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

The optimal solution for near-real-time querying and visualization of logs is to integrate Amazon CloudWatch Logs with Amazon OpenSearch Service using subscription filters, which stream the logs directly into OpenSearch for querying and dashboarding:

“Use OpenSearch Service with CloudWatch Logs and create a subscription filter to stream log data in near real time into OpenSearch. Then use OpenSearch dashboards for visualization.”

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

This approach offers low latency and avoids batch exports, unlike the scheduled Athena + S3 pattern.

The query editor v2 in Amazon Redshift is a web-based tool that allows users to run SQL queries and scripts on Amazon Redshift clusters. The query editor v2 supports creating and managing materialized views, which are precomputed results of a query that can improve the performance of subsequent queries. The query editor v2 also supports scheduling queries to run at specified intervals, which can be used to refresh materialized views automatically. This solution requires the least effort, as it does not involve any additional services, coding, or configuration. The other solutions are more complex and require more operational overhead. Apache Airflow is an open-source platform for orchestrating workflows, which can be used to refresh materialized views, but it requires setting up and managing an Airflow environment, creating DAGs (directed acyclic graphs) to define the workflows, and integrating with Amazon Redshift. AWS Lambda is a serverless compute service that can run code in response to events, which can be used to refresh materialized views, but it requires creating and deploying Lambda functions, defining UDFs within Amazon Redshift, and triggering the functions using events or schedules. AWS Glue is a fully managed ETL service that can run jobs to transform and load data, which can be used to refresh materialized views, but it requires creating and configuring Glue jobs, defining Glue workflows to orchestrate the jobs, and scheduling the workflows using triggers. References:

Query editor V2

Working with materialized views

Scheduling queries

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

Amazon RDS controls network-level access through security groups, which act as virtual firewalls. To allow an EC2 instance to connect to an RDS database using JDBC, the RDS security group must explicitly allow inbound traffic from the EC2 instance or its associated security group on the appropriate database port.

IAM roles do not control network connectivity to RDS databases. The ec2_authorized_hosts parameter does not exist for RDS engines. The Amazon RDS Data API is not supported for standard JDBC connections and is only available for Aurora Serverless v1 and v2.

Updating the RDS security group is the correct and simplest approach. By adding an inbound rule that allows traffic from the EC2 instance’s security group, the application can securely connect to the database without additional configuration or restarts.

Therefore, Option C is the correct solution.

The requirement is to update the AWS Glue Data Catalog incrementally based on S3 events. Using an S3 event-based approach is the most automated and operationally efficient solution.

A. Create an S3 event-based AWS Glue crawler:

An event-based Glue crawler can automatically update the Data Catalog when new data arrives in the S3 bucket. This ensures incremental updates with minimal operational overhead.

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

Option D is the best solution to meet the requirements with the least operational overhead because AWS Lake Formation is a fully managed service that simplifies the process of building, securing, and managing data lakes. AWS Lake Formation allows you to define granular data access policies at the row and column level for different users and groups. AWS Lake Formation also integrates with Amazon Athena, Amazon Redshift Spectrum, and Apache Hive on Amazon EMR, enabling these services to access the data in the data lake through AWS Lake Formation.

Option A is not a good solution because S3 access policies cannot restrict data access by rows and columns. S3 access policies are based on the identity and permissions of the requester, the bucket and object ownership, and the object prefix and tags. S3 access policies cannot enforce fine-grained data access control at the row and column level.

Option B is not a good solution because it involves using Apache Ranger and Apache Pig, which are not fully managed services and require additional configuration and maintenance. Apache Ranger is a framework that provides centralized security administration for data stored in Hadoop clusters, such as Amazon EMR. Apache Ranger can enforce row-level and column-level access policies for Apache Hive tables. However, Apache Ranger is not a native AWS service and requires manual installation and configuration on Amazon EMR clusters. Apache Pig is a platform that allows you to analyze large data sets using a high-level scripting language called Pig Latin. Apache Pig can access data stored in Amazon S3 and process it using Apache Hive. However, Apache Pig is not a native AWS service and requires manual installation and configuration on Amazon EMR clusters.

Option C is not a good solution because Amazon Redshift is not a suitable service for data lake storage. Amazon Redshift is a fully managed data warehouse service that allows you to run complex analytical queries using standard SQL. Amazon Redshift can enforce row-level and column-level access policies for different users and groups. However, Amazon Redshift is not designed to store and process large volumes of unstructured or semi-structured data, which are typical characteristics of data lakes. Amazon Redshift is also more expensive and less scalable than Amazon S3 for data lake storage.

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

What Is AWS Lake Formation? - AWS Lake Formation

Using AWS Lake Formation with Amazon Athena - AWS Lake Formation

Using AWS Lake Formation with Amazon Redshift Spectrum - AWS Lake Formation

Using AWS Lake Formation with Apache Hive on Amazon EMR - AWS Lake Formation

Using Bucket Policies and User Policies - Amazon Simple Storage Service

Apache Ranger

Apache Pig

What Is Amazon Redshift? - Amazon Redshift

AWS Glue Data Quality is designed specifically to validate data as part of ETL pipelines with minimal cost and operational overhead. By creating a custom data quality rule, the data engineer can enforce business logic such as ensuring that the shipping date is later than the order date and that the delivery date is later than the shipping date.

Glue Data Quality rules run natively within AWS Glue ETL jobs, eliminating the need for additional services, duplicate queries, or post-processing steps. The service supports automatically identifying failed records and routing them to a separate Amazon S3 bucket, which directly satisfies the requirement to isolate invalid orders.

Using AWS Glue DataBrew introduces additional profiling and transformation steps that are unnecessary for a simple rule-based validation. Athena-based validation would require scanning data repeatedly, increasing query costs and adding complexity. AWS Glue crawlers only discover schema metadata and cannot enforce data quality rules.

From a cost and architecture perspective, Glue Data Quality integrates directly into existing Glue pipelines, avoids additional compute usage, and provides centralized monitoring of data quality metrics. Therefore, Option C is the most cost-effective and exam-aligned solution.