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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q315-Q320):
NEW QUESTION # 315
Your Spark application involves a complex data pipeline with multiple dependent stages. How can you configure Spark to handle failures gracefully and ensure data consistency across the pipeline?
Answer: C
Explanation:
Option A can lead to cascading failures, while custom error handling B might be complex. Relying solely on automatic retries D might not be sufficient for complex pipelines. Checkpointing allows saving intermediate data periodically, enabling recovery from failures by reprocessing only affected stages, improving efficiency and data consistency.
NEW QUESTION # 316
You're tasked with optimizing an existing Airflow DAG that processes large datasets daily. The DAG has multiple tasks, some of which frequently fail due to memory constraints on the worker nodes. Which approach would best mitigate this issue without upgrading hardware?
Answer: D
Explanation:
The Pool feature in Airflow allows you to limit the number of tasks running concurrently in a given pool of resources, which can help manage memory usage and prevent workers from being overwhelmed by memory-intensive tasks. By assigning memory-intensive tasks to a pool with a limited number of slots, you ensure that only a certain number of these tasks can execute at the same time, thereby managing the overall memory consumption on worker nodes. Increasing priority_weight affects task scheduling order but does not manage resource constraints, retrying tasks does not address the root cause of memory issues, and splitting the DAG might help organizational clarity but does not directly address concurrent memory usage.
NEW QUESTION # 317
You're developing a Spark application with multiple stages, and you want to ensure that later stages only start processing after all data from the previous stage is complete. How can you achieve this dependency management in Spark?
Answer: B
Explanation:
Spark automatically tracks the lineage of data across transformations. By defining stages in your application logic, Spark ensures that downstream stages only start processing once the upstream stage (and its lineagE. is complete, eliminating the need for manual dependency management.
NEW QUESTION # 318
In Spark, what is the advantage of using the 'coalesce' method over the 'repartition' method when reducing the number of partitions in an RDD?
Answer: C
Explanation:
The 'coalesce' method is used to decrease the number of partitions in an RDD, and it does so without performing a full shuffle of the data. This makes it more efficient than 'repartition' for reducing the number of partitions because 'repartition' involves a full shuffle, which is more costly in terms of performance. 'coalesce' is particularly useful for optimization after filtering down a large dataset. Option A is incorrect as 'coalesce' is specifically designed for reducing partitions. Option B describes 'repartition', and Option D is factually incorrect.
NEW QUESTION # 319
In Apache Airflow, how can you dynamically generate tasks for each table in your database that needs a quality check?
Answer: D
Explanation:
Dynamic Task Mapping is a feature in Apache Airflow 2.0 and later that allows you to dynamically generate tasks at runtime. This can be used to create a separate quality check task for each table in your database by iterating over a list of tables and generating a task for each one, ensuring all necessary quality checks are automated and scalable.
NEW QUESTION # 320
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