Free Practice Questions for Amazon Web Services DVA-C02 Exam

Comprehensive and Detailed Step-by-Step Explanation:

Issue Analysis:

The stream uses service name as the partition key. This can cause " hot partition " issues when a few service names generate significantly more logs compared to others, causing uneven distribution of data across shards.

Metrics show that the write capacity used is below provisioned capacity, which confirms that the throughput errors are due to shard-level limits and not overall capacity.

Option C: Change Partition Key to Creation Timestamp:

By changing the partition key to the creation timestamp (or a composite key including timestamp), the distribution of data across shards can be randomized, ensuring an even spread of records.

This resolves the shard overutilization issue and eliminates ProvisionedThroughputExceededException.

Why Other Options Are Incorrect:

Option A: Switching to on-demand capacity mode might temporarily alleviate the issue, but the root cause (hot partitioning) remains unresolved.

Option B: Adding shards increases capacity but does not fix the skewed data distribution caused by using the service name as the partition key.

Option D: Creating separate streams for each service adds unnecessary complexity and does not scale well as the number of services grows.

Why Option B is Correct:The RetainExceptOnCreate deletion policy ensures that the IAM user is retained after successful stack creation but is deleted if the stack creation fails or rolls back. This meets both requirements.

Why Other Options are Incorrect:

Option A: The Retain policy retains the resource regardless of stack status and does not delete the IAM user upon rollback.

Option C: Updating the service role for termination protection does not address the specific deletion behavior for the IAM user.

Option D: Stack policy controls updates, not resource deletion behavior during rollbacks.

AWS Documentation References:

CloudFormation DeletionPolicy Attribute

Secrets Management: AWS Secrets Manager is designed specifically for storing and managing sensitive credentials.

Built-in Rotation: Secrets Manager provides automatic secret rotation functionality, enhancing security posture significantly.

IAM Integration: IAM policies and roles grant fine-grained access to ECS Fargate, ensuring the principle of least privilege.

Reduced Overhead: This solution centralizes secrets management and automates rotation, reducing operational overhead compared to the other options.

The core requirement is to perform a one-time deployment for testing without impacting the existing pre-production stack that other team members rely on. The safest way to avoid disruption is to deploy into an isolated AWS account (a dedicated development/testing account) instead of modifying the shared pre-production environment.

Option C achieves this with the least effort: use the AWS SAM CLI to package and deploy the same SAM application into a new AWS account designated for development. This creates an isolated copy of the stack (Lambda + SNS topics) where the developer can validate changes without affecting the shared pre-production deployment or the release pipeline.

Option A still deploys into pre-production, which directly violates the requirement to avoid impacting the existing application. A “debug parameter” does not prevent resource updates or side effects.

Option B is inconsistent: a CloudFormation change set is created against a specific account/stack. You cannot create a change set in one account and “execute it” in a different account as stated. Cross-account execution would require different credentials and a separate deployment process.

Option D introduces additional pipeline/stage complexity and is not necessary for a one-time test; it also implies changing the existing stack/pipeline, which can itself cause disruption.

Step-by-Step Breakdown:

Requirement Summary:

Encrypt S3 objects using KMS keys

Cross-account read access to another AWS account

Minimize operational overhead

Option A: Use a customer managed key + key policy granting kms:Decrypt to second account

Correct: Customer managed keys allow full control of key policy.

You can add a statement to allow cross-account access using the second account’s IAM principal.

Option B: Use AWS managed key + key policy granting access to second account

Incorrect: AWS-managed KMS keys (aws/s3) cannot be modified to add cross-account access in key policies.

Option C: SCP that grants s3:GetObject to second account

Incorrect: SCPs are used to restrict or allow permissions across AWS Organizations, not to grant S3 or KMS access directly.

Option D: Create a bucket policy granting s3:GetObject to second account

Correct: Bucket policies can grant cross-account access to specific IAM users or roles in the second account.

Option E: Gateway endpoint for S3 with cross-account access

Incorrect: Gateway endpoints only apply within the same VPC/account. Cross-account use with endpoint policies is not the correct pattern for this use case.

Cross-account S3 access with KMS: https://docs.aws.amazon.com/AmazonS3/latest/userguide/example-walkthroughs-managing-access-example2.html

KMS cross-account key policy: https://docs.aws.amazon.com/kms/latest/developerguide/key-policy-modifying.html

Bucket Policy for cross-account access: https://docs.aws.amazon.com/AmazonS3/latest/userguide/example-bucket-policies.html

To securely grant temporary access to a specific S3 object, the best option is to use an Amazon S3 presigned URL . A presigned URL is created by an IAM principal (user or role) that already has permission to access the object. The URL includes a signature and an expiration time , allowing anyone who has the URL to perform the specified operation (typically GET to download, or PUT to upload) until the URL expires . This meets the requirement for temporary access without changing the bucket to be publicly accessible.

Presigned URLs are especially useful when the developer needs to share access with people who do not have AWS credentials or are not part of the same AWS account. The developer can set an expiration that matches the business need (minutes to hours, etc.). After expiration, the URL no longer works, which reduces security exposure compared with long-lived access keys or permanently permissive bucket policies.

Option C (bucket policy with time restriction) can enforce time-based access, but it is not a practical way to securely share with a “specific group of people” unless those people authenticate with identifiable principals (IAM roles/users, federated identities) and you scope the policy to those principals. Simply providing the bucket S3 URL would not grant access unless the recipients also have valid permissions. Option D (static website hosting) is generally for public web content and does not inherently provide secure, temporary, per-object access control. Option A is unrelated to access sharing; object retention and restore operations are for retention/compliance and archival retrieval, not temporary permission grants.

Therefore, generating and sharing a presigned URL (B) is the standard, secure mechanism for time-limited access to S3 objects.

The correct answer is B because Amazon S3 event notifications can directly invoke an AWS Lambda function whenever a supported event occurs, such as ObjectCreated . This is the simplest and most infrastructure-efficient design for processing files that are uploaded to an S3 bucket. Since the requirement is to invoke the function with the least amount of AWS infrastructure , using the native direct integration between S3 and Lambda is the best choice.

AWS documentation explains that S3 can publish event notifications for object-level operations and send those notifications directly to Lambda. This allows the developer to build an event-driven workflow in which each newly uploaded project status file automatically triggers processing logic. There is no need for polling, scheduled scans, or intermediary messaging services unless there are additional requirements such as buffering, fan-out, or decoupling.

Option A is incorrect because polling the bucket every 5 minutes with EventBridge is less efficient and adds unnecessary delay and complexity. Option C is incorrect because introducing an SNS topic adds another infrastructure component when direct invocation is already supported. Option D is also incorrect because using SQS introduces a queue and polling behavior, which increases operational overhead and infrastructure count.

The direct S3-to-Lambda pattern is a common AWS serverless best practice for file processing workloads. It minimizes moving parts, reduces latency, and simplifies permissions and maintenance. Therefore, the developer should configure an S3 event notification for object creation events to invoke the Lambda function directly.

So, B is the correct answer.

The solution that will meet the requirements is to add a test phase to the amplify.yml build settings for the application. This way, the developer can run end-to-end tests on every code commit and catch any bugs before deploying to production. The other options either do not support end-to-end testing, or do not run tests automatically.

Distributed Tracing with X-Ray: X-Ray helps visualize request paths and identify bottlenecks in applications distributed across Regions.

Region Annotations (Automatic for AWS Services): X-Ray automatically adds a Region annotation to segments representing calls to AWS services. This aids in tracing cross-Region traffic.

Region Annotations (Manual for User-Defined): For segments representing calls to user-defined services in different Regions, the developer needs to add the Region annotation manually to enable comprehensive tracing.

The solution that will meet the requirements with the least development effort is to set the image resize Lambda function as a destination of the avatar generator Lambda function for the events that fail processing. This way, the fallback mechanism is automatically triggered by the Lambda service without requiring any additional components or configuration. The other options involve creating and managing additional resources such as queues, topics, state machines, or rules, which would increase the complexity and cost of the solution.

Amazon Cognito User Pools: A managed user directory service, simplifying user registration and login.

Social Identity Providers: Cognito supports integration with external providers (e.g., Google, Facebook), reducing development effort.

IAM Roles for Authorization: Cognito-managed IAM roles grant fine-grained access to AWS resources (like Lambda functions).

Operational Overhead: Cognito minimizes the need to manage user identities and credentials independently.

When sharing SSE-KMS–encrypted S3 objects across AWS accounts , AWS documentation states that three elements are required:

A customer-managed KMS key (Option E), because AWS-managed keys cannot be shared across accounts.

An S3 bucket policy (Option B) that grants the second account permission to access the objects.

A KMS key policy (Option C) that explicitly allows the second account to use the KMS key for decrypt operations.

Option A is incorrect because AWS-managed KMS keys cannot be shared. Option F uses SSE-S3, which does not use KMS and violates the encryption requirement. Option D is irrelevant because cross-account access does not require an S3 service role.

Therefore, the correct combination is B, C, and E .

In AWS X-Ray, filter expressions can filter trace data based on indexed fields that X-Ray can query efficiently. Custom data can be added to segments in two main ways: annotations and metadata. The critical difference is that annotations are indexed and can be used for filtering, while metadata is not indexed and is intended for additional diagnostic context that you do not typically query on.

Therefore, if the developer wants user-specified custom attributes to be usable in X-Ray filter expressions, the developer should record those attributes as annotations in the segment document. Once recorded as annotations, the developer can write filter expressions that match annotation keys/values and return only the relevant traces.

Option B is incorrect because metadata is not indexed and cannot be used in filter expressions for trace search the same way annotations can.

Option C is incorrect because segment documents have a defined schema; adding arbitrary “new segment fields” is not the intended method for searchable custom attributes.

Option D is unrelated: sampling rules control which requests get traced in the first place. They are not used to filter returned results by custom attributes after traces are recorded.