AIF-C01 Applications of Foundation Models Practice Test 5 — 15 Questions

Question 1

An e-commerce company uses a foundation model to generate personalized email subject lines. The marketing team notices that the subject lines sometimes contain product recommendations that are out of stock. Which action would best reduce the generation of out-of-stock recommendations without retraining the model?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Add a system prompt that explicitly instructs the model to only recommend products that are in stock.. Adding a system prompt that explicitly instructs the model to only recommend in-stock products directly constrains the model's output at inference time without requiring retraining. This leverages the model's instruction-following capability to filter its generated content based on the provided context, which is a lightweight and immediate solution.

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Implement a post-processing step to replace out-of-stock recommendations with in-stock alternatives.

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Fine-tune the model on a dataset of past successful subject lines that only include in-stock products.

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Use a retrieval-augmented generation (RAG) approach to retrieve a list of in-stock products and include it in the prompt.

Question 2

A company wants to build a chatbot that responds to customer queries using a foundation model. They need low latency and want to avoid managing infrastructure. Which AWS service should they use?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is a fully managed service that provides access to foundation models (FMs) from leading AI providers via a simple API, eliminating the need to manage underlying infrastructure. It is designed for building generative AI applications like chatbots with low latency, as it handles model hosting, scaling, and inference optimization automatically. This makes it the ideal choice for the company's requirement of low-latency responses without infrastructure management.

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Amazon EC2

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AWS Lambda

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Amazon SageMaker

Question 3

A developer is using Amazon Bedrock to generate text summaries. The output sometimes includes irrelevant information. What is the most effective prompt engineering technique to improve relevance?

Accepted Answer

Use few-shot examples with summaries. Few-shot examples provide the model with explicit patterns of desired output, directly guiding it to produce summaries that match the format and content of the examples. This technique is the most effective for improving relevance because it gives the model concrete reference points, reducing the likelihood of including irrelevant information.

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Add a negative prompt specifying what to avoid

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Increase max tokens

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Decrease temperature

Question 4

A financial services company uses a foundation model for document analysis. They need to ensure the model does not output sensitive customer information from its training data. What is the most effective mitigation?

Accepted Answer

Implement output filtering using an external service. Output filtering using an external service is the most effective mitigation because it acts as a post-processing layer that can detect and redact sensitive customer information (e.g., PII, account numbers) before the model's response is returned to the user. This approach does not rely on the model's internal training or input modifications, which can be bypassed or incomplete. It provides a robust, policy-driven control that can be updated independently of the model.

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Choose a model that has been fine-tuned on financial data

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Apply data masking before sending input

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Use a private endpoint

Question 5

An e-commerce company uses Amazon Bedrock to generate product descriptions. They notice the descriptions are too long and contain repetitive phrases. Which parameter adjustment can help?

Accepted Answer

Increase frequency penalty. Increasing the frequency penalty reduces the likelihood of the model repeating the same phrases or tokens, directly addressing the issue of repetitive language in generated product descriptions. This parameter penalizes tokens that have already appeared in the text, encouraging more diverse output and naturally shortening overly long descriptions by avoiding redundant loops.

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Increase temperature

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Increase top_p

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Decrease presence penalty

Question 6

A healthcare company needs to use a foundation model for analyzing medical records while complying with HIPAA. They plan to use Amazon Bedrock. What should they do to meet HIPAA requirements?

Accepted Answer

All of the above. HIPAA compliance in Amazon Bedrock requires a combination of controls: using a HIPAA-eligible model in a region where AWS offers a Business Associate Addendum (BAA), enabling access logging for auditability, and encrypting data at rest and in transit. None of the individual options alone satisfy all HIPAA requirements; only the full set of controls ensures compliance.

Answer

Use a model that is HIPAA eligible in a region that supports BAA

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Implement access logging for all API calls

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Encrypt data at rest and in transit

Question 7

A company uses a foundation model for real-time translation in a chat application. The latency is high. Which optimization would reduce latency the most?

Accepted Answer

Use model distillation to create a smaller model. Model distillation reduces the size of the foundation model by training a smaller 'student' model to mimic the behavior of a larger 'teacher' model. This directly decreases inference latency because the smaller model requires fewer computational resources (FLOPs) per forward pass, which is critical for real-time translation in a chat application where low latency is paramount.

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Increase batch size

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Use a larger model

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Use a CDN for model weights

Question 8

A data scientist is fine-tuning a foundation model on a custom dataset using Amazon SageMaker. After training, the model shows high accuracy on training data but poor on validation. Which action should be taken?

Accepted Answer

Reduce training epochs or add regularization. The model is overfitting, as indicated by high training accuracy but poor validation performance. Reducing training epochs or adding regularization (e.g., L1/L2 weight decay) directly addresses overfitting by limiting the model's capacity to memorize noise. In Amazon SageMaker, this can be implemented via hyperparameter tuning or by modifying the training script to include regularization terms.

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Add dropout layers

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Increase learning rate

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Use a different foundation model

Question 9

Which THREE of the following are factors to consider when selecting a foundation model for a text generation task?

Accepted Answer

Pricing per token. Pricing per token is a critical factor because foundation model APIs (e.g., Amazon Bedrock, OpenAI) charge based on the number of input and output tokens. For text generation tasks, token costs directly impact operational budgets, especially for high-volume or long-context applications. Selecting a model with lower per-token pricing can significantly reduce inference costs without sacrificing quality.

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Supported output modalities

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Availability of automatic scaling

Question 10

Which TWO of the following are valid methods to reduce the risk of foundation models generating harmful or biased content?

Accepted Answer

Use a content filter. Content filters act as a safety layer that intercepts and blocks harmful or biased outputs before they reach the user. These filters can be rule-based or use a separate classifier model trained to detect toxic, hateful, or biased language, reducing the risk of harmful content generation without altering the underlying model.

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Use a smaller model

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Fine-tune the model on a biased dataset

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Disable all logging

Question 11

Refer to the exhibit. A data scientist created this endpoint config for a foundation model in Amazon SageMaker. However, the endpoint fails to scale under load. What is the most likely reason?

Accepted Answer

Missing AutoScaling configuration. The endpoint fails to scale under load because the endpoint configuration shown lacks an AutoScaling policy. Without AutoScaling, SageMaker will not automatically adjust the number of instances based on traffic, so even if the initial instance count is 1, the endpoint cannot add more instances to handle increased load. AutoScaling must be explicitly configured via Application Auto Scaling to define scaling policies and target tracking metrics.

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Variant weight is 1.0

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Instance type is too small

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InitialInstanceCount is 1

Question 12

Refer to the exhibit. A developer deploys this CloudFormation stack but the agent fails to query the knowledge base. What is a likely cause?

Accepted Answer

The agent role does not have permissions to invoke the knowledge base. The agent role must have an IAM policy that grants the `bedrock:Retrieve` and `bedrock:RetrieveAndGenerate` permissions on the knowledge base. Without these permissions, the agent cannot invoke the knowledge base, even if the KnowledgeBaseId is correctly passed and the embedding model is available.

Answer

The KnowledgeBaseId is not passed correctly

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The embedding model is not available in the region

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The OpenSearch collection type should be SEARCH not VECTORSEARCH

Question 13

Refer to the exhibit. A developer runs this command but gets an error: 'An error occurred (AccessDeniedException) when calling the ListFoundationModels operation'. What is the most likely cause?

Accepted Answer

The IAM role does not have bedrock:ListFoundationModels permission. The error 'AccessDeniedException' when calling ListFoundationModels indicates that the IAM role or user executing the AWS CLI command lacks the required permission to list foundation models in Amazon Bedrock. The specific permission needed is bedrock:ListFoundationModels, which must be attached to the IAM identity via a policy. Without this permission, the API call is denied regardless of other factors like region or CLI version.

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The AWS CLI version is outdated

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The foundation model is not available in us-west-2

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The region us-west-2 does not support Bedrock

Question 14

A company uses Amazon Bedrock to generate product descriptions. They notice that the output sometimes contains incorrect information. What should they do to improve accuracy?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG).. Retrieval-Augmented Generation (RAG) enhances the accuracy of foundation model outputs by grounding the generation in authoritative, up-to-date external knowledge sources. Instead of relying solely on the model's parametric memory, RAG retrieves relevant documents or data from a vector database (e.g., Amazon OpenSearch Serverless) and injects them into the prompt context, reducing hallucinations and incorrect information in product descriptions.

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Increase the temperature parameter.

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Use a larger foundation model.

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Use AWS WAF to filter outputs.

Question 15

A healthcare company uses Amazon Bedrock to generate patient summaries. They need to ensure no protected health information (PHI) is leaked in the output. Which AWS service can they use to detect and mask PHI in text?

Accepted Answer

Amazon Comprehend Medical. Amazon Comprehend Medical is specifically designed to extract and identify protected health information (PHI) from unstructured medical text using natural language processing (NLP). It can detect entities such as patient names, dates, medical conditions, and medications, and provides APIs to mask or redact that PHI before output. This makes it the correct choice for the healthcare company's requirement to prevent PHI leakage in patient summaries generated by Amazon Bedrock.

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Amazon Macie

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AWS Glue

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Amazon Rekognition