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

AWS Glue Detect PII transform is designed to identify sensitive data using custom pattern matching, making it well suited for detecting PII in proprietary or non-standard data formats. The transform integrates directly into AWS Glue ETL jobs and requires minimal configuration beyond defining the detection patterns.

Amazon Macie primarily relies on managed data identifiers and machine learning models optimized for common data formats such as JSON, CSV, and text. While Macie supports custom identifiers, it is less efficient for deeply proprietary formats and introduces additional service configuration.

Using AWS Lambda with custom regular expressions or Amazon Athena with SQL-based pattern matching would require building, operating, and maintaining custom logic across multiple buckets, increasing operational overhead and complexity.

AWS Glue provides a serverless, scalable, and centralized approach for PII detection as part of an existing data processing pipeline, making it the most operationally efficient solution for this requirement.

Therefore, Option A is the best answer.

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 B is most cost-effective because it combines a serverless ETL service for overnight processing with a serverless warehouse for partner SQL access during business hours. The material describes AWS Glue as a “fully managed, serverless, scalable ETL service” that can extract, transform, and load data with minimal operational overhead, which fits the requirement for multiple transformations and data cleaning.

For the SQL access layer, the study material highlights the cost advantage of Amazon Redshift Serverless: it automatically provisions and scales capacity and you pay only for the compute capacity provisioned, with no compute costs when no workloads are running. That directly supports “multiple business partners” querying during business hours while avoiding the always-on cost of a provisioned cluster.

Options C and D use a provisioned Redshift cluster, which is less cost-effective because capacity must be paid for even when partners are not querying. Option A adds EMR cluster management cost and operational overhead compared to using Glue for ETL.