AWS Certified Developer Associate Practice Test (DVA-C02)

The correct service for tracking requests in a distributed system, providing an end-to-end view of requests as they travel through the application, is intended to enable developers to analyze and debug systems. It helps in creating a map of services used by an application with detailed latency information for each service component. The other options listed are not primarily used for application tracing. CloudTrail is for auditing API use, Inspector for security assessments, and CloudFormation for infrastructure management.

Secrets Manager is the service specifically created for securely managing secrets, such as database credentials and API keys, which includes capabilities like secrets rotation. Systems Manager provides broader configuration management but lacks the direct focus on secrets lifecycle management. The other options are either incorrect because they do not exist (Amazon Secure Storage Service) or are used for key management rather than secret rotation (Key Management Service).

The correct approach is to create an alarm that monitors the service's 'ConcurrentExecutions' metric, signaling an impending breach of the allowable parallel execution threshold. The creation of such an alarm can be configured to send a notification to a designated topic within a notification service, which dispatches the alert to the developer. This is a proactive and automated way to monitor for critical thresholds and ensure the developer is promptly notified. The other approaches either do not provide real-time alerts, are not designed for this specific monitoring purpose, or involve manual processes that would not be immediate.

The correct tool for invoking and debugging serverless functions locally is AWS SAM CLI. It allows developers to test their Lambda functions in an environment similar to the actual AWS runtime. AWS CodePipeline and AWS CodeDeploy are used in deployment cycles and do not have the capability to run or test functions locally. The AWS SDKs are for interacting with AWS services in your application code, not for local invocation of functions.

A bucket policy is the most suitable option for this scenario because it allows you to grant precise permissions, such as readonly access, to resources in your bucket to another account without creating individual user accounts or sharing security credentials. This method adheres to the principle of least privilege, ensuring you're not granting any more permissions than necessary. The alternatives either grant overly broad permissions, do not cater to cross-account access efficiently, or would require managing credentials directly, which is not advised.

The Parameter Store, part of Systems Manager, is the appropriate service for managing configuration data and secrets. It allows storing of parameters as plain text or encrypted data, making it suitable for handling sensitive information. It supports version tracking and notifications on parameter changes, and can be accessed dynamically at runtime. This service also provides granular permissions via IAM for controlling access, and integrates with KMS for encryption, ensuring a high security standard without the need to redeploy applications when configurations change.

In the scenario given, a third-party service that is intermittently unavailable would benefit from implementing retries with exponential backoff and jitter. The exponential backoff method systematically increases the wait time between retries after a failed attempt to call the third-party service. Adding jitter reduces the probability that multiple instances of your application will attempt to retry at the same moment, avoiding simultaneous retries that can lead to the 'thundering herd' problem when the service recovers. This can be particularly useful for distributed systems that rely on external services to ensure they can continue to operate effectively during transient outages. Using a circuit breaker does address an unreliable service, but the question specifically asks for a pattern that involves retry logic. Increasing read and write capacity or immediate recursive retries do not address the underlying issue of the service's unpredictability and may worsen it by potentially overloading the service when it comes back online.

The service that enables developers to add detailed annotations to the execution flow of an application, including serverless functions, is X-Ray. By using X-Ray, you can insert metadata that provides additional context within trace segments, offering insights into the application's behavior and state changes. This level of observability is crucial for effective troubleshooting. The other services mentioned do not offer this annotation capability: CloudTrail focuses on recording AWS API calls, CloudWatch primarily deals with monitoring and metrics, and Step Functions orchestrate different serverless components rather than provide annotations for tracing.

A canary release approach involves directing a small amount of live traffic to the new feature to monitor its impact and performance before making it generally available to all users. This method is preferred for services like web and mobile application backends since it reduces the risk of disruptions if unexpected issues arise. Creating a new stage with the same name or applying changes directly to the existing production environment could potentially disrupt all users if there are issues with the update. While functional testing in an isolated environment is an important step, it does not provide a mechanism for gradual exposure to the live user base.

Attaching an IAM role to the virtual server instance is considered a security best practice as it provides temporary credentials that are automatically rotated and do not require management or embedding in the application's code. Directly embedding static long-term credentials or using root account credentials is strongly discouraged due to the heightened risk of compromise and the likelihood of violating the principle of least privilege.

Attaching an instance profile that contains an IAM role with the necessary S3 permissions to your EC2 instances is the recommended solution for this scenario. This allows the application to assume the IAM role and obtain temporary credentials, which can be used to access the S3 bucket. The use of an instance profile ensures that the EC2 instance can securely make API calls to AWS services on behalf of the user that assumed the role. Unlike static credentials, these permissions are automatically rotated and managed by AWS. Creating individual IAM users is not scalable for multiple instances, and hard-coding credentials is a security risk and violates the AWS recommended practice of not embedding secrets in code. Using a resource-based policy on the S3 bucket is not possible, as it cannot grant the necessary EC2 instance permissions to assume a role.

The correct answer is false. Amazon CloudWatch Logs Insights does indeed support time-based queries, which allows developers to isolate and examine log data from specific time frames. This is a critical feature for troubleshooting to pinpoint when specific incidents or anomalies occurred. Offering 'true' as an option plays on the possibility of lacking time-based query features, which could be a common pitfall for someone not familiar with CloudWatch Logs Insights' capabilities.

Structured logging is an approach where developers log data in a consistent, predefined format, like JSON with key-value pairs. This format is beneficial because it makes it easier to search, filter, and analyze log data, allowing teams to isolate issues rapidly and efficiently. The addition of structured contextual information such as execution runtime version and unique identifiers facilitates targeted querying which is highly valuable during troubleshooting. Alternatives like unstructured text logs, only capturing error messages, or sole reliance on distributed tracing services without context-rich application-specific logs would not provide the same ease of filtering and detail required for efficient debugging.

Using Amazon S3 event notifications to trigger a Lambda function directly when a new object is created provides the immediate response required for the developer's use case. It is the most direct method for event-driven integration between Amazon S3 and AWS Lambda. Configuring a Lambda function with an Amazon S3 event as the trigger allows the function to execute/respond as soon as the specified S3 event, such as a PUT, occurs.

Using Amazon SNS or Amazon SQS would introduce additional steps and potential delays, as it would require the S3 event to first publish to a topic or send a message to a queue, which would then trigger the Lambda function. This is less efficient and suitable for cases where indirect coupling or message filtering is required. Amazon S3 event notifications to Amazon CloudWatch Events/EventBridge would also introduce unnecessary complexity for this direct integration use case.

Allocating more memory to the serverless function can lead to an improvement in the compute capacity available to the function. Since the compute capacity scales with the memory size, this can decrease execution time for complex tasks and large data payloads. Thus, timeouts are less likely to occur. Extending the function's execution time may alleviate symptoms but does not address performance inefficiency and may increase costs without solving the real issue. Removing code lines is a practice of code optimization, but without a strategy, it can be ineffective at solving the problem. Moreover, it is not a configuration setting that can be adjusted for performance tuning. Diagnostic tracing with a tool can highlight performance issues, but it does not directly improve execution time.