How Do You Use a Customized Model in Amazon Bedrock for Summarization?
Describe AWS infrastructure and technologies for building generative AI applications.
Describe the training and fine-tuning process for foundation models.
Answer
Correct answer: A — Purchase Provisioned Throughput for the customized Bedrock model, which is required before the model can be invoked for summarization.
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?
Community Insight
The test is whether you know the operational prerequisite for invoking a Bedrock custom model — purchasing Provisioned Throughput — versus confusing it with the one-time base-model access grant or with SageMaker hosting.
Customized (fine-tuned) models in Amazon Bedrock cannot be invoked until the account purchases Provisioned Throughput for that model. This page confirms why purchasing Provisioned Throughput is the required action and why SageMaker endpoints, the Model Registry, and model access grants do not apply.
Choosing "Grant access to the custom model in Amazon Bedrock" because it sounds like the familiar base-model access step; model access only unlocks foundation models, it does not make a fine-tuned custom model invokable.
Community Discussion (54 comments)
wem
👍 1
Selected: A
Purchase Provisioned Throughput for the custom model. Customized models in Bedrock (fine-tuned or continued pre-training) cannot be invoked on-demand. You must purchase Provisioned Throughput to create a dedicated deployment before running inference against the custom model.
9727539
👍 1
Selected: D
To use a customised model (fine-tuned/custom model) through Amazon Bedrock, the company must ensure the model is available and accessible within Amazon Bedrock by granting the appropriate access/permissions to invoke it. Amazon Bedrock provides access to foundation models and customised models through its APIs once access is enabled.
Kaushal1987
👍 1
Selected: D
To use a custom model within Amazon Bedrock, the model must be made accessible through Bedrock itself. Bedrock supports custom models (fine-tuned models) by allowing you to: Register and access them within the Bedrock environment Use them via the same API as base models So the key step is to grant access to the custom model in Amazon Bedrock so it can be invoked for inference.
Aamee
👍 1
Selected: D
I feel like it should be granting access after going through with all the comments/explaination posted here.
Amac1979
👍 2
Selected: A
In Amazon Bedrock, custom models (created via fine-tuning or continued pre-training) cannot be invoked on-demand. You must purchase Provisioned Throughput to run inference on a custom model. This is a requirement — without it, the custom model exists but cannot serve requests
AWSCer1937
👍 1
Selected: D
A should be optional Bedrock manages models and access centrally. Even if you’ve trained or customized a model, you must enable access to it in Bedrock before you can invoke it for tasks like summarization.
aramisrocha
👍 1
Selected: D
Answer is D
c603e50
👍 1
Selected: D
When you customize a base model in Amazon Bedrock, the model already exists within Bedrock. To invoke and use that custom model for inference, you must grant access to it in Amazon Bedrock (via the console or API).
Sesh_14
👍 3
Selected: D
Explanation: When a company fine-tunes or customizes a model and wants to use it through Amazon Bedrock, the model must be explicitly granted access in Amazon Bedrock before it can be invoked by applications. This step enables: Use of the custom model through Bedrock APIs Proper access control via IAM roles and permissions Why the other options are incorrect: A. Purchase Provisioned Throughput for the custom model ❌ Provisioned Throughput is optional and used for predictable latency or higher capacity, not for enabling access.
Suchetana
👍 1
Selected: B
To use a custom-trained model with Amazon Bedrock for tasks like summarization, the company must Deploy the custom model to an Amazon SageMaker endpoint, making it available for real-time inference, and then grant Amazon Bedrock access to that endpoint, allowing Bedrock to invoke it as a custom model for processing.
Sesh_14
👍 1
Selected: D
When using Amazon Bedrock, even after a company trains or customizes a model (for example, via fine-tuning), the model cannot be used by applications until access is explicitly granted in Amazon Bedrock. Model access control is a required step to: Enable usage of the custom or fine-tuned model Control which AWS accounts, IAM roles, or users can invoke the model through Bedrock APIs
tdlAws
👍 1
Selected: D
D for sure!!
samplunk
👍 1
Selected: D
Amazon Bedrock allows users to access various foundation models for different tasks (e.g., summarization, text generation, etc.). If the company has trained a custom model (using either their own data or a modified base model), they need to ensure it is registered and accessible within Amazon Bedrock. By granting access to the custom model, the company can make it available for use via the Amazon Bedrock platform. urchase Provisioned Throughput for the custom model: This is not necessary for Bedrock as the service automatically manages throughput based on demand, and provisioning is generally associated with services like Amazon SageMaker when deploying models.
steli0
👍 1
Selected: A
I think that this question is before the on-demand reference was announces (Jul 2025) https://aws.amazon.com/blogs/machine-learning/implementing-on-demand-deployment-with-customized-amazon-nova-models-on-amazon-bedrock/ But in any case IAM is irrelevant so D is out of the equation.
7cd3e56
👍 2
Selected: D
It is D, read the guide, you do not need to buy provisioned throughput, the guide says for custom models you can use either provisioned or on demand
2f5c7cd
👍 2
Selected: D
https://docs.aws.amazon.com/bedrock/latest/userguide/custom-model-inference-prerequisites.html
numark
👍 2
Selected: D
Key words in this question: "To Use" You can not use it with out permissions. If the key words were "to run" it would be Provision-A. So the answer is D. We were all network admins at one point in time, permissions are always first. Its customer. IE not already in bedrock, so you need access first.
65703c1
👍 1
Selected: A
A is the correct answer
rmnveeveik
👍 2
Selected: D
question is about using the custom model not training. so it has to be D
Rcosmos
👍 2
Selected: D
A melhor opção seria D. Conceda acesso ao modelo personalizado no Amazon Bedrock. O Amazon Bedrock permite que empresas utilizem modelos básicos e personalizem modelos para casos de uso específicos. Para que a empresa possa usar o modelo treinado, ela deve garantir que o modelo personalizado esteja acessível dentro do Bedrock. Dessa forma, ele pode ser utilizado para inferências e integração com aplicações.
Hayaat
👍 3
Selected: A
Correct Answer: A. Purchase Provisioned Throughput for the custom model To utilize a custom model in Amazon Bedrock, you must first purchase Provisioned Throughput for it. This step is essential to enable inference capabilities with your customized model. According to the AWS documentation: "Before you can use a customized model, you need to purchase Provisioned Throughput for it." AWS Documentation +1 AWS Documentation +1 Once Provisioned Throughput is purchased, you can perform inference by invoking the provisioned model using its ARN.
7cd3e56
👍 1
Apparently you didn't bother reading the whole guide, it literally gives you step by step to deploy with provisioned throughput AND on demand. It is D
sudarshanbisht
👍 2
Selected: D
When you train a custom model using Amazon Bedrock, especially via fine-tuning a base foundation model (e.g., Anthropic Claude, AI21, etc.), the custom model is managed within Bedrock itself. To use it in your applications via Bedrock APIs, you must: Grant access to the fine-tuned (custom) model within Bedrock. This allows your applications to invoke it using Bedrock's InvokeModel API.
chdaphne
👍 1
Selected: D
Amazon Bedrock allows companies to import and use their customized models alongside base models through its Custom Model Import feature. By registering the custom model within Amazon Bedrock, it can be accessed seamlessly via Bedrock’s unified API without requiring deployment in SageMaker or other infrastructure management.
SP888
👍 1
Selected: D
Yes, D for sure.
SP888
👍 3
Selected: D
Correct Answer: ✅ D. Grant access to the custom model in Amazon Bedrock. Explanation: Since the company has trained a custom model to enhance summarization and wants to use it through Amazon Bedrock, they must grant access to the custom model within Bedrock so it can be used for inference. • Amazon Bedrock allows fine-tuning of base models → After fine-tuning, the custom model must be registered and access must be granted. • Ensures secure and controlled model usage → This step enables API access for the custom summarization model. • Bedrock manages model deployment internally → The model does not need an Amazon SageMaker endpoint for use within Bedrock.
Jessiii
👍 4
Selected: D
D. Grant access to the custom model in Amazon Bedrock: When using Amazon Bedrock, you can fine-tune models or create custom versions of base models. To use your custom model, you would need to grant access to it within the Amazon Bedrock environment, enabling the model to be accessed and invoked by your application for summarization tasks.
85b5b55
👍 2
Selected: A
Provisioned Throughput helps to improve the quality.
Ginopress
👍 2
Selected: A
Accordingly to https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-use.html?form=MG0AV3
kopper2019
👍 4
Selected: D
A particularly insightful comment from user "may2021_r" clarifies this: "Bottom Line: Required to use a custom model? Give Bedrock permissions and register your model so it can retrieve your artifacts. Optional but recommended at scale? Purchase Provisioned Throughput to guarantee a certain level of concurrency and avoid throttling." The key distinction is: Granting access is the fundamental requirement to use the model at all Provisioned Throughput is about performance and scaling, not basic access
Moon
👍 2
Selected: D
D: Grant access to the custom model in Amazon Bedrock. Explanation: When a company trains a custom model to improve the performance of a base model provided by Amazon Bedrock, they need to ensure the custom model is accessible through the Amazon Bedrock service. Granting access to the custom model ensures it can be integrated and used through Bedrock's APIs and workflows for inference tasks like document summarization.
may2021_r
👍 1
Selected: D
The correct answer is D. Access must be granted in Bedrock to use custom models.
may2021_r
👍 3
Bottom Line Required to use a custom model? Give Bedrock permissions and register your model so it can retrieve your artifacts. Optional but recommended at scale? Purchase Provisioned Throughput to guarantee a certain level of concurrency and avoid throttling. So if the question specifically asks which action you must take to use the custom model, the correct answer is still about granting Bedrock access—that is the non-negotiable requirement. Purchasing Provisioned Throughput is a subsequent or optional step, depending on your performance needs.
AKG85
👍 1
Selected: D
To use the custom model with Amazon Bedrock, you need to grant access to the model first.
RightAnswers
👍 1
Selected: D
When a company has trained a custom model to improve the functionality of an Amazon Bedrock base model, they need to explicitly grant access to that custom model within the Bedrock environment. This allows Bedrock to utilize the custom model's capabilities for the desired use case. Why option A is incorrect: While purchasing provisioned throughput can improve the performance and responsiveness of a model in SageMaker, it's not necessary to use a custom model with Bedrock. Bedrock itself handles the infrastructure and resource allocation. Access granting is the key step for integration.
grzeev
👍 2
Selected: D
The correct answer is D: Grant access to the custom model in Amazon Bedrock. Why not B (Purchase Provisioned Throughput): 1. Provisioned Throughput is about performance and capacity, not access 2. Granting access is a mandatory first step for using custom models in Bedrock 3. Without proper access permissions, the model cannot be used at all, even with Provisioned Throughput Granting access (C) is essential because it: - Enables model visibility in Bedrock - Controls who can use the custom model - Is a prerequisite for any model operations
grzeev
👍 1
Sorry: Granting access (C) is essential because it: - Enables model visibility in Bedrock - Controls who can use the custom model - Is a prerequisite for any model operations
6c8c706
👍 6
Selected: A
https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-use.html
Contactfornitish
👍 1
Selected: B
A. Purchase Provisioned Throughput for the custom model Provisioned Throughput is not relevant to Amazon Bedrock or custom models. It is generally associated with services like DynamoDB for performance scaling. C. Register the model with the Amazon SageMaker Model Registry While the Model Registry helps manage and track model versions, registering the model alone does not make it usable for inference. The model must still be deployed to a SageMaker endpoint. D. Grant access to the custom model in Amazon Bedrock Amazon Bedrock only provides access to foundation models hosted and managed by AWS. Custom models trained by the company need to be deployed separately via Amazon SageMaker.
CTao
👍 9
Selected: A
A To customize model you must purchase Provisioned Throughput.
7cd3e56
👍 3
No you do not, read the guide, you can deploy custom models both ways
leo321
👍 3
A - is the right answer, as you NEED to Purchase Provisioned Throughput for customized model: https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-use.html D - is NOT (less) correct as IAM is OPTIONAL: https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-prereq.html
RY66
👍 1
The correct answer is D. Grant access to the custom model in Amazon Bedrock.
LR2023
👍 22
Selected: A
Initially I was going with D but after reading this article sticking with A https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-use.html?form=MG0AV3
7cd3e56
👍 2
Apparently you didn't bother reading the whole guide since it gives you two options for deploying custom models, the second choice is on demand. So it is D
primate24
👍 1
The article you've quoted says provisioned throughput is NOT required for Nova models. Answer is D
fed6485
👍 5
Selected: A
B, and C, CANNOT be as the question is clear: "..using an Amazon Bedrock.. through Amazon BedRock" , in short SageMaker is out of the picture in this case. we are talking about a customize Bedrock Model.. so .. A is the only possible answer, we are not deploying a custom model in bedrock, we are using a bedrock customised model.. and in that case you have to pay the premium... as per this link: https://docs.aws.amazon.com/bedrock/latest/userguide/prov-throughput.html ...If you customized a model, you must purchase Provisioned Throughput to be able to use it
Blair77
👍 1
While this might be relevant for scaling usage, it's not the immediate step needed to use the custom model in Bedrock.
Blair77
👍 2
Selected: D
The question specifically mentions using the custom model "through Amazon Bedrock," which implies that the model should be integrated with Bedrock's infrastructure.
AlwaysHungry
👍 1
Has to be B
leyunjohn
👍 2
Selected: D
I agree the answer is D
jove
👍 1
Indeed, you need to "import" the custom model first : https://aws.amazon.com/bedrock/custom-model-import/
Jack78
👍 4
A. Purchase Provisioned Throughput for the custom model. https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-use.html
jove
👍 3
Selected: D
To use the custom model through Amazon Bedrock, the company needs to grant access to that model within the Bedrock environment, ensuring that the model can be utilized for tasks like document summarization.
Comments & Corrections
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Expert Analysis
Why the Answer Is Correct
When you create a custom model in Amazon Bedrock (through fine-tuning or continued pre-training), it is stored as a private custom model that has no on-demand inference. AWS documentation is explicit that "before you can use a customized model, you need to purchase Provisioned Throughput for it." Provisioned Throughput allocates dedicated model units to that custom model, which is what makes InvokeModel and Converse calls succeed against it. Summarization of internal documents is a sustained workload, so dedicated throughput also gives predictable latency and avoids throttling. As CTao states in the comments, "To customize model you must purchase Provisioned Throughput." LR2023 reached the same conclusion, noting they initially leaned toward model access but changed to A after reading the Bedrock custom-model usage page.Why the Other Options Are Wrong
Option B (a SageMaker real-time endpoint) is outside the scenario: the question requires the model be used through Amazon Bedrock, and Bedrock serves custom models from its own managed infrastructure, not from a SageMaker endpoint. Option C (SageMaker Model Registry) is a catalog for versioning and approving models, not a step that enables Bedrock inference; as fed6485 puts it, the wording keeps SageMaker "out of the picture in this case." Option D (granting access to the custom model) confuses two different mechanisms — the model-access grant applies to base foundation models from providers, whereas a custom model you created is already in your account and simply needs Provisioned Throughput attached. kopper2019 quotes another learner's reasoning that granting permissions is the required setup step and throughput is merely "optional but recommended at scale" — that ordering is wrong for Bedrock custom models, where throughput is the hard prerequisite before any invocation.Community Comment Notes
Votes and comments split between A and D, but the A camp cites the authoritative Bedrock page on using customized models, which is decisive. Sesh_14 and SP888 argue for D on the intuition that a custom model must be "registered" and granted access inside Bedrock; that conflates model access for third-party foundation models with your own custom model artifacts. Jessiii's D rationale describes fine-tuning generally but never addresses the documented requirement to buy throughput before invocation. The most reliable comments are the ones that anchor to the documentation rather than to the sound of the option.Official Reference
Exam Strategy
When a question says the model must be used through Bedrock, eliminate any SageMaker-hosted options immediately — they answer a different architecture question. Then recall the Bedrock rule: base model access is a grant, custom model usage is a Provisioned Throughput purchase.
Frequently Asked Questions
Why doesn't granting access to the custom model in Bedrock make it usable?
Model access grants unlock third-party base foundation models for an account. A custom model you fine-tuned already exists in your account, but it still needs Provisioned Throughput before it can serve inference.
Can I invoke a Bedrock custom model on-demand instead of buying Provisioned Throughput?
No. Bedrock custom models have no on-demand option; you must purchase Provisioned Throughput for that model, which provisions dedicated model units for your workload.
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