Amazon DynamoDB is a fully managed NoSQL database service that provides fast and consistent performance with seamless scalability. The developer can store each employee’s contact information in a DynamoDB table along with the object keys for the photos stored in Amazon S3. Amazon S3 is an object storage service that offers industry-leading scalability, data availability, security, and performance. The developer can use AWS APIs to search and retrieve the employee details and photos from DynamoDB and S3.

Global Secondary Index (GSI): GSIs enable alternative query patterns on a DynamoDB table by using different partition and sort keys.

Addressing Query Bottleneck: By making the slow-query attribute the GSI ' s partition key, you optimize queries on that attribute.

Scalability: GSIs automatically scale to handle increasing data volumes.

This solution will meet the requirements by creating a Lambda function execution role, which is an IAM role that grants permissions to a Lambda function to access AWS resources such as Amazon S3 objects. The developer can attach a policy to the role that grants access to specific objects in the S3 bucket that are required by the application, following the principle of least privilege. Option A is not optimal because it will hardcode the credentials that are required to access S3 objects in the application code, which is insecure and difficult to maintain. Option B is not optimal because it will create a secret access key and access key ID with permission to access the S3 bucket, which will introduce additional security risks and complexity for storing and managing credentials. Option D is not optimal because it will store the secret access key and access key ID as environment variables in Lambda, which is also insecure and difficult to maintain.

For CloudFormation or AWS SAM templates that reference local artifacts, the source code must be uploaded to Amazon S3 before CloudFormation can deploy it. The aws cloudformation package command scans the template for local artifacts, uploads those artifacts to an S3 bucket, and outputs a modified template that references the S3 object locations. That packaged template can then be deployed with aws cloudformation deploy. There is no standard aws cloudformation compile, aws lambda zip, or aws serverless create-package command for this workflow. The Transform: AWS::Serverless-2016-10-31 declaration means SAM resources are being transformed through CloudFormation, but local code artifacts still need packaging. AWS documentation describes aws cloudformation package as the command for uploading local artifacts referenced by a CloudFormation template to S3. ( AWS Documentation )

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Amazon DynamoDB is a fully managed NoSQL database service that can store and retrieve any amount of data with high availability and performance. DynamoDB can handle concurrent requests from multiple IoT devices without throttling or data loss. To prevent duplicate requests from causing inconsistencies or data loss, the Lambda function can use DynamoDB conditional writes to check if the unique identifier for each request already exists in the table before processing the request. If the identifier exists, the function can skip or abort the request; otherwise, it can process the request and store the identifier in the table. Reference: Using conditional writes

The requirements describe a Git-driven workflow where each new branch automatically gets its own deployment with minimal setup and management. AWS Amplify is designed for exactly this use case for web (and supporting mobile backends): it integrates directly with Git providers and offers continuous deployment . Amplify’s feature branch deployments automatically create a separate deployment (and typically a distinct preview URL) for each connected branch, enabling developers to test and review changes per branch without manually provisioning environments.

With Amplify, the developer connects the repository once, selects the branches to deploy, and Amplify automatically builds and deploys the application whenever code changes are pushed. When a new branch is added, Amplify can be configured to deploy it as a separate environment, providing isolated testing, previewing, and validation. This dramatically reduces operational overhead because Amplify manages build settings, hosting, SSL, and branch-based environments without needing custom CI/CD wiring.

Option A (ECR + ECS + GitHub Actions) can support branch deployments, but it requires substantial operational work: container build pipelines, task/service management, networking, scaling, and environment isolation per branch. Option B (Elastic Beanstalk) can manage deployments, but branch-based automatic deployments are not as native; creating separate environments and managing application versions typically requires more manual steps or additional automation. Option C (Lambda aliases) is not a general-purpose web/mobile deployment approach and does not naturally map “new Git branch” to an isolated deployment environment without significant custom tooling.

Therefore, D is the best fit with the least operational overhead: use AWS Amplify with feature branch deployments to automatically create and manage separate deployments for Git branches.

The correct answer is D because the error is occurring when the Lambda function calls the third-party fulfillment service , and the log message clearly states “Rate limit exceeded from fulfillment service.” This indicates the downstream system is throttling requests. In AWS best practices for integrating with external services, developers should use retries with exponential backoff to handle transient throttling and service rate-limit responses gracefully.

API Gateway showing 5XX errors instead of 4XX errors also makes sense in this design. The client request reaches API Gateway successfully, and API Gateway invokes Lambda. The failure happens inside the backend processing path when Lambda calls the external fulfillment API. Because the backend integration fails, API Gateway surfaces a server-side error rather than a client-side error.

Option A is incorrect because increasing API Gateway throttling limits would allow even more requests to reach Lambda, which could worsen the rate-limit issue against the external fulfillment service. Option B is incorrect because provisioned concurrency helps with cold starts and startup latency, not downstream rate limiting. Option C is incorrect because increasing Lambda memory might improve CPU performance, but it does not solve an external API returning rate-limit responses.

By implementing exponential backoff and retry logic , the Lambda function can reduce the frequency of immediate repeated requests to the third-party service, give the service time to recover, and improve overall request success. This approach aligns with AWS guidance for fault tolerance and resilient service integration patterns when working with throttled downstream dependencies.

CodeBuild batch builds are designed for running multiple related builds together and can model dependencies between build tasks. This fits the requirement for simultaneous, interdependent builds across web, mobile, and API components. Using a combination of reserved capacity fleets and On-Demand capacity helps optimize cost and availability. Running all builds as separate projects with only On-Demand capacity misses the reserved fleet cost optimization requirement. Assigning separate projects manually creates more operational overhead and does not naturally coordinate dependencies. Running everything sequentially contradicts the requirement to run builds simultaneously and slows delivery. AWS CodeBuild documentation includes batch build concepts and reserved capacity fleet support, including fleet settings for batch builds. ( AWS Documentation )

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Storing secrets in environment variables encrypted with AWS KMS is a standard secure practice for Lambda. By using a Customer Managed Key (CMK), you can audit every decryption request via CloudTrail. This ensures the keys are never in plaintext in the management console or code. Options B and C are highly insecure as they expose keys in URLs or client-side code. Tags (Option D) are not meant for sensitive secrets and are not encrypted.

Understand the Problem: The existing DynamoDB table has email_address as the partition key. Searching by customer_type requires a different data access pattern. We need an efficient way to query for partial matches on email_address based on customer_type.

Why Global Secondary Index (GSI):

GSIs allow you to define a different partition key and sort key from the main table, enabling new query patterns.

In this case, having customer_type as the GSI ' s partition key lets you group all emails with the same customer type together.

Using email_address as the sort key allows ordering within each customer type, facilitating the partial matching.

Querying the GSI:

You ' ll perform a query operation on the GSI, not the original table.

Use the begins_with key condition expression on the GSI ' s sort key (email_address) to find partial matches as the user types in the customer_type field.

Maximum concurrency for SQS as an event source allows customers to control the maximum concurrent invokes by the SQS event source1. When multiple SQS event sources are configured to a function, customers can control the maximum concurrent invokes of individual SQS event source1.

In this scenario, the developer needs to resolve the issue of the third-party API frequently returning an HTTP 429 Too Many Requests error message, which prevents a significant number of messages from being processed successfully. To achieve this, the developer can follow these steps:

Find out the documented rate limits of the third-party API, which specify how many requests can be made in a given time period.

Configure maximum concurrency on the SQS event source based on the rate limits of the third-party API. This will limit the number of concurrent invokes by the SQS event source and prevent exceeding the rate limits of the third-party API.

Test and monitor the application performance and adjust the maximum concurrency value as needed.

By using this solution, the developer can reduce the frequency of HTTP 429 errors and improve the message processing success rate. The developer can also avoid throttling or blocking by the third-party API.

Pseudo Parameters: CloudFormation provides pseudo parameters that reference runtime context, including the current AWS Region.

Operational Efficiency: The AWS::Region pseudo parameter offers the most direct and self-contained way to obtain the Region dynamically within the template.