AIF-C01 Fundamentals of Generative AI Practice Test 3 — 15 Questions

Question 1

A company is deploying a customer-facing chatbot using Amazon Bedrock. They want to reduce the risk of generating biased or harmful responses. Which TWO measures should they implement? (Choose 2.)

Accepted Answer

Implement a human-in-the-loop review for sensitive replies. Implementing a human-in-the-loop review for sensitive replies allows human reviewers to assess and approve or reject model outputs before they reach the customer. This directly mitigates the risk of biased or harmful responses by adding a layer of human judgment, especially for edge cases or high-stakes interactions where the model's guardrails might not be sufficient.

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Train the model exclusively on historical customer conversations

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Set the temperature parameter to 1.5

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Disable logging to improve performance

Question 2

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.

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High temperature, low top_p

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Low temperature, high top_p

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Increase the number of steps and reduce the number of samples

Question 3

Which AWS service provides a fully managed experience for building generative AI applications with a variety of foundation models through a unified API?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is the correct answer because it is a fully managed AWS service that provides access to a variety of foundation models (FMs) from providers like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single, unified API. This allows developers to build and scale generative AI applications without managing underlying infrastructure or model hosting, directly addressing the requirement for a fully managed experience with diverse FMs.

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AWS Lambda

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Amazon SageMaker

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Amazon Rekognition

Question 4

A company is using Amazon Bedrock to generate marketing copy. They want to ensure the model's responses are factually accurate and grounded in their proprietary knowledge base. Which feature should they use?

Accepted Answer

Retrieval Augmented Generation (RAG). Retrieval Augmented Generation (RAG) is the correct choice because it retrieves relevant documents from the company's proprietary knowledge base and provides them as context to the foundation model at inference time. This grounds the model's responses in factual, up-to-date information without modifying the underlying model weights, ensuring accuracy and reducing hallucinations.

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Model customization

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Fine-tuning

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Prompt engineering

Question 5

A data scientist wants to fine-tune a foundation model on a specific domain dataset using Amazon SageMaker. Which built-in SageMaker feature can simplify the training process?

Accepted Answer

SageMaker JumpStart. SageMaker JumpStart provides pre-trained foundation models and built-in training scripts that simplify fine-tuning on custom datasets. It handles the underlying infrastructure, hyperparameter configurations, and model deployment, allowing the data scientist to focus on the domain-specific data rather than writing custom training loops or managing SageMaker training jobs manually.

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SageMaker Neo

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SageMaker Canvas

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SageMaker Ground Truth

Question 6

A company is using Amazon Bedrock to summarize long documents. They notice that the summary sometimes omits key details. What is the most likely cause?

Accepted Answer

The model's context window is too small. When summarizing long documents with Amazon Bedrock, the model's context window determines the maximum amount of text it can process at once. If the document exceeds this limit, the model truncates or ignores portions, leading to omitted key details. This is the most likely cause because summarization requires the model to attend to the entire input, and a small context window directly prevents full coverage.

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The model is overfitted

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The prompt lacks examples

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The temperature parameter is too high

Question 7

An enterprise wants to ensure that generative AI applications built on AWS comply with data privacy regulations. They need to prevent the model from using customer data in future training. Which feature of Amazon Bedrock should they enable?

Accepted Answer

Opt-out of model improvement. Amazon Bedrock's opt-out of model improvement feature allows customers to prevent AWS from using their data (including prompts, completions, and associated metadata) for model training or service improvement. This is essential for compliance with data privacy regulations like GDPR or CCPA, as it ensures customer data is not retained or used beyond the immediate inference request.

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Policy-based data governance

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Data encryption at rest

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Model customization with customer data

Question 8

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 9

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. Model quantization reduces the precision of the model's weights (e.g., from FP32 to INT8), which decreases memory footprint and computational requirements, leading to lower latency per inference. This is the most effective optimization for real-time code completion because it directly reduces the time to generate each token without requiring additional infrastructure changes.

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Increase batch size

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Switch to a larger instance type

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Increase instance count with Auto Scaling

Question 10

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. BLEU score is a standard metric for evaluating the quality of text generated by generative AI models, particularly in tasks like machine translation. It measures the overlap of n-grams between the generated output and one or more reference texts, providing a quantitative assessment of fluency and accuracy.

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Response time

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Model size

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CPU utilization

Question 11

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

Accepted Answer

Context window length. Context window length (A) is critical because it determines the maximum number of tokens the model can process in a single input-output sequence. For text generation tasks like long-form content creation or document summarization, a larger context window allows the model to maintain coherence over extended passages, directly impacting output quality and relevance.

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Inference latency

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AWS Region availability

Question 12

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 13

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. Titan Text Lite is the correct choice because it is designed for text generation tasks and is explicitly positioned as the lowest-cost option among Amazon's Titan text generation models. Unlike Titan Embed Text, which only produces embeddings and cannot generate text, Titan Text Lite offers a cost-efficient solution for generating text while meeting the requirement of lowest cost.

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Titan Embed Text

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Titan Text Express

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Need more information

Question 14

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.

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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 15

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.. Few-shot prompt engineering provides the model with explicit examples of correct code and formatting instructions, guiding it to generate syntactically valid code without modifying the underlying model. This approach leverages in-context learning to improve output quality by conditioning the model on desired patterns, which is more effective than parameter adjustments alone for addressing compilation errors.

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Reduce the temperature parameter to 0 for deterministic output.

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

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