Free Amazon Web Services DVA-C02 Practice Exam with Questions & Answers | Set: 7

Amazon Simple Queue Service (Amazon SQS) is a fully managed queue service that allows you to de-couple and scale for applications1. Amazon SQS offers two types of queues: Standard and FIFO (First In First Out) queues1. The FIFO queue uses the messageDeduplicationId property to treat messages with the same value as duplicate2. Therefore, changing the Amazon SQS standard queue to an Amazon SQS FIFO queue using the Amazon SQS message deduplication ID can help resolve the issue of the Lambda function processing some messages multiple times. Therefore, option A is correct.

Why Option C is Correct:Implementing a connection pool in the Redis client reduces connection overhead and avoids frequent connection establishment, which helps to reduce latency and connection failures.

Why Other Options are Incorrect:

Option A: Exponential backoff retry strategies help with transient failures but do not resolve latency caused by frequent connection establishment.

Option B: Storing scores in application memory adds complexity and risks inconsistency.

Option D: Adding more nodes is unnecessary unless the cluster is under heavy load. Latency due to connection failures is better addressed at the application level.

AWS Documentation References:

Amazon ElastiCache Best Practices

AWS STS AssumeRole: The central operation for assuming temporary security credentials, commonly used for cross-account access.

MFA Integration: The AssumeRole call can include MFA information to enforce multi-factor authentication.

Credentials for S3 Access: The returned temporary credentials would provide the necessary permissions to access the S3 bucket in the other account.

This solution allows easy and secure control of access to the downloads at the lowest cost because it uses a content delivery network (CDN) that can cache and distribute firmware updates to customers around the world, and uses a mechanism that can restrict access to specific files or versions. Amazon CloudFront is a CDN that can improve performance, availability, and security of web applications by delivering content from edge locations closer to customers. Amazon S3 is a storage service that can store firmware updates in buckets and objects. Signed URLs are URLs that include additional information, such as an expiration date and time, that give users temporary access to specific objects in S3 buckets. The developer can use CloudFront to serve firmware updates from S3 buckets and use signed URLs to control who can download them and for how long. Creating a dedicated CloudFront distribution for each customer will incur unnecessary costs and complexity. Using Amazon CloudFront with AWS Lambda@Edge will require additional programming overhead to implement custom logic at the edge locations. Using Amazon API Gateway and AWS Lambda to control access to an S3 bucket will also require additional programming overhead and may not provide optimal performance or availability.

The amount of memory you allocate to your Lambda function also determines how much CPU and network bandwidth it gets. Increasing the memory size can improve the performance of CPU-bound functions by giving them more CPU power. The CPU allocation is proportional to the memory allocation, so a function with 1 GB of memory has twice the CPU power of a function with 512 MB of memory. Reference: AWS Lambda execution environment

This solution allows the developer to create and delete branches in AWS CodeCommit by granting the codecommit:CreateBranch and codecommit:DeleteBranch permissions. These are the minimum permissions required for this task, following the principle of least privilege. Option B grants too many permissions, such as codecommit:Put*, which allows the developer to create, update, or delete any resource in CodeCommit. Option C grants too few permissions, such as codecommit:Update*, which does not allow the developer to create or delete branches. Option D grants all permissions, such as codecommit:*, which is not secure or recommended.

Step-by-Step Breakdown:

Requirement Summary:

Use AWS SAM to deploy:

1 Lambda Function

1 S3 Bucket

Lambda needs read-only access to the S3 bucket

Solution must be expressed via AWS SAM template

Option A: Reference a second Lambda authorizer function

Incorrect: Lambda authorizers are used in API Gateway for authentication, not for granting S3 permissions.

Option B: Add a custom S3 bucket policy to the Lambda function

Incorrect: Bucket policies control who can access the bucket, not what the Lambda function can do.

The permission must be granted to the Lambda’s IAM execution role.

Option C: Create an Amazon SQS topic for only S3 object reads

Incorrect: SQS cannot read objects from S3 and is not relevant to this scenario.

Option D: Add the S3ReadPolicy template to the Lambda function's execution role

Correct: AWS SAM provides managed policy templates like AmazonS3ReadOnlyAccess and shortcuts like S3ReadPolicy.

You can apply these to the Lambda’s execution role using the Policies: section in your SAM template.

Example SAM YAML:

yaml

CopyEdit

Resources:

MyFunction:

Type: AWS::Serverless::Function

Properties:

CodeUri: my-code/

Handler: app.handler

Runtime: python3.11

Policies:

- S3ReadPolicy:

BucketName: !Ref MyBucket

MyBucket:

Type: AWS::S3::Bucket

Properties:

BucketName: my-personal-bucket-name

SAM Policy Templates: https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/serverless-policy-templates.html

Example using S3ReadPolicy: https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/serverless-policy-templates.html#s3-readpolicy

Amazon S3 Glacier Instant Retrieval is an archive storage class that delivers the lowest-cost storage for long-lived data that is rarely accessed and requires retrieval in milliseconds. With S3 Glacier Instant Retrieval, you can save up to 68% on storage costs compared to using the S3 Standard-Infrequent Access (S3 Standard-IA) storage class, when your data is accessed once per quarter. https://aws.amazon.com/s3/storage-classes/glacier/instant-retrieval/

Understanding Storage Requirements:

Files are large and infrequently accessed, but need to be available within minutes when requested in the first year.

Long-term (7-year) retention is required.

Cost-effectiveness is a top priority.

Why S3 Glacier Instant Retrieval:

Matches the retrieval requirements (access within minutes).

More cost-effective than S3 Standard for infrequently accessed data.

Simpler to use than traditional Glacier where retrievals take hours.

Why S3 Glacier Deep Archive:

Most cost-effective S3 storage class for long term archival.

Meets the 7-year retention requirement.

S3 Lifecycle Policy:

Automate the transition from Glacier Instant Retrieval to Glacier Deep Archive after one year.

Optimize costs by matching storage classes to access patterns.

The AWS Serverless Application Model Command Line Interface (AWS SAM CLI) is a command-line tool for local development and testing of Serverless applications2. The sam local start-api subcommand of AWS SAM CLI is used to simulate a REST API by starting a new local endpoint3. Therefore, option D is correct.

The objects are “rarely accessed after 1 week” but must remain immediately available at all times. That means the storage class must support millisecond access without a restore process. S3 Standard-Infrequent Access (Standard-IA) is designed for exactly this: lower storage cost than S3 Standard, while still providing rapid access when needed.

With option B, an S3 Lifecycle rule transitions objects from S3 Standard to S3 Standard-IA after 7 days. This aligns perfectly with the usage pattern: frequent access initially, then infrequent access after a week, while keeping immediate availability.

Option C (S3 Glacier Flexible Retrieval) is not appropriate because Glacier classes are archival. Access typically requires a restore operation and retrieval time (minutes to hours), which violates “immediately available at all times.”

Option A expires objects, which deletes them after 7 days—this contradicts the requirement to keep objects available.

Option D is irrelevant because delete markers exist only when versioning is enabled. The bucket does not have versioning enabled, so this rule would not help.

Therefore, transitioning to S3 Standard-IA after 7 days is the correct cost-optimized solution while maintaining immediate availability.

Amazon S3 supports server-side encryption, which encrypts data at rest on the server that stores the data. One of the encryption options is SSE-S3, which uses keys managed by S3. To use SSE-S3, the x-amz-server-side-encryption header must be set to AES256 when invoking the PutObject API operation. This instructs S3 to encrypt the object data with SSE-S3 before saving it on disks in its data centers and decrypt it when it is downloaded. Reference: Protecting data using server-side encryption with Amazon S3-managed encryption keys (SSE-S3)

Why Option A is Correct:Encrypting PII at rest and in transit before storing it in DynamoDB ensures end-to-end security. Using the AWS Database Encryption SDK with KMS keys allows the Lambda function to encrypt data before transmission, meeting security and compliance requirements.

Why Other Options are Incorrect:

Option B: While AWS-managed KMS keys encrypt DynamoDB data at rest, they do not encrypt data in transit.

Option C: DynamoDB streams process updates after the data is written to the table, failing to encrypt PII in transit.

Option D: Step Functions and SQS add unnecessary complexity and still require encryption logic for both transit and at rest.

AWS Documentation References:

Encrypting Data in DynamoDB

AWS Database Encryption SDK

Comprehensive and Detailed Explanation (250–300 words):

Amazon SQS uses a visibility timeout to prevent other consumers from processing a message while it is being handled. If a Lambda function does not complete processing before the visibility timeout expires, the message becomes visible again and can be processed a second time.

AWS documentation states that the SQS visibility timeout should be greater than the maximum Lambda execution time. If not, messages may be delivered multiple times, even though processing is still in progress.

Increasing the Lambda timeout or memory does not directly affect message visibility. Increasing batch size can worsen the problem by increasing processing time.

Therefore, increasing the SQS visibility timeout is the correct and AWS-recommended solution.

This is a feature flag / dynamic configuration requirement: enable a new feature for a subset of users (premium customers), toggle it on/off quickly based on feedback/performance, and deploy configuration changes safely with validation and without disruption. AWS AppConfig (part of AWS Systems Manager) is designed for exactly this use case.

AWS AppConfig helps create, manage, and deploy application configurations, including feature flags, in a controlled way. It supports configuration validators (such as JSON schema or Lambda validators) to catch errors before deployment. It also supports controlled rollouts with deployment strategies to reduce blast radius (for example, gradual deployments). This allows rapid changes without redeploying code and minimizes disruption.

Option A aligns perfectly: use AppConfig feature flags to target premium users and toggle the feature quickly.

Option B is incorrect because Secrets Manager is for sensitive secrets (passwords, API keys), not feature flag management and progressive configuration deployments.

Option C is incorrect because AWS Config evaluates resource compliance and records configuration changes of AWS resources; it does not serve runtime feature flag delivery.

Option D (Parameter Store) can store configuration values, but it does not provide the same feature-flag-focused capabilities, validation/deployment strategies, and safe rollout controls that AppConfig provides. Also, “lifecycle rules” is not how Parameter Store toggles features.

A feature branch is a branch that is created from the main branch to work on a specific feature or task1. Feature branches allow developers to isolate their work from the main branch and avoid conflicts with other changes1. Feature branches can be merged back to the main branch when the feature or task is completed and tested1.

In this scenario, the developer needs to maintain two parallel streams of work: one for fixing and updating the current version of the application that is deployed in production, and another for developing the new version of the application. The developer can use feature branches to achieve this goal.

The developer can create a feature branch from the main branch for production bug fixes. This branch will contain the code that is currently deployed in production, and any fixes or updates that need to be applied to it. The developer can push this branch to the CodeCommit repository and use it to deploy changes to the production environment.

The developer can also create a second feature branch from the main branch for development of the new version of the application. This branch will contain the code that is under development for the new version, and any changes or enhancements that are part of it. The developer can push this branch to the CodeCommit repository and use it to test and deploy the new version of the application in a separate environment.

By using feature branches, the developer can keep the main branch stable and clean, and avoid mixing code from different versions of the application. The developer can also easily switch between branches and merge them when needed.