AWS Certified Developer Associate Practice Test (DVA-C02)

A spike in faults on an X-Ray service map indicates a high rate of errors when one service calls another. The best first step is to drill into that edge or node and review the related traces, CloudWatch logs, and performance metrics to pinpoint exceptions, timeouts, or misconfigurations causing the failures. The other responses either assume capacity problems, postpone investigation, or disable the tracing tool that revealed the issue.

Use a lazy-loading (cache-aside) strategy combined with an appropriate TTL. The cache is populated only when a profile is requested, so seldom-read items do not consume memory or increment cache charges. The TTL automatically expires entries after a set interval, ensuring the cache is periodically refreshed and limiting the window during which stale data could be served. Write-through would duplicate every infrequent update into the cache, adding latency and storing data that may never be read. Read-through offers similar lazy population but requires additional middleware without providing a cost or freshness advantage in this scenario. Not caching would leave the database exposed to the bursty read load you are trying to avoid.

Boto3 wraps every error that comes back from an AWS service-including the Amazon S3 NoSuchBucket error code-in a single Python exception class: botocore.exceptions.ClientError. To determine whether the underlying service error was NoSuchBucket, developers catch ClientError and then check e.response['Error']['Code'] for the string "NoSuchBucket". Names such as BucketNotFound or S3BucketMissing are not defined in the SDK, and although 'NoSuchBucket' appears in the error code, it is not the name of the Python exception class.

Amazon Cognito User Pools provide a fully managed user directory that enables developers to add sign-up and sign-in functionality to web and mobile applications. Upon successful authentication, a user pool issues JSON Web Tokens (JWTs) that the application can use to manage user sessions and authorize API calls. In contrast, Amazon Cognito Identity Pools are used to grant users temporary access to AWS services, and they do not directly provide sign-up/sign-in user directory functionality. AWS IAM Users are meant for managing access to the AWS account for administrators and developers, not for application end-users. The AWS Security Token Service (STS) is a web service that enables you to request temporary, limited-privilege credentials but does not provide a user directory or sign-in UI.

Enforcing encryption at rest can be achieved by storing data using Amazon S3 with Server-Side Encryption (SSE) enabled, where Amazon handles the entire encryption process transparently. The data is decrypted for you when you access it. For encryption in transit, implementing HTTPS connections ensures data transferred between the client and servers is secured. While IAM policies do manage access, they do not directly encrypt data. Additionally, database column encryption only addresses data at rest and doesn't provide encryption in transit.

The service that offers the ability to conduct pre-deployment validation checks on configuration data, ensuring its correctness before the configuration is deployed to the application, is designed for structured configuration management and is capable of deploying configurations without impacting the application state. AWS AppConfig is specifically built for this purpose, enabling developers to create, manage, and quickly deploy configurations. The service also allows for configurations to be validated against a defined schema, and it supports controlled deployment strategies to limit the blast radius of any potential misconfigurations. AWS Secrets Manager's primary use case is to secure, store, and manage access to secrets, and while it's an application configuration tool in the broader sense, it does not offer configuration validation capabilities. AWS Systems Manager Parameter Store provides hierarchical storage for configuration data and secrets management but does not support the same level of validation and controlled deployment options as AppConfig. AWS CodeDeploy is a deployment service that automates application deployments but is not specialized in configuration management, nor does it offer validation features focused on configuration data.

The correct answer is a canary deployment strategy. A canary deployment involves deploying the new version of an application to a small subset of users before fully rolling it out. This allows developers to monitor the performance and stability of the application with live traffic and reduce the risk of introducing issues that could affect all users. In contrast, blue/green deployment swaps the entire environment at once, which doesn't meet the requirement for gradual exposure. A rolling deployment uniformly updates instances but again, does not support the feature of selective traffic routing as described. All-at-once deployment doesn't allow for gradual exposure, as it updates all instances simultaneously.

Using Amazon ElastiCache for Redis is the best option for temporary session-specific data storage for a stateless Lambda function. Redis provides fast, in-memory data storage and retrieval, which is ideal for the ephemeral nature of session data within a single game session without persisting state in the Lambda function itself. Amazon DynamoDB, while durable and scalable, introduces higher single-digit-millisecond latencies and is optimized for long-lived data, making it less suitable when microsecond-level performance is required. Lambda environment variables are intended for static configuration rather than per-invocation data, and mounting Amazon EFS would create cross-invocation persistence that violates stateless principles and could become a scalability bottleneck.

A 502 Bad Gateway error indicates that the server, while acting as a gateway or proxy, received an invalid response from the upstream server. In this scenario, API Gateway is the proxy, and the Lambda function is the upstream server. A misconfigured integration, such as incorrect permissions or if the function doesn't exist, prevents API Gateway from getting a valid response, resulting in a 502 error. A 404 Not Found error would typically mean the API Gateway endpoint itself could not be found, not that the backend integration failed. A 400 Bad Request would indicate a client-side error, like a malformed request. A 500 Internal Server Error is a generic server error, but 502 is more specific to gateway or proxy problems.

To enforce a pause for human sign-off after automated tests, the team should add a manual approval action immediately after the test stage in their pipeline (for example, an Approval action in AWS CodePipeline). This allows the pipeline to progress automatically to testing, then waits for a named approver to review and approve before the production deployment proceeds. Adding the approval before tests would block automated verification, while relying solely on automated tests removes the required human gate. Infrastructure-as-code tools alone do not provide the approval gate without a dedicated manual approval step in the pipeline.

The correct approach to ensure idempotent operations is to issue a unique transaction identifier (often called an idempotency key) for each financial operation. The service stores the first result associated with that identifier and returns the same response for subsequent requests that present the same key, preventing duplicate charges. Timestamps do not guarantee idempotency because retries can reuse the same timestamp or suffer from clock skew. Checking previous HTTP status codes is unreliable because network issues can obscure responses, and status codes are not part of the retried request. AWS X-Ray provides distributed tracing and observability; it does not implement request de-duplication or idempotency.

Amazon DynamoDB Accelerator (DAX) is a fully managed, in-memory cache that is API-compatible with DynamoDB. Because it sits transparently between the application and DynamoDB, the developer can redirect existing SDK calls to the DAX endpoint with minimal code changes. DAX automatically handles read-through and write-through caching, absorbs bursty read traffic, and can reduce response times from milliseconds to microseconds, preventing throttling of the underlying table. ElastiCache is a general-purpose in-memory store, but it requires additional application logic for cache population and invalidation. RDS Read Replicas, AWS DataSync, and Amazon S3 Transfer Acceleration do not provide a managed cache for DynamoDB reads.

Amazon Elastic Container Registry (ECR) is a fully-managed Docker container registry that makes it easy to store, manage, and deploy Docker container images. It's integrated with Amazon ECS, allowing for seamless deployment of images to containers in a secure, scalable, and reliable manner. ECR also provides image scanning to identify software vulnerabilities, and it supports image versioning through tags, making it the optimal choice for managing container images on AWS.

Amazon Elastic Block Store (EBS) is primarily for block storage, not for storing container images. AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers, and it does not store container images. Amazon Simple Storage Service (S3) can store any type of object, but it's not the most integrated solution for managing Docker container images for use with ECS.

Amazon API Gateway mock integrations enable developers to set up the entire API interface without having to have the backend services available. This feature allows for the creation of integration responses and error cases to test how the front end handles these situations. This capability is vital during development for ensuring that the application logic is properly handling requests and responses. AWS Step Functions are not designed for mocking API interfaces but for orchestrating microservices, applications and workflows. CloudFormation, while used for infrastructure as code, does not provide a direct way to mock service responses. Amazon EventBridge schedules events but is not intended for simulating backend responses.

Assigning a role to the virtual server that specifies the permissions for the object storage service is the most secure and manageably sound approach, allowing the application to access resources without the need to embed or manually enter credentials. This method leverages the capability of the virtual server to use temporary credentials that are automatically rotated, preventing the risks associated with hardcoding or exposing long-term credentials.

The correct option is AWS Serverless Application Model (SAM). AWS SAM is an open-source framework specifically designed to streamline the development and deployment of serverless applications. It uses a simplified YAML or JSON template syntax, which is an extension of AWS CloudFormation, to define serverless resources like functions, APIs, and event source mappings. SAM also has built-in features for managing different deployment environments, directly meeting the user's requirements.

• AWS CloudFormation is the underlying Infrastructure as Code (IaC) service that SAM uses. While you can define the entire application using native CloudFormation, its syntax is more verbose for serverless resources. SAM provides a specialized, higher-level abstraction that simplifies the template, making it the more appropriate and efficient tool for this specific scenario.
• The AWS Cloud Development Kit (CDK) is an IaC framework that uses familiar programming languages (like Python or TypeScript) to define cloud resources. The question specifically asks for a tool that uses a declarative 'template format' (like YAML/JSON), making SAM the better fit over the programmatic approach of the CDK.
• AWS CodeDeploy is a service that automates application deployments to various compute services, including AWS Lambda. It is focused on the deployment process itself, such as managing traffic-shifting strategies like canary and blue/green, rather than defining the infrastructure resources in a template.

The most effective method involves setting up a new deployment stage within the service that provides the endpoints, which in this context is Amazon API Gateway. Stages enable you to create independent environments for development and production. Setting up stage variables, specific authentication methods, and throttling rules for the development environment ensures that testing does not interfere with the production environment. This approach allows the team to test new code changes under conditions that closely mimic the live setup without risking the integrity and stability of the production systems.

The correct answer is AWS CodePipeline because it is specifically designed to orchestrate and automate the steps required to release software changes frequently and reliably, including the scenarios in which code is automatically processed upon updates. While AWS CodeBuild and AWS CodeDeploy are part of the CI/CD process, they do not offer the end-to-end automation of code updates from version control systems to deployment as a standalone service. Amazon S3 is a storage service and does not handle automation of build, test, and deployment workflows.

When updating a critical backend service such as a serverless function, it is important to have a strategy that allows for gradual transition of traffic to enable monitoring and quick rollback without affecting end-users. Utilizing the cloud provider's feature to create a new service version and update its pointer to control the percentage of traffic shift is an effective canary deployment strategy. It enables the developer to observe the new version's behavior and revert to the old version if needed. This practice ensures that any potential issues with the new version do not impact all users at once. Direct deployment without traffic control, updating configuration variables, or deploying through a new API stage would not provide the same level of control and safety for this operation.

Enabling a dead-letter queue (DLQ) for the Lambda function is the best proactive measure to handle failed execution messages. The DLQ pattern is a well-established way to isolate messages that a Lambda function fails to process, allowing them to be moved to a separate queue and not interfere with the continuous flow of new messages being processed. This aids in analyzing and debugging issues without losing the context of the failure. This is preferable to re-attempting processing within the Lambda function which might cause repeated failures and does not segregate the problematic messages, or increasing resources which might not address the root cause of processing failures.