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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q66-Q71):

NEW QUESTION # 66
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 using withColumn instead of a dedicated method for exploding arrays. Fix: Use df.withColumnExploded. (This function doesn't exist in Spark)
B. The error is attempting to modify the original DataFrame in-place. Fix: Use a separate variable to store the exploded DataFrame.
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: B

NEW QUESTION # 67
When persisting an RDD in Spark, what factors should you consider when choosing the storage level (e.g., MEMORY ONLY, MEMORY AND_DISK)?

A. Only consider the size of the RDD
B. Balance memory usage with fault tolerance needs
C. Prioritize keeping data in memory for faster access
D. All of the above

Answer: D

Explanation:
Choosing the storage level involves considering various factors. You should consider the size of the RDD A, as large datasets might not fit entirely in memory. Prioritizing memory usage B improves access speed but might not be suitable for fault-tolerance. Balancing memory usage with disk persistence C ensures data availability even in case of node failures, offering a compromise between performance and fault tolerance.

NEW QUESTION # 68
You're working with a team of data engineers who collaborate on developing and maintaining Airflow DAGs. How can you ensure version control and maintain a consistent development workflow?

A. Store all DAG code in a shared folder on a local file system, relying on manual coordination between team members.
B. Grant full access to the Airflow web Ul to all team members for code modifications, regardless of their roles.
C. Implement a custom workflow management system outside of Airflow for managing DAG development.
D. Utilize a version control system like Git to track changes in the DAG code and enable collaborative development.

Answer: D

Explanation:
While option A might be necessary for specific metrics, option B and D highlight Airflow's built-in capabilities:Airflow Metrics: Allow you to track DAG run times, task execution times, and success/failure rates, providing insights into performance. Alerts and Notifications (Optional): Can be configured to trigger upon events like DAG failures or performance thresholds being breached, enabling proactive monitoring and troubleshooting.

NEW QUESTION # 69
In optimizing join operations, what role does the Catalyst optimizer in Spark play, specifically regarding join strategies?

A. It disables all optimizations by default to provide consistent performance across different datasets.
B. It dynamically selects the most appropriate join strategy based on the query execution plan.
C. It exclusively uses broadcast join for all operations to minimize execution time.
D. It manually requires the developer to specify the join strategy for each operation.

Answer: B

Explanation:
The Catalyst optimizer in Spark dynamically selects the most appropriate join strategy based on the query execution plan and the characteristics of the data involved. It considers factors such as the size of the datasets, their distribution, and the available resources to choose between available join strategies (e.g., broadcast join, shuffle hash join, sort merge join) to optimize the query's performance.

NEW QUESTION # 70
Which of the following is a critical consideration when deciding between using a sort merge join and a shuffle hash join in a distributed data processing system like Spark?

A. The availability of secondary indexes on the join keys
B. The version of the Spark cluster being used
C. The relative size of the datasets and the available memory on each executor
D. The network latency between nodes in the cluster

Answer: C

Explanation:
The decision between using a sort merge join and a shuffle hash join critically depends on the relative size of the datasets involved in the join and the available memory on each executor. Shuffle hash join can be more efficient when one of the datasets fits in memory, as it hashes that dataset for faster lookup. In contrast, sort merge join is generally better for larger datasets, as it doesn't require one dataset to fit entirely in memory but may incur more I/O cost due to sorting and shuffling.

NEW QUESTION # 71
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