Free Data-Engineer-Associate Questions Attempt

Option B best meets a highly resilient DR requirement with an RTO under 1 hour because a Multi-AZ deployment is designed to provide rapid recovery through redundancy across Availability Zones, rather than requiring a restore-and-rebuild process after a failure. A snapshot-based strategy (Option C) can be valuable for backup and regional recovery, but restoring a new cluster from snapshots is a heavier operation and is not the most reliable path to consistently achieving sub-hour RTO during real incidents. The study material highlights snapshot/restore as an operational mechanism for creating a new environment from a Redshift cluster’s saved state, which inherently implies a restore process and associated time to bring resources online.

Option D (cluster relocation) is primarily an operational move mechanism and is not the same as maintaining continuously resilient capacity for unplanned AZ-impacting events. Option A is not the best choice because Multi-AZ resiliency is aligned with modern Redshift architectures, and RA3 is the recommended node family for contemporary Redshift deployments where performance and managed storage characteristics are key for enterprise analytics. The study material reinforces Amazon Redshift as the platform for high-performance enterprise analytics, so a resilience-first configuration is appropriate.

 Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between SaaS applications and AWS services like Amazon S3 and Amazon Redshift. Amazon AppFlow supports many SaaS applications as data sources and targets, and allows you to configure data flows with a few clicks. Amazon AppFlow also provides features such as data transformation, filtering, validation, and encryption to prepare and protect your data. Amazon AppFlow meets the requirements of the media company with the least operational overhead, as it eliminates the need to write code, manage infrastructure, or monitor data pipelines. References:

Amazon AppFlow

Amazon AppFlow | SaaS Integrations List

Get started with data integration from Amazon S3 to Amazon Redshift using AWS Glue interactive sessions

Option B is the right approach because the requirement is to mask PII in the log destination (the CloudWatch Logs log group). The exam guide explicitly calls out security responsibilities that include “data encryption and masking” and also emphasizes enabling and preparing logs for audit and governance needs. A log-group–level masking mechanism is therefore the most direct control point to prevent sensitive values from being exposed to anyone who can view logs.

Option A (Glue security configuration) is primarily used to apply protections such as encryption settings for Glue jobs and related outputs; it does not inherently solve the problem of masking PII that has already been emitted into application logs. Option C (Macie) is intended to discover and classify sensitive data—most commonly in Amazon S3—and to produce findings; it is not a log-masking control for CloudWatch Logs. The material reinforces Macie as a discovery/classification service for PII rather than a masking mechanism. Option D adds custom code paths in the ETL job and still may miss PII that appears in framework/system logs; it also violates the “least operational effort” spirit compared to applying a centralized log policy.

Therefore, attaching a data protection policy directly to the CloudWatch Logs log group best meets the masking requirement.

AWS Glue streaming jobs are purpose-built for low-latency, near real-time ETL pipelines.

Glue Studio allows visual job authoring and supports complex transformations, including joins with Amazon RDS databases, through JDBC connectors.

Data can be written directly to S3 in Apache Parquet format using built-in AWS Glue support.

Options B and C are more suitable for batch or development/testing. Option D is for custom batch jobs, not real-time processing.

“You can use AWS Glue streaming jobs with Glue Studio to process streaming data, perform transformations, join with RDS data sources, and write results in formats like Parquet to S3.”