Amazon Exam AIF-C01 Topic 4 Question 2 Discussion

Actual exam question for Amazon's AIF-C01 exam

Question #: 2
Topic #: 4

A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality.

Which action must the company take to use the custom model through Amazon Bedrock?

APurchase Provisioned Throughput for the custom model.

BDeploy the custom model in an Amazon SageMaker endpoint for real-time inference.

CRegister the model with the Amazon SageMaker Model Registry.

DGrant access to the custom model in Amazon Bedrock.

Show Suggested Answer

Suggested Answer: B

by Blair at Sep 10, 2024, 09:00 PM

Limited Time Offer

25%

Off

Get Premium AIF-C01 Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel

Han

8 months ago

Wait, I thought Amazon Bedrock was a new superhero team. I'm so confused right now. Anyway, I'm guessing C is the right answer. Register that model, baby!

upvoted 0 times

...

Alida

8 months ago

Haha, did they really think they could just buy 'Provisioned Throughput' and call it a day? That's like trying to power a rocket with a hamster on a wheel. Option B all the way!

upvoted 0 times

Devorah

7 months ago

Definitely, using Amazon SageMaker endpoint for real-time inference is the way to go.

upvoted 0 times

...

Jacquelyne

7 months ago

I agree, trying to power a rocket with a hamster on a wheel won't get you far.

upvoted 0 times

...

Nathan

7 months ago

Option B) Deploy the custom model in an Amazon SageMaker endpoint for real-time inference.

upvoted 0 times

...

Shawnda

8 months ago

I'm going with D. Granting access to the custom model in Amazon Bedrock is the crucial step, right? I mean, that's where the magic happens.

upvoted 0 times

Micah

7 months ago

That's correct. Registering the model with the Amazon SageMaker Model Registry is also important for managing the custom model.

upvoted 0 times

...

Elroy

7 months ago

I think we also need to deploy the custom model in an Amazon SageMaker endpoint for real-time inference.

upvoted 0 times

...

Gennie

8 months ago

Yes, you're right. Granting access to the custom model in Amazon Bedrock is essential for using it.

upvoted 0 times

...

Myra

8 months ago

Definitely option C. Gotta get that custom model registered with the SageMaker Model Registry, that's the key step here.

upvoted 0 times

Maryrose

7 months ago

Exactly, it's a crucial step in the process of using the custom model effectively within the Amazon Bedrock environment.

upvoted 0 times

...

Chun

7 months ago

Once it's registered, the company can easily access and manage the custom model for document summarization.

upvoted 0 times

...

Jacklyn

8 months ago

Agreed, that's the first step to take. It will make it easier to use the custom model through Amazon Bedrock.

upvoted 0 times

...

Roosevelt

8 months ago

Option C is the way to go. Registering the custom model with the SageMaker Model Registry is essential.

upvoted 0 times

...

Rosio

8 months ago

I think granting access to the custom model in Amazon Bedrock is also important for integration with the company's existing systems.

upvoted 0 times

...

Rosio

8 months ago

That's a good point, Carli. Maybe both deploying in an endpoint and registering the model are needed.

upvoted 0 times

...

Carli

9 months ago

But wouldn't registering the model with the Amazon SageMaker Model Registry also be necessary for tracking and versioning?

upvoted 0 times

...

Artie

9 months ago

Hmm, I'm not sure. Registering the model with the Amazon SageMaker Model Registry seems like it could be the right answer, but I'm not 100% certain.

upvoted 0 times

Kanisha

7 months ago

C: Yeah, that way they can improve the summarization quality for their internal use case.

upvoted 0 times

...

Dawne

7 months ago

B: That makes sense. It would allow them to use the custom model through Amazon Bedrock.

upvoted 0 times

...

Otis

8 months ago

A: I think the company should deploy the custom model in an Amazon SageMaker endpoint for real-time inference.

upvoted 0 times

...

Rosio

9 months ago

I agree with Rosio, deploying the custom model in an endpoint makes sense for real-time use.

upvoted 0 times

...

Floyd

9 months ago

I think option B is the way to go. Deploying the custom model in an Amazon SageMaker endpoint for real-time inference seems like the logical choice here.

upvoted 0 times