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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Security | 26% | - Implement authentication and/or authorization for applications and AWS services
|
| Topic 2: Deployment | 24% | - Automate deployment testing
|
| Topic 3: Development with AWS Services | 32% | - Use data stores in application development
|
| Topic 4: Troubleshooting and Optimization | 18% | - Optimize applications by using AWS services and features
|
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NEW QUESTION # 591
A developer must use multi-factor authentication (MFA) to access data in an Amazon S3 bucket that is in another AWS account.
Which AWS Security Token Service (AWS STS) API operation should the developer use with the MFA information to meet this requirement?
Answer: A
NEW QUESTION # 592
An application is processing clickstream data using Amazon Kinesis. The clickstream data feed into Kinesis experiences periodic spikes. The PutRecords API call occasionally fails and the logs show that the failed call returns the response shown below:
Which techniques will help mitigate this exception? (Choose two.)
Answer: A,D
Explanation:
The response from the API call indicates that the ProvisionedThroughputExceededException exception has occurred. This exception means that the rate of incoming requests exceeds the throughput limit for one or more shards in a stream. To mitigate this exception, the developer can use one or more of the following techniques:
Implement retries with exponential backoff. This will introduce randomness in the retry intervals and avoid overwhelming the shards with retries.
Reduce the frequency and/or size of the requests. This will reduce the load on the shards and avoid throttling errors.
Increase the number of shards in the stream. This will increase the throughput capacity of the stream and accommodate higher request rates.
Use a PutRecord API instead of PutRecords. This will reduce the number of records per request and avoid exceeding the payload limit.
Reference:
[ProvisionedThroughputExceededException - Amazon Kinesis Data Streams Service API Reference]
[Best Practices for Handling Kinesis Data Streams Errors]
NEW QUESTION # 593
A company is planning to use AWS CodeDeploy to deploy an application to Amazon Elastic Container Service (Amazon ECS) During the deployment of a new version of the application, the company initially must expose only 10% of live traffic to the new version of the deployed application. Then, after 15 minutes elapse, the company must route all the remaining live traffic to the new version of the deployed application.
Which CodeDeploy predefined configuration will meet these requirements?
Answer: B
Explanation:
* CodeDeploy Predefined Configurations: CodeDeploy offers built-in deployment configurations for common scenarios.
* Canary Deployment: Canary deployments gradually shift traffic to a new version, ideal for controlled rollouts like this requirement.
* CodeDeployDefault.ECSCanary10Percent15Minutes: This configuration matches the company's requirements, shifting 10% of traffic initially and then completing the rollout after 15 minutes.
References:
* AWS CodeDeploy Deployment Configurations: https://docs.aws.amazon.com/codedeploy/latest
/userguide/deployment-configurations-create.html
NEW QUESTION # 594
A company has an application that uses an AWS Lambda function to process customer orders. The company notices that the application processes some orders more than once.
A developer needs to update the application to prevent duplicate processing.
Which solution will meet this requirement with the LEAST implementation effort?
Answer: C
Explanation:
Requirement Summary:
* Orders are being processed more than once
* Need to prevent duplicate processing
* Looking for least implementation effort
Key Concept:
* Lambda + Event-driven patterns can occasionally result in duplicate invocations (at-least-once delivery model)
* You need idempotency (i.e., prevent repeated processing of same event) Evaluate Options:
A). Use DynamoDB for de-duplication
* Simple and widely used approach
* Store a unique orderId as the primary key
* Before processing, check if order exists
* If yes # skip
* If no # process and store the ID
* Minimal code changes required
B). ECS + Step Functions
* Overkill for basic de-duplication
* Adds significant complexity
C). Retry logic with fixed delay
* Doesn't prevent duplication - makes it worse
* Retrying might trigger the same message again
D). Athena to identify duplicates
* Reactive solution, not preventative
* Not suitable for real-time event de-duplication
* Lambda idempotency: https://docs.aws.amazon.com/lambda/latest/dg/invocation-retries.html
* Using DynamoDB for idempotent design: https://aws.amazon.com/blogs/compute/how-to-design- idempotent-APIs-on-aws/
NEW QUESTION # 595
For a deployment using AWS Code Deploy, what is the run order of the hooks for in-place deployments?
Answer: C
Explanation:
Explanation
For in-place deployments, AWS CodeDeploy uses a set of predefined hooks that run in a specific order during each deployment lifecycle event. The hooks are ApplicationStop, BeforeInstall, AfterInstall, ApplicationStart, and ValidateService. The run order of the hooks for in-place deployments is as follows:
* ApplicationStop: This hook runs first on all instances and stops the current application that is running on the instances.
* BeforeInstall: This hook runs after ApplicationStop on all instances and performs any tasks required before installing the new application revision.
* AfterInstall: This hook runs after BeforeInstall on all instances and performs any tasks required after
* installing the new application revision.
* ApplicationStart: This hook runs after AfterInstall on all instances and starts the new application that has been installed on the instances.
* ValidateService: This hook runs last on all instances and verifies that the new application is running properly on the instances.
NEW QUESTION # 596
......
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