Amazon Web Services Data-Engineer-Associate Exam With Confidence Using Practice Dumps

The data engineer needs to detect custom categories of PII within the data lake using AWS Glue DataBrew. While DataBrew provides standard data quality rules, the solution must support custom PII categories.

Option B: Implement custom data quality rules in DataBrew. Apply the custom rules across datasets.This option is the most efficient because DataBrew allows the creation of custom data quality rules that can be applied to detect specific data patterns, including custom PII categories. This approach minimizes operational overhead while ensuring that the specific privacy requirements are met.

Options A, C, and D either involve manual intervention or developing custom scripts, both of which increase operational effort compared to using DataBrew's built-in capabilities.

The company is using a data mesh architecture with AWS Lake Formation for governance and needs to share specific subsets of data with different teams (marketing and compliance) using Amazon Redshift Serverless.

Option A: Create views of the tables that need to be shared. Include only the required columns.Creating views in Amazon Redshift that include only the necessary columns allows for fine-grained access control. This method ensures that each team has access to only the data they are authorized to view.

Option E: Share the Amazon Redshift data share to the Amazon Redshift Serverless workgroup in the marketing team's account.Amazon Redshift data sharing enables live access to data across Redshift clusters or Serverless workgroups. By sharing data with specific workgroups, you can ensure that the marketing team and compliance team each access the relevant subset of data based on the views created.

Option B (creating a Redshift data share) is close but does not address the fine-grained column-level access.

Option C (creating a managed VPC endpoint) is unnecessary for sharing data with specific teams.

Option D (sharing with the Lake Formation catalog) is incorrect because Redshift data shares do not integrate directly with Lake Formation catalogs; they are specific to Redshift workgroups.

Amazon Athena charges per amount of data scanned by each query. When the same query with identical parameters is executed multiple times, re-scanning the data repeatedly increases both cost and execution time. Athena provides query result reuse, which allows previously computed query results to be reused when an identical query is rerun within a configurable time window.

By enabling query result reuse at the Athena workgroup level, Athena automatically returns cached results instead of re-reading data from Amazon S3. This significantly improves query performance while reducing data scan costs. This feature is specifically designed for workloads where analysts frequently rerun complex queries with the same parameters, which directly matches the scenario.

Converting data to JSON would degrade performance because JSON is not a columnar format and increases scan size. Using Amazon EMR introduces unnecessary infrastructure management and cost for a query optimization problem that Athena already solves natively. Amazon Redshift Spectrum is intended for querying S3 data from within Redshift and does not address repeated-query optimization in Athena.

Therefore, configuring query result reuse in the Athena workgroup is the most cost-effective and operationally efficient solution.