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CDP-3002 Exam Guide - CDP-3002 Test Questions & CDP-3002 Exam Torrent
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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q49-Q54):
NEW QUESTION # 49
In Apache Spark, which storage level is recommended for caching data that is accessed frequently but is too large to fit in memory?
- A. MEMORY AND DISK
- B. MEMORY ONLY
- C. DISK ONLY
- D. OFF HEAP
Answer: A
Explanation:
The MEMORY_AND_DISK storage level is recommended for caching data that is frequently accessed but too large to fit entirely in memory. This level stores the data in memory as much as possible and spills the remainder to disk, providing a good balance between performance and resource utilization.
NEW QUESTION # 50
You're tasked with creating a DAG in Airflow that orchestrates a complex data processing workflow. What are some key considerations for designing an effective DAG?
- A. Implement extensive logging within each task for detailed information, even if it slows down execution.
- B. Break down the workflow into smaller, modular tasks with clear dependencies.
- C. Configure the DAG to run continuously without any specific scheduling or triggering mechanism.
- D. Use a single DAG for the entire workflow, regardless of its complexity.
Answer: B
NEW QUESTION # 51
A PySpark application is facing performance issues due to uneven distribution of data across the nodes. Which approach would best help in resolving this issue?
- A. Employ to redistribute data based on a key.
- B. Apply 'df.coalesce(Y to reduce the number of partitions.
- C. Use 'df.collect()'.
- D. Convert the DataFrame to an RDD and then back to a DataFrame.
Answer: A
Explanation:
Using in PySpark helps to redistribute the data more evenly based on the specified key column. This can alleviate issues caused by data skewness and improve the performance of distributed operations.
NEW QUESTION # 52
What is the role of a Spark driver in a distributed processing job?
- A. Coordinates tasks across the cluster
- B. Stores and processes intermediate data
- C. Performs computations on individual data partitions
- D. Manages communication between executors and workers
Answer: A
Explanation:
The driver program acts as the central entity, submitting jobs, scheduling tasks on executors, and managing dependencies between stages
NEW QUESTION # 53
You want to schedule your ETL pipeline to run daily at 5:00 AM. How can you configure the DAG's scheduling?
- A. Define a custom Python function to schedule the DAG at the desired time.
- B. Utilize Airflow triggers to initiate the DAG execution at 5:00 AM daily.
- C. Use the cron expression "daily at 5:00".
- D. Set the schedule_interval parameter to "0 5
Answer: D
Explanation:
Option A leverages the cron expression format in the schedule_interval parameter to specify daily execution at 5:00 AM. While other options might be used in specific scenarios, this is the most common and straightforward approach.
NEW QUESTION # 54
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