Correct option:

Use an IAM role

IAM roles have been incorporated so that your applications can securely make API requests from your instances, without requiring you to manage the security credentials that the applications use. Instead of creating and distributing your AWS credentials, you can delegate permission to make API requests using IAM roles.

Amazon EC2 uses an instance profile as a container for an IAM role. When you create an IAM role using the IAM console, the console creates an instance profile automatically and gives it the same name as the role to which it corresponds.

This is the most secure option as the role assigned to EC2 can be used to access S3 without storing any credentials onto the EC2 instance.

Incorrect options:

Create an IAM programmatic user and store the access key and secret access key on the EC2 ~/.aws/credentials file. - While this would work, this is highly insecure as anyone gaining access to the EC2 instance would be able to steal the credentials stored in that file.

Use EC2 User Data - EC2 User Data is used to run bootstrap scripts at an instance's first launch. This option is not the right fit for the given use-case.

Create an S3 bucket policy that authorizes public access - While this would work, it would allow anyone to access your S3 bucket files. So this option is ruled out.

Reference:

https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/iam-roles-for-amazon-ec2.html

Correct option:

Increase the minimum instance capacity of the Auto Scaling Group to 2 -

You configure the size of your Auto Scaling group by setting the minimum, maximum, and desired capacity. The minimum and maximum capacity are required to create an Auto Scaling group, while the desired capacity is optional. If you do not define your desired capacity upfront, it defaults to your minimum capacity.

Since a minimum capacity of 1 was defined, an instance was launched in only one AZ. This AZ went down, taking the application with it. If the minimum capacity is set to 2. As per Auto Scale AZ configuration, it would have launched 2 instances- one in each AZ, making the architecture disaster-proof and hence highly available.

via - https://docs.aws.amazon.com/autoscaling/ec2/userguide/auto-scaling-benefits.html

Incorrect options:

Change the scaling metric of auto-scaling policy to network bytes - With target tracking scaling policies, you select a scaling metric and set a target value. You can use predefined customized metrics. Setting the metric to network bytes will not help in this context since the instances have to be spread across different AZs for high availability. The optimized way of doing it, is by defining minimum and maximum instance capacities, as discussed above.

Configure ASG fast failover - This is a made-up option, given as a distractor.

Enable RDS Multi-AZ - This configuration will make your database highly available. But for the current scenario, you will need to have more than 1 instance in separate availability zones to keep the application highly available.

References:

https://docs.aws.amazon.com/autoscaling/ec2/userguide/asg-capacity-limits.html

https://docs.aws.amazon.com/autoscaling/ec2/userguide/as-maintain-instance-levels.html

https://docs.aws.amazon.com/autoscaling/ec2/userguide/as-scaling-target-tracking.html

This is the most efficient and recommended way to promote a tested API deployment from a test stage to a production stage in API Gateway. Here's why:

Simplified Promotion: It involves a simple configuration change (updating a stage variable) rather than redeploying the entire API, making the process faster and less error-prone.

Preserves Configuration: This method ensures that any stage-specific configurations (like throttling, caching, or logging settings) defined for the production stage remain intact.

Why other options are not optimal:

B. API performance is optimized in a different way for prod environments...: While production environments might have different performance optimizations, promoting the test stage doesn't necessarily negate those. You can still apply production-specific configurations after the promotion.

C. Deploy the API without choosing a stage...: This would deploy the updated API to all stages simultaneously, potentially impacting the production environment before it's fully tested.

D. Delete the existing prod stage...: This is unnecessary and disruptive. It would require reconfiguring any integrations or client applications that depend on the production stage.

Example:

[1] You have a REST API deployed to two stages: test and prod.

[2] The test stage points to deployment ID xyz123, while the prod stage points to deployment ID abc456.

[3] After testing in the test stage, you update the prod stage to point to deployment ID xyz123.

[4] The prod stage now runs the same API version as the test stage, effectively promoting the tested deployment to production.

Correct option:

Use AWS Lambda aliases - A Lambda alias is like a pointer to a specific Lambda function version. You can create one or more aliases for your AWS Lambda function. Users can access the function version using the alias ARN. An alias can only point to a function version, not to another alias. You can update an alias to point to a new version of the function. Event sources such as Amazon S3 invoke your Lambda function. These event sources maintain a mapping that identifies the function to invoke when events occur. If you specify a Lambda function alias in the mapping configuration, you don't need to update the mapping when the function version changes. This is the right choice for the current requirement.

Incorrect options:

Use Tags to distinguish the different versions - You can tag Lambda functions to organize them by owner, project or department. Tags are freeform key-value pairs that are supported across AWS services for use in filtering resources and adding detail to billing reports. This does not address the given use-case.

Use environment variables - You can use environment variables to store secrets securely and adjust your function's behavior without updating code. An environment variable is a pair of strings that are stored in a function's version-specific configuration. The Lambda runtime makes environment variables available to your code and sets additional environment variables that contain information about the function and invocation request. For example, you can use environment variables to point to test, development or production databases by passing it as an environment variable during runtime. This option does not address the given use-case.

Deploy your Lambda in a VPC - Amazon Virtual Private Cloud (Amazon VPC) enables you to launch AWS resources into a virtual network that you've defined. This adds another layer of security for your entire architecture. Not the right choice for the given scenario.

References:

https://docs.aws.amazon.com/lambda/latest/dg/configuration-aliases.html

https://docs.aws.amazon.com/lambda/latest/dg/configuration-tags.html

https://docs.aws.amazon.com/lambda/latest/dg/configuration-envvars.html

Correct option:

Setup ElastiCache in front of RDS

Amazon ElastiCache is an ideal front-end for data stores such as Amazon RDS, providing a high-performance middle tier for applications with extremely high request rates and/or low latency requirements. The best part of caching is that it’s minimally invasive to implement and by doing so, your application performance regarding both scale and speed is dramatically improved.

Incorrect options:

Setup RDS Read Replicas - Adding read replicas would further add to the database costs and will not help in reducing latency when compared to a caching solution. So this option is ruled out.

Move to Amazon Redshift - Redshift is optimized for datasets ranging from a few hundred gigabytes to a petabyte or more. If the company is looking at cost-cutting, moving to Redshift from RDS is not an option.

Switch application code to AWS Lambda for better performance - AWS Lambda can help in running data processing workflows. But, data still needs to be read from RDS and hence we need a solution to speed up the data reads and not before/after processing.

Reference:

https://aws.amazon.com/caching/database-caching/

Correct option:

Allows full S3 access, but explicitly denies access to the Production bucket if the user has not signed in using MFA within the last thirty minutes - This example shows how you might create a policy that allows an Amazon S3 user to access any bucket, including updating, adding, and deleting objects. However, it explicitly denies access to the Production bucket if the user has not signed in using multi-factor authentication (MFA) within the last thirty minutes. This policy grants the permissions necessary to perform this action in the console or programmatically using the AWS CLI or AWS API.

This policy never allows programmatic access to the Production bucket using long-term user access keys. This is accomplished using the aws:MultiFactorAuthAge condition key with the NumericGreaterThanIfExists condition operator. This policy condition returns true if MFA is not present or if the age of the MFA is greater than 30 minutes. In those situations, access is denied. To access the Production bucket programmatically, the S3 user must use temporary credentials that were generated in the last 30 minutes using the GetSessionToken API operation.

Incorrect options:

Allows a user to manage a single Amazon S3 bucket and denies every other AWS action and resource if the user is not signed in using MFA within last thirty minutes

Allows full S3 access to an Amazon Cognito user, but explicitly denies access to the Production bucket if the Cognito user is not authenticated

Allows IAM users to access their own home directory in Amazon S3, programmatically and in the console

These three options contradict the explanation provided above, so these options are incorrect.

Reference:

https://docs.aws.amazon.com/IAM/latest/UserGuide/reference_policies_examples_s3_full-access-except-production.html

Correct option:

Configure CORS on the bucket B that is hosting the web fonts to allow Bucket A origin to make the requests

Cross-origin resource sharing (CORS) defines a way for client web applications that are loaded in one domain to interact with resources in a different domain.

To configure your bucket to allow cross-origin requests, you create a CORS configuration, which is an XML document with rules that identify the origins that you will allow to access your bucket, the operations (HTTP methods) that will support for each origin, and other operation-specific information.

For the given use-case, you would create a <CORSRule> in <CORSConfiguration> for bucket B to allow access from the S3 website origin hosted on bucket A.

Please see this note for more details:

via - https://docs.aws.amazon.com/AmazonS3/latest/dev/cors.html

Incorrect options:

Enable versioning on both the buckets to facilitate the correct functioning of the website - This option is a distractor and versioning will not help to address the web fonts loading issue on the website.

Update bucket policies on both bucket A and bucket B to allow successful loading of the web fonts on the website - It's not the bucket policies but the CORS configuration on bucket B that needs to be updated to allow web fonts to be loaded on the website. Updating bucket policies will not help to address the web fonts loading issue on the website.

Configure CORS on the bucket A that is hosting the website to allow any origin to respond to requests - The CORS configuration needs to be updated on bucket B to allow web fonts to be loaded on the website hosted on bucket A. So this option in incorrect.

Reference:

https://docs.aws.amazon.com/AmazonS3/latest/dev/cors.html

Correct options:

Same-Region Replication (SRR) and Cross-Region Replication (CRR) can be configured at the S3 bucket level, a shared prefix level, or an object level using S3 object tags - Amazon S3 Replication (CRR and SRR) is configured at the S3 bucket level, a shared prefix level, or an object level using S3 object tags. You add a replication configuration on your source bucket by specifying a destination bucket in the same or different AWS region for replication.

S3 lifecycle actions are not replicated with S3 replication - With S3 Replication (CRR and SRR), you can establish replication rules to make copies of your objects into another storage class, in the same or a different region. Lifecycle actions are not replicated, and if you want the same lifecycle configuration applied to both source and destination buckets, enable the same lifecycle configuration on both.

Incorrect options:

Object tags cannot be replicated across AWS Regions using Cross-Region Replication - Object tags can be replicated across AWS Regions using Cross-Region Replication. For customers with Cross-Region Replication already enabled, new permissions are required for tags to replicate.

Once replication is enabled on a bucket, all old and new objects will be replicated - Replication only replicates the objects added to the bucket after replication is enabled on the bucket. Any objects present in the bucket before enabling replication are not replicated.

Replicated objects do not retain metadata - You can use replication to make copies of your objects that retain all metadata, such as the original object creation time and version IDs. This capability is important if you need to ensure that your replica is identical to the source object.

Reference:

https://docs.amazonaws.cn/en_us/AmazonS3/latest/userguide/replication.html

Correct option:

You terminated the container instance while it was in STOPPED state, that lead to this synchronization issues - If you terminate a container instance while it is in the STOPPED state, that container instance isn't automatically removed from the cluster. You will need to deregister your container instance in the STOPPED state by using the Amazon ECS console or AWS Command Line Interface. Once deregistered, the container instance will no longer appear as a resource in your Amazon ECS cluster.

Incorrect options:

You terminated the container instance while it was in RUNNING state, that lead to this synchronization issues - This is an incorrect statement. If you terminate a container instance in the RUNNING state, that container instance is automatically removed, or deregistered, from the cluster.

The container instance has been terminated with AWS CLI, whereas, for ECS instances, Amazon ECS CLI should be used to avoid any synchronization issues - This is incorrect and has been added as a distractor.

A custom software on the container instance could have failed and resulted in the container hanging in an unhealthy state till restarted again - This is an incorrect statement. It is already mentioned in the question that the developer has terminated the instance.

References:

https://aws.amazon.com/premiumsupport/knowledge-center/deregister-ecs-instance/

https://docs.aws.amazon.com/AmazonECS/latest/developerguide/ECS_instances.html

Correct option:

Use the header X-Forwarded-For - The X-Forwarded-For request header helps you identify the IP address of a client when you use an HTTP or HTTPS load balancer. Because load balancers intercept traffic between clients and servers, your server access logs contain only the IP address of the load balancer. To see the IP address of the client, use the X-Forwarded-For request header. Elastic Load Balancing stores the IP address of the client in the X-Forwarded-For request header and passes the header to your server.

Incorrect options:

You can get the Client IP addresses from server access logs - As discussed above, Load Balancers intercept traffic between clients and servers, so server access logs will contain only the IP address of the load balancer.

Use the header X-Forwarded-From - This is a made-up option and given as a distractor.

You can get the Client IP addresses from Elastic Load Balancing logs - Elastic Load Balancing logs requests sent to the load balancer, including requests that never made it to the targets. For example, if a client sends a malformed request, or there are no healthy targets to respond to the request, the request is still logged. So, this is not the right option if we wish to collect the IP addresses of the clients that have access to the instances.

References:

https://docs.aws.amazon.com/elasticloadbalancing/latest/classic/x-forwarded-headers.html

https://docs.aws.amazon.com/elasticloadbalancing/latest/application/load-balancer-access-logs.html