AWS Data Engineer Free Practice Questions

The correct answer is C. “Use Amazon Athena to run SQL queries directly on the CSV files stored in S3.“

Explanation:

Amazon Athena is an interactive query service that enables users to analyze data directly in Amazon S3 using standard SQL. It is perfectly suited for the company’s requirement to run SQL queries on CSV files stored in S3.

Athena is serverless, so there’s no infrastructure to manage, and it works directly with data in various formats, including CSV, stored in S3. This makes it an ideal choice for querying large datasets without the need for data loading or transformation, simplifying the process and reducing operational overhead.

• Answer A “Use Amazon Redshift Spectrum to execute SQL queries on CSV files in S3” is incorrect. While Amazon Redshift Spectrum allows querying data in S3, it is part of the Redshift data warehousing service and requires managing a Redshift cluster. This might be more complex and costlier than necessary for directly querying CSV files, as required by the company.
• Answer B “Use AWS Data Pipeline to transfer CSV data from S3 to Amazon RDS for querying” is incorrect. AWS Data Pipeline is a web service for orchestrating data movement and transformations, but it requires transferring data to a database service like Amazon RDS. This adds an unnecessary step of moving data from S3 to RDS, which is not required with services like Athena that can query data directly in S3.
• Answer D “Use Amazon EMR with a Hive metastore to query the CSV files in the S3 bucket” is incorrect. Amazon EMR can be used for processing large datasets and can query data in S3. However, setting up EMR and configuring a Hive metastore is more complex and resource-intensive compared to using Athena. For direct SQL querying of data in S3, Athena is more straightforward and cost-effective.

References:
https://aws.amazon.com/blogs/big-data/analyzing-data-in-s3-using-amazon-athena/

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The correct answer is B. “Run an Athena CTAS query to convert the data to Parquet format with Snappy compression, partitioned by timestamp“.

Explanation:

Amazon Athena’s CTAS feature enables the conversion of data into columnar formats like Parquet, which is optimized for analytics. Parquet format, combined with Snappy compression, improves performance, and reduces costs by minimizing storage use and speeding up query times.

Partitioning the data by timestamp ensures efficient data management and retrieval, aligning with the need for daily removal of outdated data. This serverless solution does not require managing any infrastructure, making it cost-effective for transforming and optimizing data directly within S3.

• Answer A “Use AWS Glue to convert the .csv data to Apache Parquet and partition it by timestamp” is incorrect. AWS Glue is a fully managed ETL service that could perform the transformation. However, for a straightforward conversion and partitioning task, AWS Glue might introduce more complexity and cost compared to the simplicity and serverless nature of an Athena CTAS query.
• Answer C “Configure an EMR Spark job to transform the .csv files into Parquet, partitioned by the creation timestamp” is incorrect. Amazon EMR is a managed cluster platform that runs big data frameworks like Apache Spark. While EMR can handle the task, it is typically more cost-effective for large-scale, complex processing jobs. The overhead of setting up and managing an EMR cluster is not necessary for this scenario, making Athena a more cost-effective choice.
• Answer D “Set up a daily Lambda function to convert and partition the .csv data into Parquet format within S3” is incorrect. AWS Lambda allows running code in response to events, such as new data arrival. However, for data transformation tasks, particularly for large datasets, Lambda may face limitations in terms of execution time and memory. Athena’s CTAS is a more suitable tool for large-scale data transformation without these limitations.

References:

https://docs.aws.amazon.com/athena/latest/ug/ctas.html

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The correct answer is D: “Use AWS Lake Formation to define fine-grained data access policies and facilitate queries through supported AWS services“.

Explanation:

AWS Lake Formation simplifies and centralizes the setup of a secure data lake in AWS. It provides granular access control to data stored in Amazon S3, allowing organizations to define who has access to specific rows and columns within their datasets.

Lake Formation integrates seamlessly with Amazon Athena, Amazon Redshift Spectrum, and Apache Hive on Amazon EMR, providing the least operational overhead while meeting the organization’s access control requirements.

A. “Manage access through S3 bucket policies and IAM roles for row and column-level security” is incorrect. While S3 bucket policies and IAM roles can provide access control, they do not offer row-level and column-level security natively. This approach would require additional management and does not integrate as seamlessly with the querying services for granular permissions as AWS Lake Formation does.

B. “Deploy Apache Ranger on Amazon EMR for granular access control and utilize Amazon Redshift for querying” is incorrect. Apache Ranger can provide fine-grained access control, but it is typically used within the Hadoop ecosystem and requires additional setup and management when used on Amazon EMR. This approach would also not be as integrated with Amazon Redshift for querying without further configuration, leading to a higher operational overhead compared to using AWS Lake Formation.

C. “Use Redshift security groups and views for row and column-level permissions, querying with Athena and Redshift Spectrum” is incorrect. Amazon Redshift security groups and views can control access to data, but they are specific to the Redshift environment. This method would not provide the same level of granular access control across other services like Amazon Athena and would not apply to data stored in Amazon S3, which is the intended storage for the data lake. Redshift is also not typically used as the primary storage for a data lake due to cost and scalability considerations compared to Amazon S3.

References:

https://docs.aws.amazon.com/lake-formation/latest/dg/what-is-lake-formation.html

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