AIF-C01 Fundamentals of Generative AI Practice Test 2 — 15 Questions

Question 1

A research team needs to generate high-quality images with Amazon Bedrock that are realistic and consistent with a specific artistic style. Which combination of parameters should they use?

Accepted Answer

Use a CFG (classifier-free guidance) scale and include a style prompt. Classifier-free guidance (CFG) scale controls how strongly the model adheres to the prompt, and including a style prompt (e.g., 'in the style of Impressionism') directly conditions the generation to produce images consistent with a specific artistic style. This combination allows the research team to balance prompt fidelity (via CFG scale) and stylistic control (via the style prompt), which is essential for generating realistic, style-consistent images in Amazon Bedrock.

Answer

High temperature, low top_p

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Low temperature, high top_p

Answer

Increase the number of steps and reduce the number of samples

Question 2

A company wants to automate the extraction of key information from customer support tickets using generative AI. They have a small labeled dataset. Which approach would be most effective?

Accepted Answer

Fine-tune a foundation model on the labeled data. Fine-tuning a foundation model on a small labeled dataset is the most effective approach because it adapts the pre-trained model's knowledge to the specific task of extracting key information from support tickets, requiring far less data and compute than training from scratch. This leverages transfer learning, where the model already understands language structure and can learn the nuances of the target extraction task with minimal additional examples.

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Use zero-shot prompting with a foundation model

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Train a custom model from scratch

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Use Amazon Comprehend custom entity recognition

Question 3

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

Accepted Answer

Context window length. Option A (Context window length) is correct because the context window determines how many tokens the model can accept as input plus generate as output in a single request, which directly constrains whether long prompts, documents, or multi-turn conversations can be handled for the text generation task. Option C (Model license) is correct because the license governs whether the model may be used commercially, modified, or redistributed, and it dictates legal and cost implications for deploying the model in a production text generation workload. Option D (Number of parameters) is correct because parameter count is a primary driver of the model's capability, quality, and resource requirements, so it must be matched to the task's accuracy needs and the available compute budget. Option B (Inference latency) is not among the marked correct answers here, and Option E (AWS Region availability) is likewise not marked, so neither should be treated as a required selection factor for this question.

Answer

Inference latency

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AWS Region availability

Question 4

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. Provisioned throughput guarantees a specific number of processing units (TPUs) for your model invocation. When you exceed the requests-per-minute (RPM) or tokens-per-minute (TPM) limit allocated to your provisioned throughput, Amazon Bedrock returns a ThrottlingException. Option B correctly identifies that the user hit the provisioned throughput request per minute limit.

Answer

The model is not available in the region

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The prompt was too long

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The inference type should be ON_DEMAND

Question 5

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.. Retrieval Augmented Generation (RAG) with Amazon Bedrock Knowledge Base is the correct approach because it allows the chatbot to retrieve relevant chunks from the internal documentation in real time and pass them as context to the foundation model. This provides accurate, up-to-date answers without retraining, directly addressing the requirement to avoid model customization.

Answer

Use advanced prompt engineering with a generic foundation model.

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Use a pre-trained foundation model without customization.

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

Question 6

A company is building a generative AI application to personalize email marketing campaigns. They use Amazon Bedrock with Anthropic Claude 3 Sonnet. The system takes customer data (name, purchase history) from an Amazon DynamoDB table and generates a personalized email body. During testing, the team notices that some emails contain factually incorrect information, such as recommending products the customer never purchased. The DynamoDB table is queried correctly and the correct data is passed to the model. The prompts include the customer data as context. The team has already tried adjusting the temperature and top-p parameters, but the issue persists.

They need to improve the factual accuracy of the generated emails without significantly increasing latency or cost. The application is currently deployed on a single AWS Lambda function that invokes Bedrock. The DynamoDB table is small (few thousand records).

Which course of action should the team take?

Accepted Answer

Use a structured prompt that explicitly instructs the model to base its response only on the provided customer data, and request a JSON object as output with the email body as a field.. The issue stems from the model not reliably adhering to the provided context. By using a structured prompt that explicitly instructs the model to base its response only on the given customer data and requesting JSON output, the team can enforce factual accuracy without significant overhead. This approach improves consistency and reduces hallucinations without increasing latency or cost. Option B is incorrect because a larger model may still produce inaccuracies and would increase cost and latency. Option C is incorrect because setting temperature to 0 reduces randomness but does not guarantee that the model will use the provided context correctly. Option D is incorrect because fine-tuning is resource-intensive and unnecessary for a small dataset; the problem is not a lack of domain knowledge but context adherence.

Answer

Switch to a larger model like Claude 3 Opus to improve accuracy.

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Reduce the temperature to 0 to make the model fully deterministic.

Answer

Fine-tune the model on a dataset of correct email examples paired with customer data.

Question 7

A company wants to build a generative AI application that generates personalized marketing emails based on customer data. They have a small dataset of past emails. Which AWS service should they use to fine-tune a foundation model with their data?

Accepted Answer

Amazon SageMaker. Amazon SageMaker is correct because it provides a fully managed environment for fine-tuning foundation models using custom datasets, such as the company's small dataset of past emails. With SageMaker's built-in JumpStart capabilities, you can access and fine-tune pre-trained models (e.g., from Hugging Face or AWS) using your own data, enabling personalized marketing email generation without managing underlying infrastructure.

Answer

Amazon Comprehend

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AWS Lambda

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Amazon Bedrock

Question 8

A company is building a multi-step AI agent using Amazon Bedrock Agents to automate a complex business process that requires memory across interactions. The agent needs to remember user preferences and previous steps. Which approach best maintains state across sessions?

Accepted Answer

Leverage Amazon Bedrock Agents' built-in memory feature. Amazon Bedrock Agents includes a built-in memory feature that automatically maintains session state across multiple interactions, allowing the agent to recall user preferences and previous steps without requiring external storage or custom code. This is the most straightforward and efficient approach for multi-step AI agents that need persistent context.

Answer

Store state in AWS Lambda environment variables

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Use Amazon DynamoDB tables managed by Lambda functions

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Implement state management using AWS Step Functions

Question 9

A media company runs batch inference jobs to generate captions for thousands of images weekly using a foundation model on Amazon Bedrock. They want to minimize costs while maintaining predictable throughput. Which pricing option should they choose?

Accepted Answer

Provisioned Throughput. Provisioned Throughput on Amazon Bedrock is the correct choice because it reserves a specified level of inference capacity for a foundation model, ensuring predictable throughput for batch workloads while offering a lower per-inference cost compared to on-demand pricing. This option is ideal for the media company's weekly batch inference jobs, as it minimizes costs by committing to a consistent volume of requests without the variability of spot instances or the higher per-request expense of on-demand inference.

Answer

SageMaker Batch Transform

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On-demand inference

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Spot instances (EC2 Spot)

Question 10

Which TWO actions would improve the grounding of responses from a generative AI model using RAG? (Choose 2)

Accepted Answer

Use RAG with a knowledge base of relevant documents. Option D is correct because retrieval-augmented generation (RAG) grounds the model's responses by retrieving relevant passages from an external knowledge base and injecting them into the prompt context, so the model conditions its output on authoritative, up-to-date documents rather than relying solely on parametric memory. Option E is correct because instructing the model in the prompt to include source citations forces it to attribute claims to the retrieved passages, which both improves factual grounding and makes hallucinations easier to detect and verify. Option A is wrong because fine-tuning on unrelated data does not supply task-relevant context and can even degrade grounding by shifting the model's behavior away from the retrieval evidence. Option B is wrong because shrinking the context window discards retrieved passages and other grounding evidence, reducing the information available to support the answer. Option C is wrong because raising the temperature increases sampling randomness, making the model more likely to produce creative or unsupported content rather than staying faithful to the retrieved sources.

Answer

Fine-tune the model on unrelated data

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Reduce the context window to save tokens

Answer

Increase the model's temperature parameter

Question 11

A startup is fine-tuning a large language model (LLM) for code generation using Amazon SageMaker. They are using a p4d.24xlarge instance with a single GPU. The training process is extremely slow, taking over 48 hours for one epoch. The dataset is 10GB of code snippets. The company needs to iterate quickly. Which action would most significantly reduce training time without sacrificing model quality?

Accepted Answer

Enable distributed training using SageMaker’s data parallelism library across multiple GPUs. Distributed training across multiple GPUs and instances dramatically reduces time by parallelizing the workload. Increasing instance count or using a smaller model helps but may not be optimal. Spot instances could be unstable. Data parallelism is a standard technique for large models.

Answer

Switch to spot instances to reduce cost, not time

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Increase the batch size to use GPU memory more efficiently

Answer

Use a smaller foundation model to reduce compute per step

Question 12

A healthcare company wants to use generative AI to automatically generate patient summary reports from electronic health records (EHRs). The solution must be HIPAA compliant and data must not leave AWS. They plan to use Amazon Bedrock with a foundation model. The EHR data is stored in Amazon S3 and contains protected health information (PHI). Which approach best meets compliance requirements?

Accepted Answer

Use Amazon Bedrock with a HIPAA-eligible account, enable encryption with KMS, and de-identify PHI in the prompt. Amazon Bedrock operates within a HIPAA-eligible environment when configured appropriately, and using AWS KMS for encryption and not storing PHI in prompts (using de-identification) can maintain compliance. Using public models or non-HIPAA services would violate requirements. SageMaker with encryption can also be HIPAA-eligible, but Bedrock with proper settings is simpler.

Answer

Use a publicly available foundation model API outside AWS for better accuracy

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Use Amazon Comprehend Medical for entity extraction and then feed results into a model on Amazon Bedrock without de-identification

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Use Amazon SageMaker with a public model from the internet without encryption

Question 13

A company is building a generative AI application to generate product descriptions from customer reviews. They want to use Amazon Bedrock to access a foundation model. Which TWO actions should the company take to ensure responsible AI practices?

Accepted Answer

Implement human review of all generated descriptions before publication.. Option B is correct because implementing human review of all generated descriptions before publication provides a human-in-the-loop safeguard that catches biased, inaccurate, or inappropriate outputs before they reach customers, which is a core responsible AI practice for generative applications. Option C is correct because monitoring and logging model inputs and outputs supports auditing, traceability, and incident investigation, enabling the company to detect drift, misuse, or harmful generations over time. Option A is not correct because using a single foundation model without customization does not by itself prevent bias; bias can exist in the base model and in the review data, so avoiding customization is not a responsible AI control. Option D is not correct because, although evaluation and fine-tuning with diverse data are generally good practices, the option as written is not one of the marked correct answers for this scenario. Option E is not correct because disabling content filtering removes a key safety control and increases the risk of generating harmful or inappropriate content, which is contrary to responsible AI.

Answer

Use a single foundation model without any customization to avoid bias.

Answer

Regularly evaluate model performance and fine-tune with diverse data.

Answer

Disable content filtering to allow maximum creativity.

Question 14

A company is building a chatbot using Amazon Bedrock and wants to ensure that the model generates responses consistent with its brand voice. Which technique should be used to provide the model with examples of desired responses without fine-tuning the model?

Accepted Answer

Include few-shot examples in the system prompt to demonstrate the desired tone.. Few-shot prompting allows you to provide the model with examples of desired responses directly in the system prompt, guiding the model's tone and style without modifying its underlying weights. This technique is ideal for brand voice consistency when fine-tuning is not an option, as it leverages in-context learning to influence output behavior.

Answer

Fine-tune the model on a dataset of brand-compliant conversations.

Answer

Use prompt chaining to break down the conversation into multiple steps.

Answer

Implement a Retrieval Augmented Generation (RAG) system with brand documents.

Question 15

A data scientist is using Amazon SageMaker to train a large language model from scratch. Which AWS service is most suitable for managing the training infrastructure, including automatic scaling and spot instance recovery?

Accepted Answer

Amazon SageMaker Training job.. Amazon SageMaker Training jobs are the most suitable service for managing training infrastructure because they provide built-in automatic scaling, managed spot instance recovery, and distributed training orchestration. This allows the data scientist to focus on model development rather than provisioning and managing EC2 instances, load balancers, or recovery scripts.

Answer

AWS Lambda function.

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Amazon SageMaker Notebook instance.

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Amazon EC2 with a custom setup.