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

Amazon Redshift supports Role-Based Access Control (RBAC) to manage access to database objects. RBAC allows administrators to create roles for job functions and assign privileges at the schema, table, or column level based on data sensitivity and user roles.

“RBAC in Amazon Redshift helps manage permissions more efficiently at scale by assigning users to roles that reflect their job function. It simplifies user management and secures access based on job role and data sensitivity.”

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

RBAC is preferred over RLS or CLS alone because it offers a more comprehensive and scalable solution across multiple users and permissions.

The data engineer needs to orchestrate ETL jobs that include Spark jobs on Amazon EMR, API calls to Salesforce, and loading data into Redshift. They also need automatic failure handling and retries. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is the best solution for this requirement.

Option A: Amazon Managed Workflows for Apache Airflow (Amazon MWAA)Apache Airflow is designed for complex job orchestration, allowing users to define workflows (DAGs) in Python. MWAA manages Airflow and its integrations with other AWS services, including Amazon EMR, Redshift, and external APIs like Salesforce. It provides automatic retry handling, failure detection, and detailed monitoring, which fits the use case perfectly.

Option B (AWS Step Functions) can orchestrate tasks but doesn ' t natively support complex workflow definitions with Python like Airflow does.

Option C (AWS Glue) is more focused on ETL and doesn ' t handle the orchestration of external systems like Salesforce as well as Airflow.

Option D (Amazon EventBridge) is more suited for event-driven architectures rather than complex workflow orchestration.

Option A is correct because this is a sequential, dependent workflow where each validation stage depends on the previous one, and the overall execution can last up to 24 hours. AWS states that Standard Workflows in Step Functions are ideal for long-running, durable, auditable workflows and can run for up to one year. AWS also documents the Wait for a Callback with Task Token pattern, which is specifically designed for workflows that must pause until an external process reports completion. That maps directly to multi-level validations that may complete asynchronously but still need orchestration within a bounded window.

Option B is incorrect because Express Workflows can run for only up to five minutes, so they cannot support a transaction workflow that may take many hours. Option C adds more custom code and operational management than Step Functions and does not naturally provide the same stateful orchestration, dependency tracking, and callback waiting pattern. Option D is also more operationally heavy and is better suited to data pipeline orchestration than per-transaction validation workflows. Therefore, Standard Step Functions provides the lowest operational cost while fully meeting the timing and dependency requirements.