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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q51-Q56):
NEW QUESTION # 51
Which Airflow operator is best used for executing a Python function as part of a DAG?
Answer: C
Explanation:
The PythonOperator is specifically designed to execute a Python function or callable as part of an Airflow DAG. This operator allows for extensive customization and integration of Python code within your workflows, making it the ideal choice for tasks that require executing Python functions.
NEW QUESTION # 52
In Apache Airflow, what is the purpose of setting max_active_runs in a DAG's configuration?
Answer: D
Explanation:
The max_active_runs setting in a DAG's configuration limits the number of DAG runs that can be executed concurrently. This is useful for controlling resource utilization and ensuring that the Airflow instance does not get overwhelmed by too many parallel executions of the same DAG.
NEW QUESTION # 53
What challenge does schema inference aim to address when dealing with big data ecosystems?
Answer: A
Explanation:
Schema inference primarily addresses the challenge of dealing with the variety and complexity of data formats and structures inherent in big data ecosystems. By automatically determining the structure of data, schema inference allows for more flexible and efficient processing of diverse datasets without the need for predefined schemas, thus tackling the issue of data heterogeneity.
NEW QUESTION # 54
How can you leverage the Cloudera Data Engineering service to monitor the performance and health of your data pipelines?
Answer: B
Explanation:
While options a and b might seem feasible, they lack efficiency and fault tolerance for complex pipelines. Option c is the most suitable approach. Here's why:Stages and steps: Breaking down the pipeline into stages and steps allows for modularity and easier management. Spark operators: Utilizing Spark operators within the pipeline definition enables efficient data processing using Spark's capabilities. Retries and error handling: Implementing retries and error handling mechanisms ensures the pipeline can recover from failures and continue execution automatically.
NEW QUESTION # 55
Considering the dynamic nature of data workloads, how can Spark's dynamic resource allocation feature impact caching strategies?
Answer: B
Explanation:
Spark's dynamic resource allocation can impact caching strategies by potentially evicting cached data when executors are removed due to reduced workload. This feature adjusts the cluster size based on the workload, adding or removing executors dynamically. While it optimizes resource utilization, it may lead to the eviction of cached data if executors holding that data are decommissioned, which can affect performance if the data needs to be recomputed or fetched again.
NEW QUESTION # 56
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