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

Question 1

A company is using a foundation model on Amazon Bedrock to generate customer support responses. They notice that the model sometimes produces harmful or offensive content. Which approach is MOST effective to mitigate this issue?

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

Accepted Answer

Configure Amazon Bedrock Guardrails with content filters. Amazon Bedrock Guardrails provides configurable content filters that can block harmful, offensive, or inappropriate content in both user inputs and model outputs. This is the most effective approach because it operates at the inference layer, applying safety policies consistently across all requests without requiring model retraining or manual review. Prompt engineering alone is unreliable, and fine-tuning may not generalize to all harmful content patterns.

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Use prompt engineering to instruct the model to avoid harmful content

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Enable model invocation logging to review and block responses

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Fine-tune the model on a curated dataset of safe responses

Question 2

A company wants to use a foundation model to automatically summarize lengthy documents. Which capability of foundation models is being utilized?

Accepted Answer

Text generation. Summarization is a text generation task where the model produces a concise version of the original content. Foundation models (e.g., GPT, Claude) are pre-trained on vast corpora and can generate coherent summaries by predicting the next tokens conditioned on the input document. This directly utilizes the text generation capability, not classification or translation.

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Sentiment analysis

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Text classification

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Machine translation

Question 3

A company uses Amazon Bedrock to generate product descriptions. They want to ensure the outputs consistently follow a specific brand tone (professional yet friendly). They have a small set of example descriptions (few-shot examples) but do not want to fine-tune the model. Which strategy best achieves consistent tone without modifying the base model?

Accepted Answer

Use a system prompt that defines the brand tone and include few-shot examples in the prompt.. Using a system prompt to define the brand tone and including few-shot examples in the prompt is the best strategy because it guides the model's behavior without modifying its weights. This leverages in-context learning, which is effective for style adaptation. Fine-tuning is unnecessary and more resource-intensive.

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Fine-tune the model on a dataset of product descriptions that exemplify the desired tone.

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Implement prompt chaining by breaking the task into multiple steps, each with its own prompt.

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Use retrieval-augmented generation (RAG) to pull example descriptions from a database and prepend them to the prompt.

Question 4

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 5

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 6

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 7

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 8

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.

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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 9

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 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. Option B (Use a content filter) is correct because content filters act as a post-processing guardrail that screens both prompts and model completions for harmful, violent, hateful, or otherwise policy-violating content, blocking or redacting it before it reaches users. Option C (Apply prompt engineering to guide output) is correct because carefully crafted system prompts, few-shot examples, and instructions can steer the foundation model toward safe, neutral, and on-topic responses, reducing the likelihood of biased or harmful generations. Option A (Use a smaller model) is not a valid mitigation because model size does not determine safety or bias; smaller models can still produce harmful or biased content. Option D (Fine-tune the model on a biased dataset) would actually increase the risk by reinforcing biased patterns in the model's outputs. Option E (Disable all logging) does not reduce harmful content generation and instead removes the audit trail needed to detect, monitor, and remediate such issues.

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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 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 13

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 14

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

Question 15

A company uses Amazon Bedrock to build a conversational AI. They want to enforce role-based access to the model. Which AWS service should they use?

Accepted Answer

AWS Identity and Access Management (IAM). AWS Identity and Access Management (IAM) is the correct service because it enables fine-grained, role-based access control (RBAC) to Amazon Bedrock models. You can define IAM policies that specify which principals (users, groups, or roles) are allowed to invoke specific foundation models, ensuring that only authorized roles can interact with the conversational AI.

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AWS Config

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AWS CloudTrail

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AWS Organizations