Amazon-DEA-C01 Exam 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

The company wants to use Amazon Kinesis Data Firehose to transform CSV files into JSON format and store the files in Apache Parquet format with the least development effort.

Option B: Use Kinesis Data Firehose to convert the CSV files to JSON and to store the files in Parquet format.Kinesis Data Firehose supports data format conversion natively, including converting incoming CSV data to JSON format and storing the resulting files in Parquet format in Amazon S3. This solution requires the least development effort because it uses built-in transformation features of Kinesis Data Firehose.

Other options (A, C, D) involve invoking AWS Lambda functions, which would introduce additional complexity and development effort compared to Kinesis Data Firehose's native format conversion capabilities.

Amazon Kinesis Data Firehose Documentation

Changing the volume type of the existing gp2 volumes to gp3 is the easiest and fastest way to migrate to the new storage type without any downtime or data loss. You can use the AWS Management Console, the AWS CLI, or the Amazon EC2 API to modify the volume type, size, IOPS, and throughput of your gp2 volumes. The modification takes effect immediately, and you can monitor the progress of the modification using CloudWatch. The other options are either more complex or require additional steps, such as creating snapshots, transferring data, or attaching new volumes, which can increase the operational overhead and the risk of errors.Reference:

Migrating Amazon EBS volumes from gp2 to gp3 and save up to 20% on costs(Section: How to migrate from gp2 to gp3)

Switching from gp2 Volumes to gp3 Volumes to Lower AWS EBS Costs(Section: How to Switch from GP2 Volumes to GP3 Volumes)

Modifying the volume type, IOPS, or size of an EBS volume - Amazon Elastic Compute Cloud(Section: Modifying the volume type)

For low-latency, high-throughput workloads with API-based access and predictable reads/writes, Amazon DynamoDB is the optimal choice. It scales automatically, supports thousands of read/write operations per second, and offers fully managed API-driven access.

''Amazon DynamoDB provides consistent, single-digit millisecond latency for high-traffic applications and scales seamlessly to handle thousands of concurrent reads and writes.''

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

Option B is correct because IAM policies are the standard AWS mechanism to control access to Amazon S3, and SSE-KMS encrypts S3 data with AWS KMS keys. AWS S3 documentation for SSE-KMS explains that it uses AWS Key Management Service keys for server-side encryption, and Secrets Manager documentation states that you can store secrets securely and configure automatic rotation. AWS further explains that rotation updates the credentials in both the secret and the target database or service, and that Secrets Manager uses a Lambda rotation function for supported rotation patterns.

Option A is weaker because Lambda environment variables are not the right service for managed secret storage and automatic rotation. Option C is not the best answer because S3 ACLs are not the preferred modern access-control model compared with IAM and bucket policies, and Parameter Store is not the main AWS service for built-in managed database credential rotation. Option D uses SSE-S3, not KMS-based encryption, and relies on a custom rotation approach instead of the native managed secret-rotation service. The study guide also highlights AWS KMS for encryption-key management and Secrets Manager for rotating credentials.