AIF-C01 Applications of Foundation Models Practice Test 4 — 15 Questions

Question 1

A data scientist is fine-tuning a foundation model on Amazon Bedrock for a custom summarization task. Which THREE practices should they follow to optimize the fine-tuning process?

Accepted Answer

Start with a base model that is already strong in the domain.. Starting with a base model that is already strong in the domain (Option A) is correct because it reduces the amount of fine-tuning data and compute required. Amazon Bedrock provides access to various foundation models (e.g., Anthropic Claude, Amazon Titan) that have been pre-trained on diverse corpora; selecting one that is already proficient in the target domain (e.g., legal or medical summarization) means the model's existing knowledge can be adapted with fewer training steps, leading to better performance and lower risk of catastrophic forgetting.

Answer

Use the default hyperparameters without tuning.

Answer

Train for as many epochs as possible.

Question 2

A company is using Amazon Bedrock to generate code snippets. They want to ensure the generated code is secure. Which TWO practices should they implement?

Accepted Answer

Use guardrails to block insecure code patterns.. Amazon Bedrock Guardrails allow you to define policies that filter or block generated content containing insecure code patterns, such as SQL injection or hardcoded credentials, before the output is returned. This provides a proactive security layer that prevents insecure code from reaching the user, directly addressing the requirement to ensure generated code is secure.

Answer

Increase the max token limit to generate longer code.

Answer

Set the temperature to 0 for deterministic output.

Answer

Use a larger model for better accuracy.

Question 3

Refer to the exhibit. An IAM policy is attached to a user. Which models can the user invoke?

Accepted Answer

Only Claude v2. The IAM policy explicitly allows the `bedrock:InvokeModel` action only on the resource ARN `arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-v2`. This means the user can invoke only the Claude v2 model. No other models, including other Claude versions or any model with 'claude' in its name, are permitted because the resource ARN is exact and does not use wildcards.

Answer

No models

Answer

Claude v2 and any model with a name containing 'claude'

Answer

Any model in the account

Question 4

Refer to the exhibit. A user invokes Claude v2 using the AWS CLI. The response is truncated. What is the most likely cause?

Accepted Answer

The prompt includes a stop sequence 'Assistant:'.. The prompt includes the stop sequence 'Assistant:', which causes the model to halt generation as soon as it encounters that token sequence. In Claude v2, stop sequences are used to control the output length and structure; when the model generates the exact stop sequence, it truncates the response at that point, even if more content could have been produced.

Answer

The AWS CLI is missing the --endpoint-url parameter.

Answer

The max_tokens_to_sample is too low.

Answer

The model does not support this use case.

Question 5

A company is building a chatbot using Amazon Bedrock to answer customer questions about their product catalog. The chatbot should only use information from the company's internal knowledge base and should not generate answers based on the model's pre-training data. Which feature should be enabled?

Accepted Answer

Configure a knowledge base with Retrieval Augmented Generation (RAG). Configuring a knowledge base with Retrieval Augmented Generation (RAG) allows the chatbot to retrieve relevant documents from the company's internal knowledge base and use them as context for generating answers. This ensures the model's responses are grounded solely in the provided data, preventing reliance on its pre-training knowledge.

Answer

Use prompt engineering to instruct the model to only use the knowledge base

Answer

Enable model invocation logging to review responses

Answer

Fine-tune the model on the product catalog data

Question 6

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?

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.

Answer

Use prompt engineering to instruct the model to avoid harmful content

Answer

Enable model invocation logging to review and block responses

Answer

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

Question 7

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. 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.

Answer

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

Answer

Use the on-demand model through Amazon Bedrock

Answer

Use a third-party model hosted outside of AWS

Question 8

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.

Answer

Sentiment analysis

Answer

Text classification

Answer

Machine translation

Question 9

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. 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.

Answer

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

Answer

Provide few-shot examples of secure code

Answer

Set max_tokens to a low value to limit output

Question 10

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.

Answer

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

Answer

Use a separate model to validate the SQL after generation

Answer

Limit the max_tokens to 50 to reduce complexity

Question 11

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. 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.

Answer

Reduce the temperature parameter to 0.1

Answer

Increase the max_tokens to allow longer descriptions

Answer

Use human reviewers to correct errors after generation

Question 12

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.

Answer

Train a custom model using Amazon SageMaker

Answer

Fine-tune a large language model on labeled emails

Answer

Use Amazon Lex with a classifier intent

Question 13

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.

Answer

Accuracy

Answer

Perplexity

Answer

BLEU score

Question 14

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.

Answer

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

Answer

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

Answer

Implement adversarial validation by testing against toxic inputs.

Question 15

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.

Answer

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

Answer

The AWS region does not support the AI21 Labs model.

Answer

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