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Cloudera CDP Data Engineer - Certification Sample Questions:

1. You need to create a new Hive table from a Spark DataFrame. What are the different approaches you can consider?

A) All of the above
B) Convert the DataFrame to a temporary table and then use HiveQL commands to create a permanent table
C) Directly write the DataFrame to a directory in HDFS and define a corresponding Hive table schema
D) Use the DataFrame.write.saveAsTable("table_name") method with appropriate options

2. Consider a PySpark application that calculates the count of rows in a CSV file. The main application file is 'count_rows.py', and it uses a custom library 'data_utils.py'. After packaging, which 'spark-submit' command correctly submits this job including the custom library?

A) 'spark-submit -py-files data utils.py count_rows.py'
B) 'spark-submit --archives data_utils.py count_rows.py'
C) 'spark-submit --files data_utils.py count_rows.py'
D) 'spark-submit --jars data utils.py count_rows.py'

3. Which strategy is most effective for managing schema evolution in a big data application that relies on schema inference?

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

4. When performing a bucketed join between two tables, what must be true for the join to be executed as a map-side join, thereby maximizing performance?

A) Both tables must be partitioned, not bucketed, on the join columns.
B) Both tables must be bucketed on the join columns with a different number of buckets.
C) Both tables must be bucketed on the join columns with the same number of buckets and the same hash function.
D) Only one table needs to be bucketed, regardless of the bucket count.

5. You're working with a complex data pipeline involving Spark and Hive, and you need to monitor its performance and identify potential bottlenecks. Which tools and techniques can you employ for effective monitoring?

A) Implement custom instrumentation code within your Spark application
B) Leverage Spark's web UI and Hive logs for basic information
C) Utilize YARN resource manager and Spark/Hive metrics for detailed monitoring
D) Manually analyze Spark and Hive logs after job completion

Solutions: