Welcome to ExamTopics
ExamTopics Logo
- Expert Verified, Online, Free.
Mail Us[email protected]
Menu
Amazon Discussions
exam questions

Exam AWS Certified Solutions Architect - Associate SAA-C03 All Questions

View all questions & answers for the AWS Certified Solutions Architect - Associate SAA-C03 exam
Go to Exam

Exam AWS Certified Solutions Architect - Associate SAA-C03 topic 1 question 954 discussion

Exam question from Amazon's AWS Certified Solutions Architect - Associate SAA-C03
Question #: 954
Topic #: 1
[All AWS Certified Solutions Architect - Associate SAA-C03 Questions]

A company is developing machine learning (ML) models on AWS. The company is developing the ML models as independent microservices. The microservices fetch approximately 1 GB of model data from Amazon S3 at startup and load the data into memory. Users access the ML models through an asynchronous API. Users can send a request or a batch of requests.

The company provides the ML models to hundreds of users. The usage patterns for the models are irregular. Some models are not used for days or weeks. Other models receive batches of thousands of requests at a time.

Which solution will meet these requirements?

  • A. Direct the requests from the API to a Network Load Balancer (NLB). Deploy the ML models as AWS Lambda functions that the NLB will invoke. Use auto scaling to scale the Lambda functions based on the traffic that the NLB receives.
  • B. Direct the requests from the API to an Application Load Balancer (ALB). Deploy the ML models as Amazon Elastic Container Service (Amazon ECS) services that the ALB will invoke. Use auto scaling to scale the ECS cluster instances based on the traffic that the ALB receives.
  • C. Direct the requests from the API into an Amazon Simple Queue Service (Amazon SQS) queue. Deploy the ML models as AWS Lambda functions that SQS events will invoke. Use auto scaling to increase the number of vCPUs for the Lambda functions based on the size of the SQS queue.
  • D. Direct the requests from the API into an Amazon Simple Queue Service (Amazon SQS) queue. Deploy the ML models as Amazon Elastic Container Service (Amazon ECS) services that read from the queue. Use auto scaling for Amazon ECS to scale both the cluster capacity and number of the services based on the size of the SQS queue.
Show Suggested Answer Hide Answer
Suggested Answer: D 🗳️
by [deleted] at Aug. 18, 2024, 2:49 p.m.
Disclaimers:
  • - ExamTopics website is not related to, affiliated with, endorsed or authorized by Amazon.
  • - Trademarks, certification & product names are used for reference only and belong to Amazon.

Comments

Chosen Answer:
This is a voting comment (?) , you can switch to a simple comment.
Switch to a voting comment New
Submit Cancel
kbgsgsgs
2 weeks ago
Selected Answer: B
Why should I use SQS in option D? Wouldn't ALB be enough?
upvoted 1 times
...
[Removed]
2 months ago
Selected Answer: D
D is the answer: SQS Queue: Directing API requests to SQS decouples the API from ML processing, efficiently handles high traffic, and ensures reliable request processing without overloading the ML models. Amazon ECS Services: Running ML models on ECS provides effective management of containerized applications, ideal for handling ML workloads. Auto Scaling: ECS auto scales based on SQS queue size, adjusting container and cluster capacity to match demand, ensuring efficient handling of varying workloads.
upvoted 3 times
...

Get IT Certification

Unlock free, top-quality video courses on ExamTopics with a simple registration. Elevate your learning journey with our expertly curated content. Register now to access a diverse range of educational resources designed for your success. Start learning today with ExamTopics!

Start Learning for free
Community vote distribution
A (35%)
C (25%)
B (20%)
Other
Most Voted
A voting comment increases the vote count for the chosen answer by one.

Upvoting a comment with a selected answer will also increase the vote count towards that answer by one. So if you see a comment that you already agree with, you can upvote it instead of posting a new comment.

SaveCancel