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NEW QUESTION # 313
In the context of schema inference, which component of the Apache Spark ecosystem plays a crucial role in enabling the exploration of semi-structured data?
Answer: A
Explanation:
The DataFrame API in Apache Spark is instrumental in schema inference and exploring semi-structured data. DataFrames provide a higher-level abstraction that allows Spark to automatically infer the schema of the data based on its structure, enabling more efficient processing and analysis of semi-structured data compared to RDDs, which are lower-level and require more explicit structure definition.
NEW QUESTION # 314
Your ETL pipeline processes sensitive dat
a. How can you ensure data security within Airflow?
Answer: A
Explanation:
Option C highlights the crucial security practice of encrypting sensitive data at rest (e.g., in databases) and in transit (e.g., during network communication). This protects confidential information from unauthorized access.
NEW QUESTION # 315
Given a DataFrame containing product information with columns "product_id", "name", and "price", how can you filter and sort the DataFrame to only include products with a price greater than $50 and sort them by price in descending order?
Answer: A
Explanation:
Chaining conditions within the filter method allows for concise and efficient filtering. Option B demonstrates this approach:filtered_sorted_df = ] 50).sort("price", ascending=FalsE.
This code first filters the DataFrame to only include rows where the "price" is greater than 50 and then sorts the resulting DataFrame by the "price" column in descending order (using ascending=FalsE.
NEW QUESTION # 316
Why are partitioned tables beneficial in Hive for large datasets?
Answer: B
Explanation:
Partitioned tables are beneficial because they allow Hive to perform faster query processing by scanning only the relevant partitions of data rather than the entire dataset. This approach significantly reduces the amount of data read from disk during query execution, improving performance, especially with large datasets.
NEW QUESTION # 317
When deploying a Spark application on Cloudera Data Engineering (CDE. service managed on Kubernetes, which command would you use to specify resource requirements such as CPU and memory for the Spark executor?
Answer: C
Explanation:
When deploying a Spark application on CDE service, which is managed on Kubernetes, you would typically use the CDE CLI to submit and configure your Spark jobs. The correct command would involve the cde spark submit command along with -conf flags to specify configuration options such as executor memory and cores. This approach is tailored to the CDE environment, leveraging its CLI for configuration rather than directly interfacing with Kubernetes (kubectl) or using the generic spark-submit command.
NEW QUESTION # 318
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