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

Question 1

A company wants to use a pre-trained generative AI model to analyze customer feedback. They need to adjust the model for their specific domain without retraining from scratch. Which approach is MOST suitable?

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

Accepted Answer

Fine-tuning the model on domain-specific data. Fine-tuning is the most suitable approach because it takes a pre-trained generative AI model and updates its weights using a smaller, domain-specific dataset (e.g., customer feedback transcripts). This allows the model to adapt to the company's specific terminology, sentiment patterns, and context without the massive computational cost and data requirements of training from scratch. It preserves the general language understanding from pre-training while specializing the model for the target domain.

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Reinforcement Learning from Human Feedback (RLHF)

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Training a new model from scratch on the domain data

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Using prompt engineering to provide context

Question 2

A company is using Amazon Bedrock to generate creative marketing copy. They want to reduce the randomness of the output while maintaining diversity. Which TWO parameters should they adjust?

Accepted Answer

Decrease the top_p value. Decreasing the temperature (Option E) reduces randomness by lowering the probability of sampling lower-ranked tokens, making the model more deterministic. Decreasing top_p (Option D) narrows the cumulative probability threshold for token selection, which also reduces randomness while still allowing some diversity within the narrowed set. Together, these parameters control the trade-off between creativity and determinism in Amazon Bedrock's text generation.

Answer

Increase the temperature

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Increase the max token count

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Increase the top_k value

Question 3

A developer attached this IAM policy to a role used by an application that invokes Claude v2 in us-east-1. The application receives an access denied error. What is the MOST likely cause?

Accepted Answer

The Deny statement is blocking requests because the condition does not match the resource ARN's region. The Deny statement uses a `StringNotEquals` condition on `aws:RequestedRegion` set to `us-east-1`. This means the Deny applies to any request where the requested region is NOT `us-east-1`. Since the resource ARN in the Deny statement is `arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-v2`, the condition does not match the resource's region (the resource ARN itself is in us-east-1), but the Deny is triggered when the request is made to a different region, blocking the call. The application is likely invoking the model from a region other than us-east-1, causing the Deny to take effect.

Answer

The Allow statement does not include a condition on the region

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The Deny statement uses StringNotEquals instead of StringEquals

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The resource ARN in the Allow statement is incorrect

Question 4

A developer is using the Amazon Bedrock InvokeModel API with the above request to summarize meeting notes. The response is a single word repeated many times. Which parameter is MOST likely causing this issue?

Accepted Answer

temperature set to 0. A temperature of 0 forces the model to always select the highest-probability token at each step, which can lead to repetitive loops if the most likely token repeatedly points back to itself (e.g., the same word). This deterministic behavior eliminates randomness, causing the model to get stuck in a single-word cycle rather than generating diverse or coherent text.

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topP set to 0.9

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stopSequences is empty

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maxTokenCount set to 100

Question 5

A financial services company is deploying a generative AI model on Amazon SageMaker for real-time fraud detection. The model, a fine-tuned Llama 2 7B, must respond to transaction requests within 500 milliseconds. The team has deployed the model using a SageMaker real-time endpoint with a single ml.g5.2xlarge instance. During load testing, the endpoint achieves an average latency of 450 ms at 10 requests per second (RPS), but the latency spikes to over 2 seconds at 20 RPS. The team needs to maintain sub-500 ms latency at up to 50 RPS. The model is too large to fit on a single GPU, so they are using CPU instances. They considered using a larger instance type but want to minimize cost. What should the team do to meet the latency requirement cost-effectively?

Accepted Answer

Use a SageMaker multi-model endpoint with multiple ml.g5.xlarge instances and auto scaling. A SageMaker multi-model endpoint (MME) allows multiple model replicas to be hosted on a fleet of instances, enabling horizontal scaling to handle increased throughput. By using multiple ml.g5.xlarge instances with auto scaling, the team can distribute the 50 RPS load across several instances, keeping per-instance latency low while minimizing cost compared to a single larger instance. This approach also leverages the fact that the model is too large for a single GPU but can be efficiently served on CPU instances with proper load distribution.

Answer

Upgrade to a single ml.g5.4xlarge instance

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Attach an Amazon Elastic Inference accelerator to the existing instance

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Use SageMaker Serverless Inference to automatically scale

Question 6

A media company is using Amazon Bedrock to generate captions for images. They have a batch processing pipeline that sends thousands of images daily to the Bedrock API using the Titan Image Generator G1 model. Recently, they started receiving ThrottlingException errors during peak hours. The team needs to process all images within 24 hours without changing the model or the application code. The current account has a default quota of 10 requests per second (RPS) for the Titan model in us-east-1. The team estimates they need 50 RPS during peak hours. They have already implemented exponential backoff in the client, but the errors persist. What is the MOST effective solution to resolve the throttling issue?

Accepted Answer

Request a service quota increase for the InvokeModel API for the Titan model in us-east-1. The team has already implemented exponential backoff, but the errors persist because their current quota of 10 RPS is insufficient for the required 50 RPS. Requesting a service quota increase for the InvokeModel API for the Titan Image Generator G1 model in us-east-1 directly addresses the root cause by raising the throughput limit, allowing the existing application code and model to handle the peak load without any architectural changes.

Answer

Use Amazon SageMaker batch transform to process images offline

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Distribute the requests across multiple AWS Regions

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Switch to a different foundation model that has a higher default quota

Question 7

A company is building a chatbot that must provide accurate answers based on internal documents without retraining the model. Which approach should they use?

Accepted Answer

Prompt engineering with retrieval-augmented generation (RAG). Retrieval-augmented generation (RAG) allows the chatbot to fetch relevant internal documents at inference time and incorporate them into the prompt, providing accurate, up-to-date answers without retraining the model. This approach combines prompt engineering with a retrieval step, ensuring the model's responses are grounded in the company's specific knowledge base while keeping the base model frozen.

Answer

Reinforcement learning from human feedback (RLHF)

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Fine-tuning the model on internal documents

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Model distillation to a smaller model

Question 8

A developer wants to test different foundation models quickly without setting up infrastructure. Which AWS service allows interactive prompting and comparison of multiple models?

Accepted Answer

Amazon Bedrock Playground. Amazon Bedrock Playground is a feature within Amazon Bedrock that provides a web-based interface for interactive prompting and side-by-side comparison of multiple foundation models (FMs). It allows developers to test different models quickly without provisioning any infrastructure, making it ideal for rapid experimentation and evaluation.

Answer

Amazon Comprehend

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Amazon Lex

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Amazon SageMaker Studio

Question 9

A company is building a customer service chatbot using Amazon Bedrock. Which component of a foundation model determines the creativity and randomness of the generated responses?

Accepted Answer

Temperature. The temperature parameter controls randomness. Higher values (e.g., >1) produce more creative but less focused outputs, while lower values (e.g., near 0) produce more deterministic responses.

Answer

Prompt template

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Max tokens

Answer

Top-p

Question 10

A retail company wants to generate product descriptions from catalog data. The data includes structured attributes (e.g., price, brand) and unstructured reviews. The team needs to ensure factual accuracy. Which approach is most appropriate?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG) with a knowledge base. Retrieval-Augmented Generation (RAG) retrieves relevant documents (product attributes, reviews) and provides them as context to the model, reducing hallucinations and grounding responses in facts.

Answer

Use prompt engineering with few-shot examples

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Fine-tune a foundation model on the entire product catalog

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Deploy a larger foundation model with more parameters

Question 11

Which AWS service provides a serverless experience for building and scaling generative AI applications with access to various foundation models?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is a fully managed service that provides a serverless experience for building and scaling generative AI applications. It offers access to a variety of foundation models (FMs) from providers like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon via a single API, without the need to manage underlying infrastructure.

Answer

Amazon SageMaker

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Amazon Lex

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AWS Lambda

Question 12

A startup is building an AI-powered code assistant using a large language model (LLM). They want to ensure the model generates syntactically correct code and avoids security vulnerabilities. Which technique should they prioritize?

Accepted Answer

Augment prompts with few-shot examples of secure coding practices and unit tests. Contextual grounding by providing code examples and security guidelines in the prompt (prompt engineering) helps guide the model to produce safe and correct code. Fine-tuning on secure codebases would also help but is more resource-intensive; prompt engineering is a quicker first step.

Answer

Deploy the model with max tokens set to 4096

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Fine-tune the model on a large corpus of open-source code

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Use chain-of-thought prompting to explain reasoning before code generation

Question 13

Which TWO practices help ensure responsible AI when deploying generative AI applications? (Select TWO.)

Accepted Answer

Implement guardrails to filter harmful or inappropriate content. Implementing guardrails (e.g., content filtering) to prevent harmful outputs (D) and continuously monitoring outputs for bias and drift (E) are essential responsible AI practices. Deploying without filters (A) is unsafe, increasing model size (B) reduces interpretability and doesn't address responsibility, and using only synthetic data (C) can introduce bias and is impractical. The correct answers are D and E.

Answer

Deploy the model without any content filters to maximize creativity

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Increase model size to improve accuracy at the expense of interpretability

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Use only synthetic data for training to avoid privacy issues

Question 14

Refer to the exhibit. A company sets up a knowledge base for a customer support chatbot using Amazon Bedrock. Users report that the chatbot misses relevant details from long documents. Which change to the data source configuration would most likely improve retrieval?

Accepted Answer

Change chunking strategy to SEMANTIC. Semantic chunking groups text based on meaning rather than fixed token counts, preserving the natural boundaries of concepts and paragraphs. This ensures that relevant details from long documents remain intact within a single chunk, improving retrieval accuracy for the chatbot.

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Increase the chunk size in FIXED_SIZE chunking

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Add more documents to the S3 bucket

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Change the embedding model to a larger one

Question 15

A company wants to build a generative AI application that can summarize customer support tickets. They need to ensure the model stays up-to-date with the latest product documentation without retraining. Which AWS service would best support this requirement?

Accepted Answer

Amazon Bedrock with Retrieval Augmented Generation (RAG). Amazon Bedrock with Retrieval Augmented Generation (RAG) is the correct choice because it allows the generative AI model to access and incorporate the latest product documentation from an external knowledge base without retraining. RAG works by retrieving relevant document chunks at inference time and injecting them into the model's context, ensuring responses reflect current information. This directly meets the requirement for staying up-to-date with evolving documentation while avoiding the cost and latency of full model retraining.

Answer

Amazon Comprehend

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Amazon Rekognition

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Amazon SageMaker Ground Truth