Free AWS Certified Developer Associate DVA-C02 Practice Test

The correct approach is to integrate the cloud application with the on-premises directory service using a federation protocol such as SAML 2.0. IAM supports federation with SAML, which allows users to authenticate using their existing corporate credentials without storing those credentials in the cloud. While Cognito is also a service that supports federation, IAM with SAML is specifically designed to work seamlessly with corporate directories like Active Directory and is hence the better-suited choice for this particular use case. The other options mentioned do not directly address the requirement of federating with an existing on-premises directory service.

Utilizing CodeCommit provides a secure, highly scalable, managed source control service that hosts private Git repositories. This service is specifically designed to work seamlessly with other services on the platform, ensuring a cohesive development experience. Conversely, GitHub and Bitbucket, while popular for source control, are third-party services that do not offer the same level of integration as native platform services. CodePipeline is focused on CI/CD processes and uses repositories rather than serving as a source control hosting service.

Creating a dashboard in Amazon CloudWatch with customized widgets provides the optimal solution for monitoring system health. It allows the team to view multiple metrics concurrently, offering a comprehensive, real-time glance at application performance across services. Although Amazon CloudWatch Alarms, Logs Insights, and AWS X-Ray offer valuable data examination and notifications, they do not provide a singular, unified view ideal for immediate and holistic understanding of system health.

The correct service for the scenario described is CodeBuild, which has the capacity to automatically execute unit tests and other commands each time there is an update to the repository, ensuring that new commits don't break the application. CodeDeploy is mainly focused on deployment tasks and doesn't inherently run tests. CodePipeline is responsible for orchestrating the flow of updates, rather than executing the tests themselves. CodeCommit is a source control service and does not directly handle testing; it would integrate with other tools to achieve this functionality.

To create a custom metric for failed profile update attempts due to validation errors, the developer should use the CloudWatch PutMetricData API. This action allows for publishing of custom metrics directly to Amazon CloudWatch. Using the AWS SDK to increment a custom metric ensures that each specific scenario such as 'failed update attempts due to validation errors' is tracked accurately. While creating a new log stream and logging error messages to CloudWatch Logs does provide persistence of error data, it does not fulfill the requirement of creating and utilizing a custom metric. Similarly, using AWS X-Ray to trace requests would provide insights into the request flow and latency but would not create a custom metric. Finally, emitting logs with the embedded metric format (EMF) would be a valid approach for collecting custom metrics from Lambda logs, but since the question specifies using the PutMetricData API, the correct approach is to use the AWS SDK to call PutMetricData.

The statement is true because AWS Private Certificate Authority (PCA) is a private certificate authority service that enables you to issue and manage private Secure Sockets Layer/Transport Layer Security (SSL/TLS) certificates that are used to establish secure network communications and to secure applications. These certificates can be used within an organization to securely authenticate and encrypt traffic, even in environments without internet access, such as a private corporate intranet or an isolated virtual private cloud. It's important to understand that while PCA allows you to manage these private certificates, public trust is not established through AWS PCA as it would be with a public certificate authority.

Using the batch operations API of AWS SDKs, such as BatchGetItem in the case of Amazon DynamoDB, allows the application to retrieve up to 100 items from one or more DynamoDB tables in a single operation. This reduces the number of network calls compared to individual GetItem requests for each item and results in less network latency. The use of Query or Scan would not be as efficient because they are designed for different purposes. Query is used to retrieve items using a key condition expression, and Scan reads every item in a table, which can be less efficient for frequently accessed individual items. While PutItem is used for data insertion, not retrieval, hence it is not suitable for the scenario given.

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.

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.

Customer-managed keys provide the developer with the authority to intricately configure the key's policy and handle its rotation, deletion, and lifecycle. This autonomy is a primary differentiator from the AWS managed keys where the control over these aspects is limited and managed mostly by the cloud service provider.

Amazon Cognito Identity Pools (Federated Identities) give developers the ability to create unique identities for users and federate them with external identity providers, such as social media platforms (Facebook, Google, Amazon, etc.). With these unique identities, the application can secure temporary credentials allowing users to directly interact with cloud service APIs. This is necessary for direct access to cloud services post-authentication. In contrast, User Pools are mainly to create and manage a directory of app users and they don't directly facilitate access to cloud service APIs. While STS is indeed used for temporary credentials, it by itself doesn't cater to social identity provider federation nor provide directory services. AWS QuickSight is a business analytics service and not pertinent to identity management.

The correct method for a developer to directly insert custom telemetry from within the application is to use the monitoring service's API to publish these metrics. This technique is immediate, requires minimal additional setup, and enables straightforward integration into the code base. The other options, such as utilizing configuration management tools, system agents for metric collection, or SDKs that simplify API interactions, are valid in certain contexts but may be more indirect or less efficient for the task of custom metric emission directly from the application.

The asynchronous interaction design pattern is the correct choice in this scenario, as it allows submitted tasks to be queued for processing independently from the submission operation. This ensures that the end-users are not delayed while the images are being processed, which is particularly beneficial during times of high load. A synchronous design pattern would require the users to wait for each processing task to complete before continuing, leading to possible delays and a poor user experience during peak times. Event-driven architectures can encompass both synchronous and asynchronous processing but do not in themselves distinguish the type of responsiveness to user submissions required here.

Implementing a comprehensive strategy that incorporates structured logs, detailed metrics, and distributed tracing provides full observability into the application's health and performance. Structured logs allow capturing consistent and context-rich logging data, detailed metrics offer quantitative information about the system's operation, and distributed tracing enables tracking of individual requests as they flow through the system, linking the metrics and logs to provide a complete picture of the application's behavior. Caching data optimizes read times but doesn't contribute to observability. Similarly, setting up a single metrics dashboard provides a view but not in-depth tracking of individual requests. Configuring alerts for errors ensures an incident response but does not facilitate ongoing analysis of system performance and behavior.

An asynchronous communication pattern is suitable for this scenario because it allows the order processing to complete without waiting for the email notification to be sent. The email sending process can be offloaded to a separate service, such as a message queue that triggers a Lambda function to send emails. This decoupling ensures that the order processing system remains responsive even during times of high load. A synchronous pattern would require each order to wait until the email is sent before marking the order as complete, potentially causing delays and backlogs in the order processing system.