of the following actions could help address this issue?

A. Run the VACUUM procedure on the Iceberg table.
B. Restore the table to a previous snapshot using Iceberg's time travel feature.
C. Apply Iceberg's REMOVE ORPHAN FILES procedure.
D. Drop and recreate the Iceberg table from scratch.

問題 #2
In Spark, when is it most beneficial to use the repartitionByRange method?

A. When decreasing the number of partitions to reduce task scheduling overhead.
B. When sorting data within each partition by a specified column or set of columns is required.
C. When minimizing network traffic during shuffle operations is the primary concern.
D. When the data is highly skewed and a uniform distribution of data across partitions is required.

問題 #3
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. There is no error in the code snippet.
B. The error is missing parentheses around the column name in the explode function. Fix: F.explode(df("items"))
C. The error is attempting to modify the original DataFrame in-place. Fix: Use a separate variable to store the exploded DataFrame.
D. The error is using withColumn instead of a dedicated method for exploding arrays. Fix: Use df.withColumnExploded. (This function doesn't exist in Spark)

問題 #4
When creating a data pipeline in the Cloudera Data Engineering service, what is the primary file format used to define the pipeline steps and configuration?

A. Shell script
B. YAML
C. Python script
D. JSON

問題 #5
Which strategy is most effective for managing schema evolution in a big data application that relies on schema inference?

A. Completely disabling schema inference and relying on static schemas
B. Integrating schema registry services that track and manage schema versions
C. Using a single version of the schema for all data, regardless of changes
D. Manually updating the inferred schema for each data ingestion

解説：