AWS Certified Developer Associate Practice Test (DVA-C02)

Stage variables in AWS's web service interface solution allow for the configuration of environment-specific settings without altering the backend Lambda functions. By defining different stage variables for development and production, developers can alter the behavior of the interface, such as resource paths or logging levels, without duplicating Lambda functions or creating multiple interfaces. This approach is cost-effective and maintains a single codebase for backend processing. Incorrect answers suggest inefficient practices like deploying separate sets of Lambda functions or modifying backend code for each environment, which could lead to increased costs and complexity.

Yes, employing a publish/subscribe service to distribute messages simultaneously to several message queueing services is a well-established method for decoupling components within a microservices architecture, enhancing fault tolerance and resilience. When a publisher sends a message, the service fans it out to all subscribed queues, each corresponding to a different consumer service. Should one consumer service experience an issue, it doesn't affect the ability of other services to continue processing their respective messages. This design pattern prevents any single point of failure from bringing down the entire system, thereby improving overall fault tolerance.

The service designed for end-to-end tracing of requests across a distributed system, including both serverless and server-based components, is AWS X-Ray. AWS X-Ray collects data about requests and enables developers to analyze and debug production, distributed applications, such as those built using a microservices architecture. The alternatives mentioned either focus on API call auditing, application and infrastructure monitoring, or security assessments, but do not provide the distributed request tracing capabilities needed for the team's requirements.

Amazon CloudWatch is the correct answer because it is explicitly designed to monitor applications and infrastructure, allowing you to collect metrics, create alarms, and access logs in a unified way. This aids in observing system performance and operational health. Amazon Kinesis focuses on real-time data streaming and analysis, not on the comprehensive monitoring capabilities provided by CloudWatch. AWS CloudTrail is a governance, compliance, and auditing service that records API calls, including actions taken by AWS services, but does not offer the extensive metric and alarm features of CloudWatch. While Amazon S3 can be used for storing log data, it is mainly a storage service and lacks the active monitoring and alerting facilities that CloudWatch offers.

Pull request approvals are a feature in AWS CodeCommit that teams can use to enforce code review policies. By requiring a certain number of approvals before changes can be merged into a particular branch, like the master branch, teams can ensure that multiple team members have reviewed the changes. Branch protections in other services, such as GitHub, may offer similar functionality but are not the correct answer here because the scenario specifically involves AWS CodeCommit. The pre-signed commit policy is not applicable here, as it is not a feature of AWS CodeCommit. Lastly, the concept of 'stage locking' does not directly apply to Git-based version control systems and thus is incorrect in this context.

The Blue/Green deployment strategy allows a new version of the application to be deployed alongside the old version before a full-scale switch. This method minimizes downtime and provides an immediate rollback mechanism if the new version fails, by simply routing traffic back to the old environment. Incremental deployments, such as Rolling and Rolling with additional batch, would still have a limited impact on availability as instances are updated in batches and do not provide an immediate rollback capability. Immutable deployments, while minimizing downtime by provisioning new instances, do not provide the immediate rollback capability that Blue/Green deployments offer.

Implementing a 'read-through' caching strategy will minimize read latency and reduce the load on the DynamoDB table by caching the data after the first read from the database. Subsequent reads are served directly from the cache, which decreases the burden on the table. As the cache is populated on-demand during a database read, it ensures that the data in the cache is consistent with the database. Other caching strategies might not be as effective for this scenario. For example, write-through caching is more suited for write-heavy applications, lazy loading can lead to stale data if not managed properly, and TTL doesn't directly address the problem of high read load and throttling.

AWS Lambda can be directly triggered by Amazon DynamoDB Streams when data modification occurs in the DynamoDB table, enabling real-time processing of the changes. However, the scenario described involves processing a stream of incoming data, not changes to a DynamoDB table. Amazon Kinesis is a service for real-time processing of large, streaming data, and Lambda functions can be triggered by Kinesis Data Streams. Therefore, using Amazon Kinesis Data Streams to trigger the Lambda function is the correct answer because it aligns with the requirement of processing incoming data stream.

AWS Lambda Aliases enable you to route traffic to different versions of a Lambda function. Aliases are like pointers and can be changed to point to different function versions as needed. This is useful for integration testing as you can maintain separate testing environments without changing the function's ARN, thus ensuring the stability and consistency of your application during the testing phase. Using different function versions or Stage variables are incorrect choices because while they can point to different configuration settings or Lambda versions, they do not offer the same aliasing capability for routing across different environments without altering the ARN.

The HTTP 400 Bad Request status code indicates that the server could not understand the request due to invalid syntax. When an API Gateway receives a request with a well-formed payload that does not meet the criteria or expected values, it responds with this error code. It suggests that the client should not repeat this request without modification. The 200 OK status code indicates that the request has succeeded, which is not appropriate in this context. A 500 Internal Server Error suggests a server-side issue, which would not be the correct response for a client error in formatting. Finally, 502 Bad Gateway implies that the server, while acting as a gateway or proxy, received an invalid response from the upstream server, which does not fit the scenario described.

API Gateway Custom Domain Names are used to define a custom domain for your API Gateway APIs and map individual paths to different stages in your API. This functionality enables serverless applications to be accessible via human-friendly URLs. Custom Domain Names are distinctly different from Stages, which are essentially different snapshots of your API and do not inherently provide the means to use custom URLs.

When an EC2 instance is launched with an assigned role, temporary security credentials are provided automatically through the Instance Metadata Service (IMDS). The application can then make secure requests to other services without explicit credential management. Manually invoking GetSessionToken is not necessary since automatic credential fetching is already available. Storing access and secret keys is insecure, and embedding them in app code is not advisable as it poses significant security risks.

The statement is false. While exponential backoff is a strategy that progressively increases the wait time between consecutive retry attempts, thus reducing the load on the server and giving it time to recover, it might still lead to the so-called 'retry-storm' if many clients are trying to reconnect at the same intervals. By adding jitter, or randomization to the wait times, the retries are more evenly spread out, which further decreases the likelihood of overwhelming the system. This combined approach reduces the chance of synchronized retries across multiple instances and is generally acknowledged as a best practice for fault-tolerant design within distributed systems.

The best practice for deploying updates without impacting the live user environment is to deploy the changes to a separate staging environment. This approach provides a testing ground that simulates the live environment but prevents any disruptions or unintended consequences from reaching actual users. Directly updating the live environment can lead to untested or faulty code impacting users, while choosing a completely unrelated environment may not provide accurate testing feedback. Testing IAM role permissions is part of securing the deployments, but it does not replace the need for environment-specific deployment for testing changes.

A resource-based policy, specifically an S3 bucket policy, is the correct method to directly grant permissions on the S3 bucket to entities in another AWS account without sharing any user credentials. This policy allows the external account to assume the necessary permissions. While a service control policy (SCP) applies to all IAM entities in an AWS Organizations account and does not grant permissions to outside accounts, and IAM user policies are attached directly to users within your account, not to resources.