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

AWS Glue Data Quality transforms allow you to define built-in rules like IsComplete for null validation and ReferentialIntegrity for relationship validation—all with minimal code and operational overhead.

“Use AWS Glue Data Quality rules such as IsComplete and ReferentialIntegrity within ETL jobs to automatically validate incoming data.”

Option B is the best solution to meet the requirements with the least operational overhead because S3 Object Lambda is a feature that allows you to add your own code to process data retrieved from S3 before returning it to an application. S3 Object Lambda works with S3 GET requests and can modify both the object metadata and the object data. By using S3 Object Lambda, you can implement redaction logic within an S3 Object Lambda function to dynamically redact PII based on the needs of each application that accesses the data. This way, you can avoid creating and maintaining multiple copies of the dataset with different levels of redaction.

Option A is not a good solution because it involves creating and managing multiple copies of the dataset with different levels of redaction for each application. This option adds complexity and storage cost to the data protection process and requires additional resources and configuration. Moreover, S3 bucket policies cannot enforce fine-grained data access control at the row and column level, so they are not sufficient to redact PII.

Option C is not a good solution because it involves using AWS Glue to transform the data for each application. AWS Glue is a fully managed service that can extract, transform, and load (ETL) data from various sources to various destinations, including S3. AWS Glue can also convert data to different formats, such as Parquet, which is a columnar storage format that is optimized for analytics. However, in this scenario, using AWS Glue to redact PII is not the best option because it requires creating and maintaining multiple copies of the dataset with different levels of redaction for each application. This option also adds extra time and cost to the data protection process and requires additional resources and configuration.

Option D is not a good solution because it involves creating and configuring an API Gateway endpoint that has custom authorizers. API Gateway is a service that allows you to create, publish, maintain, monitor, and secure APIs at any scale. API Gateway can also integrate with other AWS services, such as Lambda, to provide custom logic for processing requests. However, in this scenario, using API Gateway to redact PII is not the best option because it requires writing and maintaining custom code and configuration for the API endpoint, the custom authorizers, and the REST API call. This option also adds complexity and latency to the data protection process and requires additional resources and configuration.

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

Introducing Amazon S3 Object Lambda – Use Your Code to Process Data as It Is Being Retrieved from S3

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AWS Glue Documentation

What is Amazon API Gateway? - Amazon API Gateway

Problem Analysis:

The company runs a daily AWS Glue ETL pipeline to clean and transform files received in an S3 bucket.

If a file is incomplete or empty, the previous day’s file should be retained.

Need a solution to validate files before overwriting the existing file.

Key Considerations:

Automate data validation with minimal human intervention.

Use built-in AWS Glue capabilities for ease of integration.

Ensure robust validation for missing or incomplete data.

Solution Analysis:

Option A: Lambda Function for Validation

Lambda can validate files, but it would require custom code.

Does not leverage AWS Glue’s built-in features, adding operational complexity.

Option B: AWS Glue Data Quality Rules

AWS Glue Data Quality allows defining Data Quality Definition Language (DQDL) rules.

Rules can validate if required fields are missing or if the file is empty.

Automatically integrates into the existing ETL pipeline.

If validation fails, retain the previous day’s file.

Option C: AWS Glue Studio with Filling Missing Values

Modifying ETL code to fill missing values with most common values risks introducing inaccuracies.

Does not handle empty files effectively.

Option D: Athena Query for Validation

Athena can drop rows with missing values, but this is a post-hoc solution.

Requires manual intervention to copy the corrected file to S3, increasing complexity.

Final Recommendation:

Use AWS Glue Data Quality to define validation rules in DQDL for identifying missing or incomplete data.

This solution integrates seamlessly with the ETL pipeline and minimizes manual effort.

Implementation Steps:

Enable AWS Glue Data Quality in the existing ETL pipeline.

Define DQDL Rules, such as:

Check if a file is empty.

Verify required fields are present and non-null.

Configure the pipeline to proceed with overwriting only if the file passes validation.

In case of failure, retain the previous day’s file.

AWS Glue Data Quality Overview

Defining DQDL Rules

AWS Glue Studio Documentation