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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q281-Q286):
NEW QUESTION # 281
When leveraging caching in Spark, which scenario illustrates the use of the MEMORY ONLY SER storage level most effectively?
Answer: A
Explanation:
The MEMORY_ONLY_SER storage level caches data in memory but in serialized form, which is more space-efficient than storing data in deserialized form (MEMORY_ONLY). This makes MEMORY ONLY_SER most effective for caching large datasets that do not fit into memory in their deserialized form, as serialization reduces the size of the data at the cost of requiring deserialization upon access, which can add computational overhead.
NEW QUESTION # 282
If you want to set a minimum and maximum number of Executor pods for a Spark application in Kubernetes, which pair of PySpark configuration settings would you use?
Answer: A
Explanation:
The settings 'spark.dynamicAllocation.minExecutors' and 'spark.dynamicAllocation.maxExecutors' are used to define the minimum and maximum number of Executor pods that can be dynamically allocated in a Spark application running on Kubernetes.
NEW QUESTION # 283
You need to design your Airflow DAG for data quality checks to be scalable and manageable as the number of datasets and checks grows. How can you achieve this?
Answer: B,C
Explanation:
While option C might be relevant for handling transient errors, option B and D combined offer a robust approach:'BranchPythonOperator': Allows you to evaluate the outcome of the data quality checks (success or failurE. within the 'PythonOperator'. Conditional Branching: Based on the check results, the DAG can either proceed with downstream tasks (success) or trigger error handling routines if the checks fail. Automatic Retries (optional): Configuring retries for the data quality checks can be added as a safeguard in case of temporary issues (option D.
NEW QUESTION # 284
You want to write the results of a Spark DataFrame operation to a Parquet file for efficient storage and retrieval. What approach can you achieve this efficiently?
Answer: A
Explanation:
While text files A are not efficient for Parquet, option B involves unnecessary steps. Spark DataFrames provide a dedicated method write.parquet() C for efficient Parquet file generation. Option D is mainly used for creating Hive tables, not DataFrames within Spark.
NEW QUESTION # 285
You are optimizing a SparkSQL query in your PySpark application running on Kubernetes. The query involves a join operation between a large DataFrame and a much smaller DataFrame. To minimize shuffling and optimize network utilization, which join strategy would you likely use?
Answer: D
Explanation:
In SparkSQL, when joining a large DataFrame with a significantly smaller one, a broadcast join is often the best choice. It broadcasts the smaller DataFrame to all nodes, reducing the need for shuffling the larger DataFrame across the network, thus optimizing network utilization.
NEW QUESTION # 286
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