In DynamoDB, each partition has a maximum throughput limit. With 400 WCUs and 4 partitions, each partition can handle a maximum of 100 WCU/second. If one partition receives 250 WCU/second, it exceeds its limit and triggers the ProvisionedThroughputExceededException error. This is known as a "hot partition".

Why other options are incorrect:

A. CloudWatch monitoring is lagging: While CloudWatch might have some delay, it wouldn't cause the error directly. The error is due to exceeding the per-partition throughput limit.

C. Write Capacity Units (WCU’s) are applied across to all your DynamoDB tables and this needs reconfiguration: WCUs are provisioned on a per-table basis, not across all tables.

D. Configured IAM policy is wrong: IAM policies control access to DynamoDB, not the provisioned throughput.

Example:

Imagine you have a DynamoDB table storing social media posts, and the partition key is the user ID. If a few users are very active and generate many posts, their partition could become a hot partition, exceeding the per-partition throughput limit.

The correct answers are:

B. AWS Elastic Beanstalk: Elastic Beanstalk uses CloudFormation under the hood to provision and manage the resources (e.g., EC2 instances, load balancers, databases) required for your application.

C. AWS Serverless Application Model (AWS SAM): SAM is a framework for building serverless applications. It uses CloudFormation to define and deploy the necessary resources for your serverless functions (e.g., Lambda functions, API Gateway, DynamoDB tables).

Why other options are incorrect:

A. AWS Autoscaling: While Auto Scaling can be integrated with CloudFormation, it's not directly reliant on it. Auto Scaling groups can be created and managed independently of CloudFormation.

D. AWS CodeBuild: CodeBuild is a continuous integration service that can be part of a CloudFormation template, but it doesn't inherently rely on CloudFormation for its own provisioning.

E. AWS Lambda: Similar to CodeBuild, Lambda functions can be defined and deployed using CloudFormation, but Lambda itself is not dependent on CloudFormation for its core functionality.

Example:

You can create a CloudFormation template that defines an Elastic Beanstalk environment, including the EC2 instance types, load balancer configuration, and database settings. CloudFormation will then take care of provisioning and configuring all these resources for you.

Similarly, you can use SAM to define a serverless application with Lambda functions, API Gateway endpoints, and DynamoDB tables. SAM will translate this definition into a CloudFormation template, which will then be used to deploy the entire application infrastructure.

Correct option:

Use SSM Parameter Store

AWS Systems Manager Parameter Store provides secure, hierarchical storage for configuration data management and secrets management. You can store data such as passwords, database strings, and license codes as parameter values. For the given use-case, as the DevOps team does not want to re-deploy the application every time there are configuration changes, so they can use the SSM Parameter Store to store the configuration externally.

Incorrect options:

Use Environment variables - Environment variables provide another way to specify configuration options and credentials, and can be useful for scripting or temporarily setting a named profile as the default. Your application is not running AWS CLI. Since the use-case requires the configuration to be stored securely, so using Environment variables is ruled out, as these are not encrypted at rest and these are visible in clear text in the AWS Console as well as in the response of some actions of the Elastic BeanStalk API.

Use Stage Variables - You can use stage variables for managing multiple release stages for API Gateway, this is not what you are looking for here.

Use S3 - S3 offers the same benefit as the SSM Parameter Store where there are no servers to manage. With S3 you have to set encryption and choose other security options and there are more chances of misconfiguring security if you share your S3 bucket with other objects. You would have to create a custom setup to come close to the parameter store. Use Parameter Store and let AWS handle the rest.

Reference:

https://docs.aws.amazon.com/systems-manager/latest/userguide/systems-manager-paramstore.html

IAM roles and resource-based policies are designed to work within the same AWS partition. Cross-partition access requires additional configurations, such as setting up VPC peering connections or using S3 bucket replication.

Why other options are feasible:

A. Use Resource-based policies and AWS Identity and Access Management (IAM) policies for programmatic-only access to S3 bucket objects: Resource-based policies (bucket policies) and IAM policies can be used together to grant programmatic access to S3 objects across accounts within the same partition.

B. Use Cross-account IAM roles for programmatic and console access to S3 bucket objects: IAM roles can be assumed by users in another account, providing both programmatic and console access to S3 objects within the same partition.

C. Use Access Control List (ACL) and IAM policies for programmatic-only access to S3 bucket objects: While ACLs are generally not recommended for cross-account access, they can be used in conjunction with IAM policies to achieve this for programmatic access within the same partition.

Example:

The Finance department can create an IAM role in their account and grant the Human Resources department's IAM users permission to assume this role. This role can then be attached to a policy that allows access to specific S3 objects within the Finance department's bucket.

The correct answers are:

A. Use Amazon Cognito for user management and facilitating the log-in/sign-up process: Cognito is a fully managed service that handles user authentication, authorization, and management. It provides built-in features for sign-up, sign-in, and MFA, significantly reducing development effort.

C. Use Amazon Cognito to enable Multi-Factor Authentication (MFA) when users log in: Cognito natively supports MFA through various methods like SMS, TOTP (Time-based One-Time Password), and push notifications. You can easily enable MFA in your Cognito user pool configuration without writing custom code.

Why other options are less suitable:

B. Use Amazon SNS to send Multi-Factor Authentication (MFA) code via SMS to mobile app users: While SNS can be used for SMS delivery, you'd still need to build the authentication logic and user management system yourself, increasing development effort.

D. Use Lambda functions and RDS to create a custom solution for user management: This approach requires building the entire user management system from scratch, including authentication, authorization, data storage, and MFA logic. This is the most time-consuming and complex option.

E. Use Lambda functions and DynamoDB to create a custom solution for user management: Similar to option D, this requires building a custom solution, although DynamoDB might be a better choice for storing user data than RDS due to its scalability and flexibility. However, it still involves significant development effort.

Example:

Using Amazon Cognito, you can quickly set up a user pool, configure MFA options (like SMS-based codes), and integrate it with your mobile app. Cognito handles user registration, authentication, and MFA challenges, allowing you to focus on your app's core functionality rather than reinventing the wheel for user management.

The correct answers are:

B. Member accounts will be able to see the Organization trail, but cannot modify or delete it: This is a key aspect of organization trails. They are created and managed by the organization's management account, ensuring centralized control over logging configuration and data access. Member accounts can view the trail details in their CloudTrail console but cannot make changes.

E. By default, CloudTrail tracks only bucket-level actions. To track object-level actions, you need to enable Amazon S3 data events: This is an important consideration when setting up logging for S3 buckets. By default, CloudTrail logs only actions like creating, deleting, or configuring buckets. To log actions on individual objects (like reading, writing, or deleting objects), you need to explicitly enable S3 data events.

Why other options are incorrect:

A. There is nothing called Organization Trail...: This is incorrect. AWS Organizations specifically provides the feature of organization trails for centralized logging across member accounts.

C. Member accounts do not have access to organization trail...: Member accounts can see the organization trail in their CloudTrail console but cannot modify it. They also cannot directly access the S3 bucket where logs are stored unless granted explicit permissions.

D. By default, CloudTrail event log files are not encrypted: This is incorrect. By default, CloudTrail encrypts log files at rest using Server-Side Encryption with Amazon S3-Managed Keys (SSE-S3). You can also choose to use AWS Key Management Service (KMS) for encryption.

AWS CodeBuild is a fully managed build service that scales automatically. This means it can handle fluctuating build demands and run multiple builds concurrently without any manual intervention. It eliminates the need to provision, manage, or scale your own build servers.

Why other options are incorrect:

A. Run CodeBuild in an Auto Scaling group: CodeBuild itself doesn't run within an Auto Scaling group. It's a managed service that handles scaling internally.

B. Enable CodeBuild Auto Scaling: There is no separate setting to "enable" auto scaling in CodeBuild. It's an inherent feature of the service.

C. Choose a high-performance instance type for your CodeBuild instances: While choosing the right instance type is important for performance, it doesn't directly address the need for scaling or parallel builds. CodeBuild will still scale automatically even with a high-performance instance type.

Example:

If your organization suddenly experiences a surge in code commits, CodeBuild will automatically scale up to handle the increased build requests, ensuring that builds are completed quickly and efficiently. You won't need to manually adjust any settings or provision additional resources.

Correct option:

Add an ElastiCache Cluster - To provide a shared data storage for sessions that can be accessed from any individual web server, you can abstract the HTTP sessions from the web servers themselves. A common solution for this is to leverage an ElastiCache service offering which is an In-Memory Key/Value store such as Redis and Memcached.

Incorrect options:

Use Elastic IP - An Elastic IP is a way to give your server a static IP address but won't solve the issue with session data.

Enable RDS read replicas - Amazon RDS server's built-in replication functionality to create a special type of DB instance called a read replica from a source DB instance. A replica continually synchronizes with the master database and hence might add a bit of lag based on how occupied the master database is. It will also add inconsistencies in the current scenario.

Enable Load Balancer stickiness - Sticky sessions are a mechanism to route requests from the same client to the same target. Application Load Balancer supports sticky sessions using load balancer generated cookies. If you enable sticky sessions, the same target receives the request and can use the cookie to recover the session context. But, ElastiCache is much more scalable and better suited in the current scenario.

Reference:

https://aws.amazon.com/caching/session-management/

X-Ray sampling is the most effective way to reduce the volume of data sent to X-Ray, thus lowering costs. It allows you to capture a representative sample of requests, providing enough information for tracing trends while minimizing the amount of data ingested. By default, X-Ray samples the first request each second and 5% of any additional requests. You can adjust the sampling rate as needed.

Why other options are less suitable:

A. Implement a network sampling rule: While network sampling rules can help filter traffic based on specific criteria, they don't directly address the core issue of excessive data volume.

B. Custom configuration for the X-Ray agents: Custom configurations might offer some control, but they are generally less straightforward than simply adjusting the sampling rate.

C. Use Filter Expressions in the X-Ray console: Filter expressions are used to refine the data displayed in the X-Ray console, not to reduce the amount of data ingested.

Example:

If your application is receiving 1000 requests per second, and you set the sampling rate to 1%, X-Ray will only trace 10 requests per second. This significantly reduces the data volume and associated costs while still providing valuable insights into your application's performance.

S3 Object Ownership is a bucket-level setting that allows you to automatically assign ownership of all objects in the bucket to the bucket owner, regardless of who uploads them. This simplifies access management and ensures consistent ownership.

Why other options are incorrect:

A. Use S3 CORS to make the S3 bucket owner, the owner of all objects in the bucket: CORS (Cross-Origin Resource Sharing) is used to manage cross-origin requests for your bucket's objects and is unrelated to object ownership.

B. Use S3 Access Analyzer to identify the owners of all objects and change the ownership to the bucket owner: While S3 Access Analyzer can help identify object owners, it doesn't provide a way to automatically change ownership in bulk.

D. Use Bucket Access Control Lists (ACLs) to control access on S3 bucket and then define its owner: ACLs are used to manage access to buckets and objects, but they don't affect object ownership when objects are uploaded from different accounts.

Example:

[1] Go to the S3 console and select the bucket.

[2] Navigate to the "Permissions" tab and click "Edit" under "Object Ownership."

[3] Choose "Bucket owner enforced" to make the bucket owner the owner of all objects.

With this setting enabled, any new objects uploaded to the bucket will be automatically owned by the bucket owner, even if they are uploaded from a different AWS account.