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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Development with AWS Services | 32% | - Use data stores in application development
|
| Topic 2: Deployment | 24% | - Test applications in development environments
|
| Topic 3: Troubleshooting and Optimization | 18% | - Optimize applications by using AWS services and features
|
| Topic 4: Security | 26% | - Implement encryption by using AWS services
|
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835. Frage
A company is building a micro services app1 cation that consists of many AWS Lambda functions. The development team wants to use AWS Serverless Application Model (AWS SAM) templates to automatically test the Lambda functions. The development team plans to test a small percentage of traffic that is directed to new updates before the team commits to a full deployment of the application.
Which combination of steps will meet these requirements in the MOST operationally efficient way? (Select TWO.)
Antwort: C,D
Begründung:
This solution will meet the requirements by using AWS Serverless Application Model (AWS SAM) templates and gradual deployments to automatically test the Lambda functions. AWS SAM templates are configuration files that define serverless applications and resources such as Lambda functions. Gradual deployments are a feature of AWS SAM that enable deploying new versions of Lambda functions incrementally, shifting traffic gradually, and performing validation tests during deployment. The developer can enable gradual deployments through AWS SAM templates by adding a DeploymentPreference property to each Lambda function resource in the template. The developer can set the deployment preference type to Canary10Percent30Minutes, which means that 10 percent of traffic will be shifted to the new version of the Lambda function for 30 minutes before shifting 100 percent of traffic. The developer can also use hooks to test the deployment, which are custom Lambda functions that run before or after traffic shifting and perform validation tests or rollback actions.
836. Frage
A company hosts its application on AWS. The application runs on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. The cluster runs behind an Application Load Balancer The application stores data in an Amazon Aurora database A developer encrypts and manages database credentials inside the application The company wants to use a more secure credential storage method and implement periodic credential rotation.
Which solution will meet these requirements with the LEAST operational overhead?
Antwort: D
837. Frage
A developer is building various microservices for an application that will run on Amazon EC2 instances. The developer needs to monitor the end-to-end view of the requests between the microservices and debug any issues in the various microservices.
What should the developer do to accomplish these tasks?
Antwort: A
Begründung:
To monitor end-to-end requests and debug issues across microservices, you need distributed tracing. Among AWS tools, AWS X-Ray is specifically built for this use case.
Step-by-Step Breakdown:
Step 1: Understand the Requirement
The developer needs:
End-to-end request tracing across microservices
Debugging capability for performance issues or failures
This points directly to a distributed tracing solution rather than just logs or metrics.
Step 2: Evaluate the Options
Option A: Amazon CloudWatch
CloudWatch is powerful for metrics, logs, and alerts.
But it does not provide distributed tracing or request path visualization between microservices.
So, it's not sufficient alone for the requirement.
Option B: AWS CloudTrail
CloudTrail tracks API calls made through the AWS Management Console, CLI, SDKs.
It is meant for auditing and governance, not microservices request tracing.
Not suitable for debugging microservices interactions.
Option C: AWS X-Ray SDK with X-Ray service map
Purpose-built for distributed tracing
Automatically collects data about requests as they travel through your microservices You can instrument your code with the AWS X-Ray SDK in Java, Node.js, Python, Go, .NET, etc.
Visualizes requests using a service map, showing latency, errors, and time spent in downstream services.
How it works:
Instrument each microservice using the AWS X-Ray SDK.
Use the SDK to trace requests, propagate trace headers across services.
The X-Ray daemon collects and sends trace data to the X-Ray service.
You can view the service map, see bottlenecks, error rates, etc.
Option D: AWS Health
AWS Health provides information on AWS service outages or account-level events.
It does not monitor your application or microservices health or request flows.
Correct Choice: C is the only option that meets the developer's requirement to monitor end-to-end request paths and debug issues in a microservices architecture.
AWS Developer References:
AWS X-Ray Documentation: https://docs.aws.amazon.com/xray/latest/devguide/aws-xray.html Instrumenting your application: https://docs.aws.amazon.com/xray/latest/devguide/xray-sdk.html Viewing the Service Map: https://docs.aws.amazon.com/xray/latest/devguide/xray-console-service-map.html Tracing Microservices with AWS X-Ray: https://aws.amazon.com/blogs/compute/tracing-microservices-aws- x-ray/
838. Frage
A developer is troubleshooting an application in an integration environment. In the application, an Amazon Simple Queue Service (Amazon SQS) queue consumes messages and then an AWS Lambda function processes the messages. The Lambda function transforms the messages and makes an API call to a third-party service.
There has been an increase in application usage. The third-party API frequently returns an HTTP 429 Too Many Requests error message. The error message prevents a significant number of messages from being processed successfully.
How can the developer resolve this issue?
Antwort: A
Begründung:
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.
839. Frage
An ecommerce company manages its application's infrastructure by using AWS Elastic Beanstalk. A developer wants to deploy the new version of the application with the least possible application downtime. The developer also must minimize the application's rollback time if there are issues with the deployment. Which approach will meet these requirements?
Antwort: C
Begründung:
A blue/green deployment creates a separate environment for the new application version and shifts traffic only after validation succeeds. This approach results in near-zero downtime and enables an immediate rollback by redirecting traffic back to the original environment if issues occur, making it the safest and fastest option for minimizing both downtime and rollback time.
840. Frage
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