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

Option C is the correct answer because the Map state is designed to process a collection of data in parallel by applying the same transformation to each element. The Map state can invoke a nested workflow for each element, which can be another state machine or a Lambda function. The Map state will wait until all the parallel executions are completed before moving to the next state.

Option A is incorrect because the Parallel state is used to execute multiple branches of logic concurrently, not to process a collection of data. The Parallel state can have different branches with different logic and states, whereas the Map state has only one branch that is applied to each element of the collection.

Option B is incorrect because the Choice state is used to make decisions based on a comparison of a value to a set of rules. The Choice state does not process any data or invoke any nested workflows.

Option D is incorrect because the Wait state is used to delay the state machine from continuing for a specified time. The Wait state does not process any data or invoke any nested workflows.

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 5: Data Orchestration, Section 5.3: AWS Step Functions, Pages 131-132

Building Batch Data Analytics Solutions on AWS, Module 5: Data Orchestration, Lesson 5.2: AWS Step Functions, Pages 9-10

AWS Documentation Overview, AWS Step Functions Developer Guide, Step Functions Concepts, State Types, Map State, Pages 1-3

AWS Glue DataBrew provides a no-code way to define and run data quality rulesets for data stored in S3. You can trigger profiling jobs via Amazon EventBridge on new uploads for automated checks.

“Use AWS Glue DataBrew to define and run data quality rules on S3 datasets with minimal coding effort. Automate validation by triggering jobs through EventBridge.”

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

Step 1: Understanding the Data Use Case

The company has data stored in an Amazon S3 bucket and needs to provide teams access for analysis, ensuring that PII data is not included in the analysis. The solution should be simple to implement and maintain, ensuring minimal operational overhead.

Step 2: Why Option D is Correct

Option D (AWS Glue DataBrew) allows you to visually prepare and transform data without needing to write code. By using a DataBrew job, the company can:

Automatically detect and separate PII data from non-PII data.

Store PII data in a second S3 bucket for security, while keeping the original S3 bucket clean for analysis.

This approach keeps operational overhead low by utilizing DataBrew ' s pre-built transformations and the easy-to-use interface for non-technical users. It also ensures compliance by separating sensitive PII data from the main dataset.

Step 3: Why Other Options Are Not Ideal

Option A (Amazon Macie) is a powerful tool for detecting sensitive data, but Macie doesn ' t inherently remove or mask PII. You would still need additional steps to clean the data after Macie identifies PII.

Option B (S3 Object Lambda with Amazon Comprehend) introduces more complexity by requiring custom logic at the point of data access. Amazon Comprehend can detect PII, but using S3 Object Lambda to filter data would involve more overhead.

Option C (Kinesis Data Firehose and Comprehend) is more suitable for real-time streaming data use cases rather than batch analysis. Setting up and managing a streaming solution like Kinesis adds unnecessary complexity.

Conclusion:

Using AWS Glue DataBrew provides a low-overhead, no-code solution to detect and separate PII data, ensuring the analysis teams only have access to non-sensitive data. This approach is simple, compliant, and easy to manage compared to other options.