AIF-C01 Applications of Foundation Models Practice Test 2 — 15 Questions

Question 1

A healthcare organization is using Amazon Bedrock to analyze medical images and generate radiology reports. They need to comply with HIPAA regulations and ensure patient data is not used for model training. Which configuration should they use?

Accepted Answer

Use a provisioned throughput model with data isolation. Option B is correct because Provisioned Throughput with data isolation in Amazon Bedrock ensures that the customer's inference data (including patient medical images and reports) is not used for any model training or service improvement, and it provides a dedicated, isolated environment that meets HIPAA compliance requirements. This configuration guarantees that patient data remains within the customer's AWS account and is not shared with other customers or used to improve the base model.

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Fine-tune the model using a custom dataset and deploy as a custom model

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Use the on-demand model through Amazon Bedrock

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Use a third-party model hosted outside of AWS

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 developer is using Amazon Bedrock to generate code snippets. The model often produces insecure code. Which prompt engineering technique is MOST effective to improve security?

Accepted Answer

Include specific instructions to avoid common security vulnerabilities. Option D is correct because directly instructing the model to avoid specific security vulnerabilities (e.g., SQL injection, buffer overflows) is the most explicit and effective way to constrain the output. Amazon Bedrock models respond well to clear, imperative instructions in the system prompt or user message, making this a direct application of prompt engineering for safety. Chain-of-thought or few-shot examples may improve reasoning or style but do not guarantee the model will avoid insecure patterns unless explicitly told to do so.

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Use chain-of-thought prompting to step through the code

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Provide few-shot examples of secure code

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Set max_tokens to a low value to limit output

Question 4

A company is using Amazon Bedrock to build a text-to-SQL application. They want to ensure that the generated SQL queries are valid and safe. Which approach is BEST?

Accepted Answer

Configure a guardrail to filter and validate the generated SQL. Amazon Bedrock guardrails provide a native, configurable mechanism to filter and validate model outputs, including SQL queries, against defined policies such as regex patterns, denied topics, and content filters. This approach directly addresses both validity and safety without requiring additional model training or external validation services, making it the most integrated and efficient solution for ensuring generated SQL is syntactically correct and free of harmful operations like DROP or DELETE.

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Fine-tune the model on a dataset of valid SQL queries

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Use a separate model to validate the SQL after generation

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Limit the max_tokens to 50 to reduce complexity

Question 5

A company is using Amazon Bedrock to generate product descriptions. They notice that the model sometimes produces descriptions that contain factual errors about the products. Which TWO actions should they take to improve factual accuracy?

Accepted Answer

Implement Retrieval Augmented Generation (RAG) with a product knowledge base. Option A is correct because Retrieval Augmented Generation (RAG) grounds the model's output in a curated product knowledge base, allowing it to retrieve and cite authoritative facts during generation. This directly reduces hallucinations by ensuring the model references verified data rather than relying solely on its parametric memory.

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Reduce the temperature parameter to 0.1

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Increase the max_tokens to allow longer descriptions

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Use human reviewers to correct errors after generation

Question 6

A company wants to classify customer emails into categories (e.g., complaint, inquiry, feedback) using a foundation model. Which approach is MOST efficient?

Accepted Answer

Use Amazon Comprehend for custom classification. Amazon Comprehend provides a managed custom classification API that is purpose-built for text classification tasks like categorizing emails. It requires only a small set of labeled data to train a custom classifier, eliminating the need to manage infrastructure or fine-tune large models, making it the most efficient choice for this specific use case.

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Train a custom model using Amazon SageMaker

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Fine-tune a large language model on labeled emails

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Use Amazon Lex with a classifier intent

Question 7

A company is using Amazon Bedrock to generate marketing copy. They want to evaluate the quality of the generated text. Which metric is MOST suitable for assessing the relevance and coherence of the content?

Accepted Answer

ROUGE-N. ROUGE-N (Recall-Oriented Understudy for Gisting Evaluation) measures the overlap of n-grams between generated text and reference text, making it suitable for assessing relevance and coherence in content generation tasks like marketing copy. It evaluates how well the generated text captures key phrases and maintains logical flow, which aligns with the need to assess content quality beyond simple factual accuracy.

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Accuracy

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Perplexity

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BLEU score

Question 8

A company is developing a chatbot using Amazon Bedrock and wants to ensure the model's responses do not include toxic or biased language. The company has a labeled dataset of undesirable responses. Which approach should be used to fine-tune the foundation model to reduce harmful outputs?

Accepted Answer

Use reinforcement learning from human feedback (RLHF) with a reward model trained on human preferences.. Reinforcement learning from human feedback (RLHF) is the correct approach because it directly optimizes the model to avoid toxic or biased outputs by training a reward model on human-labeled preferences. The reward model scores the model's responses, and the foundation model is fine-tuned via reinforcement learning to maximize these scores, effectively reducing harmful language. This method is specifically designed to align model behavior with nuanced human values, such as avoiding toxicity, which supervised fine-tuning alone cannot guarantee.

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Perform supervised fine-tuning on a curated dataset of safe responses.

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Use prompt engineering to instruct the model to avoid toxic language.

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Implement adversarial validation by testing against toxic inputs.

Question 9

A developer is calling the Amazon Bedrock InvokeModel API to generate text with the AI21 Labs Jurassic-2 Mid model. The API call includes a maxTokens parameter, but the request fails with the error shown in the exhibit. What is the most likely cause of this error?

Accepted Answer

The parameter name is incorrect; the model expects 'maxTokens' with a capital T.. The AI21 Labs Jurassic-2 Mid model expects the parameter name 'maxTokens' with a capital 'T' (camelCase). The error occurs because the API request used a different casing (e.g., 'maxtokens' or 'max_tokens'), which the model's schema does not recognize. Amazon Bedrock's InvokeModel API passes parameters directly to the model, so parameter names must match the model's exact specification.

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The API request is missing a required parameter such as 'prompt'.

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The AWS region does not support the AI21 Labs model.

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The value of 'maxTokens' exceeds the model's maximum limit.

Question 10

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 and few-shot examples in the prompt template (Option D) provides explicit guidance to the model at inference time, shaping the tone without any model updates. Option A (retrieval-augmented generation) is for incorporating external knowledge, not tone. Option B (prompt chaining) adds complexity and may not directly enforce tone. Option C (fine-tuning) requires modifying model weights, which is not desired.

Answer

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 11

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?

Accepted Answer

Add a system prompt that explicitly instructs the model to only recommend products that are in stock.. Option C is correct because 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 12

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 13

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 14

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 15

A company wants to use a foundation model to classify customer feedback into positive, neutral, negative. They have a small labeled dataset. What approach yields best results?

Accepted Answer

Fine-tune a foundation model on their dataset. Option C is correct because fine-tuning a foundation model on a small labeled dataset allows the model to adapt its pre-trained knowledge specifically to the company's sentiment classification task, achieving higher accuracy than zero-shot or generic API approaches. Fine-tuning adjusts the model's weights using the labeled examples, making it sensitive to domain-specific language and nuance in customer feedback, which is critical for a three-class sentiment task.

Answer

Use a pre-built sentiment analysis API

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Fine-tune a foundation model on their dataset

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Use zero-shot classification