AIF-C01 Fundamentals of Generative AI Practice Test 2 — 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?

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.

Answer

Reinforcement Learning from Human Feedback (RLHF)

Answer

Training a new model from scratch on the domain data

Answer

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

Answer

Increase the max token count

Answer

Increase the top_k value

Question 3

A data science team is evaluating foundation models for a code generation task. They need a model that is fine-tuned for code and can be deployed on Amazon SageMaker. Which THREE criteria are important to consider when selecting a model?

Accepted Answer

Licensing and usage terms. Option A is correct because licensing and usage terms directly impact whether a foundation model can be legally used for commercial code generation. Models like Code Llama or StarCoder have specific licenses (e.g., Llama 2 Community License, OpenRAIL-M) that may restrict fine-tuning, redistribution, or use in proprietary products. Ignoring these terms could lead to compliance violations when deploying on Amazon SageMaker.

Answer

Cost per token for inference

Answer

The training algorithm used

Question 4

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

Answer

The Deny statement uses StringNotEquals instead of StringEquals

Answer

The resource ARN in the Allow statement is incorrect

Question 5

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.

Answer

topP set to 0.9

Answer

stopSequences is empty

Answer

maxTokenCount set to 100

Question 6

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. Option C is correct because 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

Answer

Attach an Amazon Elastic Inference accelerator to the existing instance

Answer

Use SageMaker Serverless Inference to automatically scale

Question 7

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

Answer

Distribute the requests across multiple AWS Regions

Answer

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

Question 8

A company wants to generate product descriptions from a few keywords without managing infrastructure. Which AWS service provides a serverless API for accessing foundation models?

Accepted Answer

Amazon Bedrock. Amazon Bedrock offers a serverless API to invoke foundation models. Option A (SageMaker) is for training and deployment, not serverless API. Option C (Comprehend) is for NLP analysis, not generative. Option D (Lex) is for conversational interfaces.

Answer

Amazon Lex

Answer

Amazon SageMaker

Answer

Amazon Comprehend

Question 9

A data scientist is evaluating foundation models for a text summarization task and wants to use a standard metric. Which metric is commonly used to assess the quality of generated summaries?

Accepted Answer

ROUGE. ROUGE is the standard metric for summarization, measuring overlap of n-grams. Option A (Accuracy) is for classification. Option C (BLEU) is for translation. Option D (F1 score) is for classification.

Answer

F1 score

Answer

BLEU

Answer

Accuracy

Question 10

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). RAG (Retrieval-Augmented Generation) retrieves relevant documents and augments the prompt, avoiding retraining. Option A (Fine-tuning) requires retraining. Option C (Distillation) reduces model size. Option D (RLHF) is for aligning model behavior.

Answer

Reinforcement learning from human feedback (RLHF)

Answer

Fine-tuning the model on internal documents

Answer

Model distillation to a smaller model

Question 11

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 provides a playground feature for testing models. Option A (SageMaker Studio) is for full notebook environment. Option C (Comprehend) is for analysis. Option D (Lex) is for chatbots.

Answer

Amazon Comprehend

Answer

Amazon Lex

Answer

Amazon SageMaker Studio

Question 12

A machine learning engineer notices that a generative AI model occasionally produces biased outputs. Which AWS feature can automatically filter harmful content before it reaches users?

Accepted Answer

Amazon Bedrock Guardrails. Amazon Bedrock Guardrails provide content filtering and topic control. Option A (IAM) is for permissions. Option C (CloudWatch) is for monitoring. Option D (SageMaker Clarify) is for bias detection, not real-time filtering.

Answer

Amazon CloudWatch alarms

Answer

Amazon SageMaker Clarify

Answer

AWS Identity and Access Management (IAM) policies

Question 13

A team is using Amazon Bedrock with a Claude model and wants to ensure responses adhere to a specific output format such as JSON. Which technique should be applied?

Accepted Answer

Include a system prompt with explicit formatting instructions. System prompt instructions can enforce output format. Option A (Guardrails) is for safety. Option B (Retrieval augmentation) is for knowledge. Option D (Model customization) is for fine-tuning.

Answer

Use a retrieval-augmented generation (RAG) approach

Answer

Attach a guardrail with a JSON schema

Answer

Customize the model with a JSON training dataset

Question 14

A startup wants to integrate a generative AI chatbot into their mobile app with minimal latency. Which AWS service is purpose-built for deploying foundation models with low latency and high throughput?

Accepted Answer

Amazon Bedrock. Amazon Bedrock offers low-latency inference endpoints. Option A (SageMaker) is for training and inference but not purpose-built for foundation models. Option C (Lambda) is for serverless compute. Option D (Transcribe) is for speech.

Answer

AWS Lambda

Answer

Amazon SageMaker

Answer

Amazon Transcribe

Question 15

A company wants to personalize its generative AI model for its specific domain without sharing data with third-party model providers. Which method should they use?

Accepted Answer

Fine-tuning the foundation model on their proprietary data. Amazon Bedrock allows fine-tuning of foundation models with customer data in a secure environment. Option A (Prompt engineering) doesn't personalize deeply. Option B (RAG) adds context but doesn't modify model. Option C (Distillation) requires an existing model.

Answer

Prompt engineering with domain-specific examples

Answer

Retrieval-augmented generation (RAG) with a domain-specific knowledge base

Answer

Model distillation using a larger foundation model