Data-Engineer-Associate Exam Dumps - AWS Certified Data Engineer - Associate (DEA-C01)

 A data mesh is an architectural framework that organizes data into domains and treats data as products that are owned and offered for consumption by different teams1. A data mesh requires a centralized layer for data governance and access control, as well as a distributed layer for data storage and analysis. AWS Glue can provide data catalogs and ETL operations for the data mesh, but it cannot provide data governance and access control by itself2. Therefore, the company needs to use another AWS service for this purpose. AWS Lake Formation is a service that allows you to create, secure, and manage data lakes on AWS3. It integrates with AWS Glue and other AWS services to provide centralized data governance and access control for the data mesh. Therefore, option E is correct.

For data storage and analysis, the company can choose from different AWS services depending on their needs and preferences. However, one of the benefits of a data mesh is that it enables data to be stored and processed in a decoupled and scalable way1. Therefore, using serverless or managed services that can handle large volumes and varieties of data is preferable. Amazon S3 is a highly scalable, durable, and secure object storage service that can store any type of data. Amazon Athena is a serverless interactive query service that can analyze data in Amazon S3 using standard SQL. Therefore, option B is a good choice for data storage and analysis in a data mesh. Option A, C, and D are not optimal because they either use relational databases that are not suitable for storing diverse and unstructured data, or they require more management and provisioning than serverless services. References:

1: What is a Data Mesh? - Data Mesh Architecture Explained - AWS

2: AWS Glue - Developer Guide

3: AWS Lake Formation - Features

[4]: Design a data mesh architecture using AWS Lake Formation and AWS Glue

[5]: Amazon S3 - Features

[6]: Amazon Athena - Features

Option A is the only choice that directly addresses the root cause: inconsistent postal-code formatting causing processing errors. In AWS Glue, a DynamicFrame can encounter issues when incoming data contains unexpected types or malformed values. Providing an explicit PySpark schema forces the postal code column to be interpreted consistently (for example, as a string with the expected structure), which prevents downstream steps from failing because of unexpected characters or mixed representations. After enforcing a consistent schema, the Glue job can standardize values (such as trimming extra digits, removing invalid characters, and normalizing case) and then write the corrected output back to Amazon S3 so the data lake is fixed at the source.

The document reinforces that AWS Glue is the managed ETL service used to transform data and move it between stores, which is exactly what is needed to correct bad values and persist clean outputs back into the lake. It also highlights that AWS Glue can perform validation as part of the ETL process, supporting the idea that data can be checked and corrected during processing rather than allowing bad values to break the workflow.

Options B, C, and D do not fix data quality or formatting issues; they relate to workflow state sharing, partition filtering, or internal implementation details, not cleansing invalid postal codes.

Option A (AWS DMS): Lets you replicate only selected tables from Aurora to Redshift. It supports ongoing replication (CDC) and reduces unnecessary data transfers.

Option C (Zero-ETL with Aurora to Redshift): This new integration allows real-time, serverless, and low-maintenance data replication. It ' s designed to reduce operational overhead drastically.

Other options introduce manual processing or inefficient query access (Option D). Glue workflows (Option E) add unnecessary complexity and lag for near-real-time needs.

“Zero-ETL integration between Amazon Aurora and Redshift enables real-time analytics without building complex pipelines.”

AWS Glue ETL is designed for scalable and serverless data processing, and it supports integrated quality enforcement using AWS Glue Data Quality, which makes it the most cost-effective and integrated option when combined with Amazon RDS for MySQL as the relational database.

“AWS Glue can perform data validation as part of the ETL process, ensuring data quality before storing the data in the target data store.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

Using AWS Glue Data Quality directly in the ETL workflow is simpler and more cost-effective than separating transformation (Glue) and validation (DataBrew) into different services.

AWS Lake Formation is a service that allows you to easily set up, secure, and manage data lakes. One of the features of Lake Formation is row-level security, which enables you to control access to specific rows or columns of data based on the identity or role of the user. This feature is useful for scenarios where you need to restrict access to sensitive or regulated data, such as customer data from different countries. By registering the S3 bucket as a data lake location in Lake Formation, you can use the Lake Formation console or APIs to define and apply row-level security policies to the data in the bucket. You can also use Lake Formation blueprints to automate the ingestion and transformation of data from various sources into the data lake. This solution requires the least operational effort compared to the other options, as it does not involve creating or moving data, or managing multiple tables, views, or roles. References:

AWS Lake Formation

Row-Level Security

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 4: Data Lakes and Data Warehouses, Section 4.2: AWS Lake Formation

AWS Step Functions allows you to orchestrate ETL jobs and workflows by integrating with AWS Glue and EventBridge. It supports both scheduled and event-driven executions with minimal operational overhead, making it ideal for hybrid triggering.

AWS Glue workflows are limited in orchestration logic.

MWAA (option B) is powerful but requires more infrastructure management.

EMR Serverless + Lambda (option D) is possible but involves more components.

“Use Step Functions to coordinate multiple services like Glue and EventBridge for event-driven or scheduled workflows.”

Option A is correct because AWS Glue crawlers are the AWS-native service for discovering metadata and updating the AWS Glue Data Catalog. AWS documentation says that crawlers can crawl data stores, infer schema, and upon completion create or update tables in the Data Catalog. For schema-change tracking on an Amazon Redshift table, the crawler can connect through JDBC, inspect the table metadata, and update the Glue Data Catalog on a schedule. This directly satisfies the requirement to implement a cataloging solution that tracks schema changes over time.

Option B is incorrect because AWS DataSync is for data transfer, not metadata cataloging. Option C is also incorrect because AWS SCT is intended for schema conversion and migration assessments, not ongoing catalog synchronization into a Hive metastore for this use case. Option D is not the best answer because the requirement is for an AWS cataloging solution, and the standard managed catalog service here is the AWS Glue Data Catalog, not an external Apache Hive metastore. The daily scheduled crawler against Redshift with Glue Data Catalog updates is the most direct and exam-aligned solution for tracking schema changes.