AIF-C01 Applications of Foundation Models Practice Test 3 — 15 Questions

Question 1

A data science team fine-tuned a foundation model on Amazon SageMaker for sentiment analysis of customer reviews. They deployed the model as a real-time endpoint. After a successful launch, the application experienced a surge in traffic, and the endpoint's latency increased from 200ms to over 2 seconds. The team needs to reduce latency and maintain high throughput without increasing costs significantly. They are using a single ml.g5.xlarge instance. What change should the team make first?

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

Accepted Answer

Compile the model using SageMaker Neo to optimize for inference.. Compiling the model with SageMaker Neo optimizes the model for the target hardware, significantly reducing inference latency without increasing compute cost. Option A (enable automatic scaling) adds instances to handle traffic but does not reduce per-request latency and may increase cost. Option B (increase instance type) increases compute capacity but also increases cost without addressing latency per request. Option D (switch to batch transform) is not suitable for real-time inference and would not meet the latency requirement.

Answer

Enable automatic scaling for the endpoint.

Answer

Increase the instance type to ml.g5.4xlarge.

Answer

Switch to a batch transform job instead of real-time.

Question 2

A company uses Amazon Bedrock to power a generative chatbot for employee onboarding. Recently, some employees reported that the chatbot occasionally provides responses that contain biased or offensive language. The company has a strict policy for respectful communication. They want to implement a solution quickly without retraining the model. Which action should they take?

Accepted Answer

Enable Amazon Bedrock's built-in content moderation filters.. Amazon Bedrock includes built-in content moderation filters that can be enabled to automatically detect and block biased or offensive language in model responses without requiring retraining or custom infrastructure. This allows the company to quickly enforce their respectful communication policy by filtering out harmful content at inference time, directly addressing the reported issue.

Answer

Add a human reviewer to approve every response.

Answer

Use a different foundational model known for unbiased outputs.

Answer

Fine-tune the model on a dataset of polite conversations.

Question 3

A marketing agency uses a foundation model to generate images for social media campaigns. Some generated images have contained violent or inappropriate content, damaging the brand. The agency needs to prevent such content from being displayed automatically. They are using Amazon Bedrock for image generation with Stable Diffusion. What is the most effective way to filter out inappropriate images?

Accepted Answer

Enable the safety checker in Amazon Bedrock's image generation models.. Amazon Bedrock's image generation models (including Stable Diffusion) have a built-in safety checker that automatically detects and blocks NSFW or inappropriate content during generation, preventing such images from being output without manual effort. Option A (Amazon Rekognition) adds cost and latency for post-generation analysis, while Option B (manual review) is not scalable. Option C (restricting the prompt) is unreliable as models can still generate inappropriate content from seemingly safe prompts.

Answer

Use Amazon Rekognition to analyze images after generation.

Answer

Manually review all images before posting.

Answer

Restrict the prompt to avoid triggering keywords.

Question 4

A law firm uses a foundation model to draft legal briefs. To ensure accuracy, they want to ground the model's outputs in authoritative legal sources. They have a large database of prior case law and statutes stored in Amazon S3. The firm's IT team must implement a solution that reduces hallucinations while being cost-effective. The solution should allow the model to retrieve relevant documents and generate responses based on them. Which approach should they take?

Accepted Answer

Use Amazon Bedrock Agents to create a RAG application.. Amazon Bedrock Agents with a knowledge base can implement Retrieval-Augmented Generation (RAG): the agent retrieves relevant documents from S3 and uses them as context for the model, grounding responses and reducing hallucinations. Option A (fine-tuning) is expensive and does not guarantee grounding for all queries. Option B (manually attaching documents) is not scalable. Option C (using a larger model) increases cost without solving hallucination.

Answer

Fine-tune the model on the legal database.

Answer

Manually attach relevant documents to each prompt.

Answer

Use a larger foundation model with more parameters.

Question 5

A startup company is developing an e-commerce platform and wants to use Amazon Bedrock to generate product descriptions automatically. They have a small team of developers who are not machine learning experts. The product catalog is stored in a DynamoDB table, and each product has attributes like name, category, price, and a brief description. The company wants the generated descriptions to reflect the unique brand voice, which is documented in a few internal style guides stored as PDF files in Amazon S3. They need a solution that allows them to quickly test the approach without significant infrastructure changes or model training. The development team is familiar with AWS SDKs and want to minimize ongoing maintenance. The team has already set up a Bedrock foundation model (Claude) and can make API calls. They tested simple prompts but the output lacked the brand's informal yet professional tone. They want to incorporate examples from the style guides directly into the prompt without retraining. The team fears that including the entire style guide in each prompt would exceed token limits and increase costs. Which approach should they take to effectively incorporate the brand voice with minimal changes?

Accepted Answer

Use Amazon Bedrock with a custom prompt template that includes a few representative examples from the style guides as few-shot examples in the system prompt.. Uses in-context learning with carefully selected examples from the style guides (few-shot prompting), which is simple, avoids retraining, and keeps token usage low. Option A requires fine-tuning, which is complex and not quick. Option C concatenates all style guides, exceeding token limits and increasing costs. Option D does not capture the brand voice effectively.

Answer

Fine-tune the foundation model using the style guides with Amazon Bedrock Custom Models.

Answer

Concatenate all style guide PDFs into a single text and include it in every prompt.

Answer

Use Amazon Comprehend to analyze the style guides and extract a list of keywords to include in the prompt.

Question 6

A large enterprise uses Amazon Bedrock to power a conversational agent that handles customer service inquiries. The agent is built using Bedrock Agents and retrieves information from a knowledge base that contains product documentation and FAQs. Recently, users have reported that the agent sometimes provides incorrect information that contradicts the knowledge base. The development team verified that the knowledge base contains accurate and up-to-date data. They also confirmed that the retrieval process correctly fetches relevant documents. However, the agent occasionally ignores the retrieved context and generates plausible-sounding but incorrect answers. The team is concerned about customer trust and wants to improve the accuracy of the agent's responses without overhauling the architecture. They have already tuned the prompt template to instruct the model to use the context. The issue persists. Which additional action should the team take to reduce the number of hallucinated responses?

Accepted Answer

Add explicit instructions in the system prompt to require the model to base its answers solely on the retrieved context and to state when it doesn't have enough information.. The correct answer because it directly strengthens the instruction to the model to rely solely on the retrieved context, which addresses the issue of the agent ignoring context and hallucinating. The team has already tuned the prompt, but adding explicit requirements to base answers on context and to acknowledge when information is insufficient can further reduce hallucinations without architectural changes. Option A (reducing chunk size) might improve retrieval granularity but does not force the model to use the context. Option B (switching to a larger model) may improve general capability but does not guarantee context adherence and could be costly. Option C (increasing temperature) would increase randomness, likely worsening hallucinations.

Answer

Reduce the chunk size of documents in the knowledge base to retrieve more granular information.

Answer

Switch to a larger foundation model with more parameters.

Answer

Increase the temperature parameter of the foundation model.

Question 7

A company is deploying a customer service chatbot using a large language model (LLM) via Amazon Bedrock. The application must meet high accuracy for domain-specific queries, low latency, and be cost-effective. Which TWO strategies should the company adopt to achieve these goals? (Choose two.)

Accepted Answer

Store user prompts in a shared cache to reuse common queries.. Caching user prompts for common queries reduces the number of API calls to the LLM, lowering latency and cost while maintaining accuracy for repeated domain-specific questions. This is a standard optimization technique in production LLM deployments, where identical or similar prompts can be served from a cache without re-invoking the model.

Answer

Fine-tune the model on a large corpus of customer service transcripts to improve domain knowledge.

Answer

Select a smaller, faster model that trades some accuracy for throughput.

Answer

Increase the model's maximum token limit to handle longer customer queries.

Question 8

A healthcare company is using Amazon Bedrock to summarize patient notes. The compliance team requires that no patient data is used to improve the underlying foundation model. Which configuration should the team choose?

Accepted Answer

Disable model training data logging in the AWS console.. Disabling model training data logging in the AWS console prevents Amazon Bedrock from using customer inference data to improve the underlying foundation model. This setting ensures compliance with the requirement that no patient data is used for model training, as Bedrock offers a specific toggle to opt out of data sharing for model improvement.

Answer

Enable data encryption in transit and at rest.

Answer

Use a different foundation model from a different provider.

Answer

Configure a VPC endpoint for Amazon Bedrock.

Question 9

A marketing firm uses Amazon Bedrock to generate ad copy. They notice that the generated text often includes factual inaccuracies about their products. Which technique would most effectively reduce these inaccuracies?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG) with a product knowledge base.. Retrieval-Augmented Generation (RAG) grounds the model's output in a trusted, external knowledge base by retrieving relevant product documents before generating text. This directly addresses factual inaccuracies because the model references authoritative data rather than relying solely on its parametric memory, which may contain outdated or incorrect information.

Answer

Use longer, more detailed prompts.

Answer

Increase the temperature parameter to 0.9.

Answer

Fine-tune the model on a dataset of previous ad copies.

Question 10

A developer is using Amazon Bedrock to build a chatbot that answers customer queries. The chatbot must only respond based on the provided company documentation. Which approach best meets this requirement?

Accepted Answer

Use a RAG architecture with the company documentation as the knowledge base.. Retrieval-Augmented Generation (RAG) architecture retrieves relevant chunks from the company documentation at query time and injects them into the prompt, ensuring the model's response is grounded solely in the provided documents. This approach prevents the model from relying on its internal training data or generating information outside the documentation, which is critical for a closed-domain chatbot.

Answer

Use prompt engineering to instruct the model to only use documentation.

Answer

Fine-tune a foundation model on the company documentation.

Answer

Use a text classification model to filter responses.

Question 11

A financial services company is deploying a foundation model to analyze customer sentiment from call transcripts. The model outputs must be consistent and deterministic for auditing purposes. Which parameter configuration should the company use?

Accepted Answer

Set temperature to 0 and top_p to 1.. Setting temperature to 0 and top_p to 1 forces the model to always select the highest-probability token at each step, producing deterministic and repeatable outputs. This is essential for auditing and compliance in financial services, where consistency is required. Any nonzero temperature introduces randomness, which undermines determinism.

Answer

Set temperature to 0.1 and top_p to 0.9.

Answer

Set temperature to 0.7 and top_p to 1.0.

Answer

Set temperature to 0.5 and top_p to 0.5.

Question 12

An e-commerce company is using a foundation model to generate product descriptions. They want to reduce costs by caching frequently requested descriptions. Which AWS service should they use to implement a cache?

Accepted Answer

Amazon ElastiCache. Amazon ElastiCache is the correct choice because it provides an in-memory caching layer (using Redis or Memcached) that can store frequently requested product descriptions, reducing the need to invoke the foundation model repeatedly. This directly lowers inference costs and latency by serving cached responses instead of generating new ones each time.

Answer

Amazon CloudFront

Answer

Amazon DynamoDB

Answer

Amazon S3

Question 13

A company wants to use a foundation model to automatically moderate user-generated content. The model must filter out inappropriate content with high accuracy. Which Amazon service is best suited for this task?

Accepted Answer

Amazon Comprehend. Amazon Comprehend is the correct choice because it is a natural language processing (NLP) service that can analyze text for sentiment, key phrases, and — critically — toxicity and inappropriate content using built-in or custom classifiers. This directly matches the requirement to moderate user-generated text with high accuracy, as it can detect hate speech, profanity, and other harmful language.

Answer

Amazon Translate

Answer

Amazon Rekognition

Answer

Amazon Polly

Question 14

A startup is using Amazon Bedrock to power a virtual assistant. They need to ensure that personally identifiable information (PII) is not included in the model's responses. Which feature should they enable?

Accepted Answer

Enable PII redaction in the Bedrock guardrails.. Amazon Bedrock Guardrails provide a configurable content filtering and PII redaction feature that can automatically detect and mask personally identifiable information (PII) in model inputs and outputs. By enabling PII redaction within guardrails, the startup can ensure that sensitive data like names, addresses, or credit card numbers are removed or obfuscated before the virtual assistant's responses reach the user. This is the direct and intended mechanism for preventing PII leakage in model responses.

Answer

Enable model invocation logging.

Answer

Configure a VPC endpoint.

Answer

Enable data encryption at rest.

Question 15

A company is using Amazon Bedrock to generate marketing content. They want to evaluate the quality of the generated text. Which TWO metrics are most appropriate for evaluating text quality?

Accepted Answer

Perplexity. Perplexity measures how well a language model predicts a sample, with lower values indicating higher confidence and coherence in generated text. BLEU evaluates the overlap between generated text and reference text, making it suitable for assessing fluency and relevance in content generation tasks like marketing copy.

Answer

Precision

Answer

Accuracy

Answer

F1 score