AIF-C01 Fundamentals of Generative AI Practice Test 6 — 15 Questions

Question 1

A team is developing a real-time code completion feature using an LLM deployed on Amazon SageMaker. They observe high latency under load. Which optimization technique should they prioritize?

Accepted Answer

Use model quantization. Option B, model quantization, reduces the model size and speeds up inference directly, lowering latency. Option A (Auto Scaling) improves throughput but not per-request latency. Option C (increase batch size) improves throughput but may increase per-token latency. Option D (larger instance) may improve but not as effectively as quantization.

Answer

Increase batch size

Answer

Switch to a larger instance type

Answer

Increase instance count with Auto Scaling

Question 2

Which TWO AWS services can be used to build a chatbot that responds to customer inquiries using a company's documentation as source? (Select two.)

Accepted Answer

Amazon Bedrock with RAG. Options B (Amazon Bedrock with RAG) and C (Amazon Q Business) are correct. Bedrock with RAG retrieves from the documentation to generate answers, and Amazon Q Business is a conversational service that can use enterprise data. Option A (Amazon Lex) can build chatbots but requires integration for documentation retrieval. Option D (Amazon Polly) is text-to-speech. Option E (Amazon Transcribe) is speech-to-text.

Answer

Amazon Polly

Answer

Amazon Transcribe

Answer

Amazon Lex

Question 3

A company wants to evaluate the performance of a generative AI model before deployment. Which TWO metrics are most relevant for measuring model quality? (Select two.)

Accepted Answer

BLEU score. Options A (BLEU score) and C (Perplexity) are standard for evaluating text generation quality. BLEU measures similarity to reference text, and perplexity measures how well the model predicts a sample. Option B (CPU utilization) is operational, not quality. Option D (Response time) is latency. Option E (Model size) is a design parameter.

Answer

Response time

Answer

Model size

Answer

CPU utilization

Question 4

Which THREE factors should be considered when selecting a foundation model for a text generation task? (Select three.)

Accepted Answer

Context window length. Options A (Context window length), B (Number of parameters), and D (Model license) are key selection criteria. Context window affects input length, parameters affect capability, license affects usage rights. Option C (Inference latency) is operational but often considered after selection. Option E (AWS Region availability) is relevant for deployment but not model selection.

Answer

Inference latency

Answer

AWS Region availability

Question 5

Refer to the exhibit. A user invoked a Claude model using provisioned throughput and received a ThrottlingException. Which is the most likely cause?

Accepted Answer

The provisioned throughput request per minute limit was exceeded. Option A is correct. Provisioned throughput has a requests-per-minute limit, and exceeding it causes a ThrottlingException. Option B would produce a different error. Option C would be a validation error, not throttling. Option D is not the cause because PROVISIONED is valid.

Answer

The model is not available in the region

Answer

The prompt was too long

Answer

The inference type should be ON_DEMAND

Question 6

Refer to the exhibit. A developer is optimizing latency for a generative AI model deployed on SageMaker. Based on the exhibit, which change would most likely reduce per-token latency?

Accepted Answer

Reduce model size through quantization. Option C, reduce model size through quantization, directly reduces computation per token, lowering latency. Option A (larger instance) may help but is less targeted. Option B (increase batch size) improves throughput but not per-token latency. Option D (CPU instance) would increase latency.

Answer

Use a CPU instance

Answer

Switch to a larger instance type

Answer

Increase batch size to 10

Question 7

Refer to the exhibit. A developer wants to choose a model that can generate text (not just embeddings) and has the lowest cost. Based on the exhibit, which model should they select?

Accepted Answer

Titan Text Lite. Option A, Titan Text Lite, is a text generation model and is the lighter, cheaper option compared to Express. Titan Text Express is more expensive. Titan Embed Text is for embeddings, not text generation. Therefore, Titan Text Lite is correct.

Answer

Titan Embed Text

Answer

Titan Text Express

Answer

Need more information

Question 8

A company is building a customer support chatbot using Amazon Bedrock. They have a large corpus of internal documentation and want to provide accurate answers without retraining the model. Which approach should they use?

Accepted Answer

Use Retrieval Augmented Generation (RAG) with Amazon Bedrock Knowledge Base.. Option C is correct because Retrieval Augmented Generation (RAG) with Bedrock Knowledge Base allows the model to retrieve relevant documents from internal sources and generate grounded answers, avoiding the need for fine-tuning. Option A is wrong because a pre-trained model alone lacks domain knowledge. Option B is wrong because fine-tuning requires labeled data and is more costly. Option D is wrong because prompt engineering alone cannot incorporate proprietary data effectively.

Answer

Use advanced prompt engineering with a generic foundation model.

Answer

Use a pre-trained foundation model without customization.

Answer

Fine-tune the model on their documentation using Amazon SageMaker.

Question 9

A developer wants to generate product description images using Amazon Bedrock. They need to ensure the generated images match a specific brand style. Which feature should they primarily use?

Accepted Answer

Prompt engineering with detailed style descriptions.. Option A is correct because prompt engineering allows the developer to specify style guidelines in the text prompt, influencing the output. Option B is wrong because fine-tuning for image style is time-consuming. Option C is wrong because grounding is for text, not images. Option D is wrong because data augmentation is not directly relevant.

Answer

Output grounding to verify brand compliance.

Answer

Data augmentation to increase dataset diversity.

Answer

Fine-tuning the image generation model on brand assets.

Question 10

A financial services company is subject to strict regulatory requirements. They plan to use generative AI to summarize customer interaction logs. Which combination of AWS services and configurations best ensures compliance while maintaining accuracy?

Accepted Answer

Use Amazon Bedrock with a private VPC endpoint, AWS KMS encryption, and content filtering.. Option D is correct because Amazon Bedrock with a private VPC endpoint and data encryption at rest and in transit ensures data sovereignty, and using a foundation model that supports content filtering reduces risk of non-compliant outputs. Option A is wrong because Bedrock does not support local on-premises deployment. Option B is wrong because SageMaker alone does not provide built-in content filtering. Option C is wrong because using a public API key violates security policies.

Answer

Deploy an open-source model on Amazon Bedrock in a local on-premises server.

Answer

Use Amazon Bedrock with a foundation model and public internet access without encryption.

Answer

Use Amazon SageMaker to host a fine-tuned model with a public API key.

Question 11

A company is using Amazon Bedrock to generate code snippets. They notice the model occasionally generates code that fails to compile. What is the most effective way to improve code quality without retraining?

Accepted Answer

Use few-shot prompt engineering with correct code examples and formatting instructions.. Option B is correct because prompt engineering with examples and constraints can guide the model to produce more accurate code. Option A is wrong because reducing temperature increases determinism but doesn't guarantee correctness. Option C is wrong because fine-tuning is expensive and may overfit. Option D is wrong because increasing max tokens may lead to more errors.

Answer

Reduce the temperature parameter to 0 for deterministic output.

Answer

Increase the max token limit to allow the model to complete the code fully.

Answer

Fine-tune the model on a dataset of correct code snippets.

Question 12

A healthcare organization wants to use generative AI to draft clinical notes from patient-physician conversations. They must comply with HIPAA and minimize false medical information. Which approach should they take?

Accepted Answer

Use Amazon Bedrock with a HIPAA-eligible foundation model and connect it to a medical knowledge base via RAG.. Option A is correct because Amazon Bedrock offers HIPAA-eligible models and allows grounding with medical knowledge bases to reduce hallucinations. Option B is wrong because it does not use grounding. Option C is wrong because open-source LLMs may not be HIPAA-compliant. Option D is wrong because increasing temperature introduces more randomness, worsening accuracy.

Answer

Use Amazon SageMaker JumpStart with a publicly available clinical model and no additional modifications.

Answer

Use a generic open-source LLM hosted on Amazon EC2 with manual prompt engineering.

Answer

Use Amazon Bedrock with a large foundation model and a high temperature setting for creativity.

Question 13

A startup wants to quickly prototype a generative AI application for summarizing news articles. They have limited ML expertise and want minimal infrastructure management. Which AWS service should they use?

Accepted Answer

Amazon Bedrock with a foundation model accessed via API.. Option C is correct because Amazon Bedrock provides serverless access to foundation models via API, requiring no ML infrastructure. Option A is wrong because Amazon SageMaker requires managing training jobs and endpoints. Option B is wrong because AWS Lambda is a compute service, not a generative AI service. Option D is wrong because Amazon EC2 requires manual setup of models.

Answer

Amazon SageMaker to build and train a custom summarization model.

Answer

AWS Lambda with a custom Python script using the Hugging Face Transformers library.

Answer

Amazon EC2 instance running a pre-trained model from AWS Marketplace.

Question 14

A company uses Amazon Bedrock to generate marketing content. They want to reduce costs while maintaining response quality. Which action is most effective?

Accepted Answer

Select a smaller foundation model that still meets accuracy requirements.. Option D is correct because selecting a smaller, efficient foundation model can reduce cost per token while maintaining quality for simple tasks. Option A is wrong because increasing temperature does not reduce cost. Option B is wrong because caching may not be effective for variable outputs. Option C is wrong because fine-tuning increases cost.

Answer

Fine-tune a larger model to improve accuracy and reduce retries.

Answer

Increase the temperature parameter to get shorter responses.

Answer

Cache previous responses to reuse for similar prompts.

Question 15

A developer is using Amazon Bedrock to create a chatbot. They want to ensure the bot does not generate toxic or offensive content. Which feature should they enable?

Accepted Answer

Enable content filtering on the Bedrock model.. Option B is correct because Bedrock offers content filtering to detect and block harmful content. Option A is wrong because fine-tuning may not fully filter toxic content. Option C is wrong because prompt engineering alone is not enough. Option D is wrong because response validation is not a built-in feature of Bedrock.

Answer

Use careful prompt engineering to avoid toxic responses.

Answer

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

Answer

Implement external response validation using a third-party API.