Free Amazon Web Services DVA-C02 Practice Exam with Questions & Answers | Set: 6

Why Option B is Correct:The pilot light strategy keeps a minimal version of the environment in another Region and scales up during a disaster. It achieves an RTO and RPO of hours at a low cost and complexity.

Why Other Options are Incorrect:

Option A: Warm standby is more expensive as it keeps a scaled-down, fully functioning version running in another Region.

Option C: Backup and restore has a longer RTO and RPO than hours.

Option D: Multi-site active-active is costly and more complex than required.

AWS Documentation References:

Disaster Recovery Strategies on AWS

The Immutable deployment method is the best choice when a company requires minimal downtime and automatic rollback in case of a failure. Here's why:

In Immutable deployments, a new set of instances is launched with the updated version. These instances are tested and validated before they replace the old instances. This ensures zero downtime and immediate rollback if the deployment fails.

All at once (A) causes downtime because the update replaces all instances simultaneously.

Rolling deployments (B) update a few instances at a time, but if a failure occurs midway, downtime or partial unavailability can happen.

Snapshots (C) are not a deployment strategy in Elastic Beanstalk.

This solution meets the requirements because it uses a PostgreSQL-compatible database that can secure and regularly rotate database credentials without requiring additional programming overhead. Amazon Aurora PostgreSQL is a relational database service that is compatible with PostgreSQL and offers high performance, availability, and scalability. AWS Secrets Manager is a service that helps you protect secrets needed to access your applications, services, and IT resources. You can store database credentials in AWS Secrets Manager and use them to access your Aurora PostgreSQL database. You can also enable automatic rotation of your secrets according to a schedule or an event. AWS Secrets Manager handles the complexity of rotating secrets for you, such as generating new passwords and updating your database with the new credentials. Using Amazon DynamoDB for the database will not meet the requirements because it is a NoSQL database that is not compatible with PostgreSQL. Using AWS Systems Manager Parameter Store for storing and rotating database credentials will require additional programming overhead to integrate with your database.

A Lambda alias is a pointer to a specific Lambda function version or another alias1. A Lambda alias allows you to invoke different versions of a function using the same name1. You can also split traffic between two aliases by assigning weights to them1.

In this scenario, the developer needs to test their changes in production on a small percentage of all traffic without changing the API Gateway URL. To achieve this, the developer can follow these steps:

Define an alias on the $LATEST version of the Lambda function. This will create a new alias that points to the latest code of the function.

Update the API Gateway endpoint to reference the new Lambda function alias. This will make the API Gateway invoke the alias instead of a specific version of the function.

Upload and publish the optimized Lambda function code. This will update the $LATEST version of the function with the new code.

On the production API Gateway stage, define a canary release and set the percentage of traffic to direct to the canary release. This will enable API Gateway to perform a canary deployment on a new API2. A canary deployment is a software development strategy in which a new version of an API is deployed for testing purposes, and the base version remains deployed as a production release for normal operations on the same stage2. The canary release receives a small percentage of API traffic and the production release takes up the rest2.

Update the API Gateway endpoint to use the $LATEST version of the Lambda function. This will make the canary release invoke the latest code of the function, which contains the optimized changes.

Publish to the canary stage. This will deploy the changes to a subset of users for testing.

By using this solution, the developer can test their changes in production on a small percentage of all traffic without changing the API Gateway URL. The developer can also monitor and compare metrics between the canary and production releases, and promote or disable the canary as needed2.

Step-by-Step Breakdown:

Requirement Summary:

In-transit encryption (when data is moving between client and S3)

Encryption at rest using AWS KMS keys (which are rotatable on-demand)

Option A: Write an S3 bucket policy to allow only encrypted connections over HTTPS by using permissions boundary

Incorrect: Permissions boundaries are for IAM policy control, not S3 bucket policies.

Also, encryption enforcement in-transit is not achieved through permissions boundaries.

Option B: Configure an S3 bucket policy to enable client-side encryption

Incorrect: AWS does not enforce client-side encryption via S3 bucket policies.

Client-side encryption must be handled entirely by the application.

Also, KMS is for server-side encryption, not client-side.

Option C: Configure the application to encrypt the objects by using an AWS KMS customer managed key before uploading the objects

Correct: This fulfills the requirement of encrypting objects at rest using a KMS CMK, which can be rotated.

You can specify the KMS key using the SSE-KMS option when putting an object.

Option D: Write an S3 bucket policy to allow only encrypted connections over HTTPS by using the aws:SecureTransport condition

Correct: This enforces in-transit encryption, which ensures that only HTTPS connections are allowed.

This is the AWS-recommended way to enforce encryption in transit via bucket policy.

Option E: Configure S3 Block Public Access settings for the S3 bucket to allow only encrypted connections over HTTPS

Incorrect: Block Public Access is used to prevent public exposure of the bucket.

It has nothing to do with encryption enforcement, whether in transit or at rest.

Using SSE-KMS: https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingKMSEncryption.html

aws:SecureTransport condition in bucket policies: https://docs.aws.amazon.com/AmazonS3/latest/userguide/amazon-s3-policy-keys.html

Server-side encryption with AWS KMS: https://docs.aws.amazon.com/AmazonS3/latest/userguide/serv-side-encryption.html

This solution will meet the requirements by using AWS Encryption SDK, which is a client-side encryption library that enables developers to encrypt and decrypt data using data encryption keys that are protected by AWS Key Management Service (AWS KMS). The SDK encrypts the data encryption key with a customer master key (CMK) that is managed by AWS KMS, and stores it (encrypted) as part of the returned ciphertext. The developer does not need to keep track of the data encryption keys used to encrypt data, as they are stored with the encrypted data and can be retrieved and decrypted by using AWS KMS when needed. Option A is not optimal because it will require manual tracking of the data encryption keys used for each data object, which is error-prone and inefficient. Option C is not optimal because it will store the data encryption keys automatically in Amazon S3, which is unnecessary and insecure as Amazon S3 is not designed for storing encryption keys. Option D is not optimal because it will store the data encryption key in the user data for the EC2 instance, which is also unnecessary and insecure as user data is not encrypted by default.

Requirement Summary:

DynamoDB Provisioned Mode

ProvisionedThroughputExceededException error occurring

App hosted on a small burstable EC2 instance

Option A: Move to a larger EC2 instance

May improve local performance, but does not solve DynamoDB provisioned throughput limits.

Option B: Increase DynamoDB RCUs

Valid: This directly increases the read throughput capacity of the table.

Helps handle more traffic and reduce throughput exceptions.

Option C: Reduce frequency via exponential backoff

Best practice: Using exponential backoff and jitter reduces request pressure and spreads retries out to avoid spiking.

Option D: Increase frequency of retries by reducing delay

Opposite of best practice. Increases load, worsening the issue.

Option E: Change to on-demand mode

Also valid in general, but question is scoped around provisioned capacity.

Since minimizing cost or workload type isn't mentioned, and scaling the existing model is the focus, prefer B and C.

ProvisionedThroughputExceededException: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HandlingErrors.html

Exponential backoff: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Programming.Errors.html

Capacity modes: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadWriteCapacityMode.html

Why Option C is Correct:

Amazon ElastiCache (Memcached) provides low-latency, in-memory caching suitable for session storage. It ensures stateless web tier operations and supports the high throughput of 5000 requests per minute.

Why Other Options are Incorrect:

Option A: RDS has higher latency compared to in-memory caching solutions like ElastiCache.

Option B: Shared file systems introduce additional complexity and are not ideal for low-latency session data storage.

Option D: DynamoDB has low latency but is less performant than ElastiCache for in-memory session management.

AWS Documentation References:

Amazon ElastiCache for Session State Management

This benefit occurs when initializing the AWS SDK outside of the Lambda handler function because it allows the SDK instance to be reused across multiple invocations of the same function. This can improve performance and reduce latency by avoiding unnecessary initialization overhead. If the SDK is initialized inside the handler function, it will create a new SDK instance for each invocation, which can increase memory usage and execution time.