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| Section | Weight | Objectives |
|---|---|---|
| Applications of Foundation Models | 28% | - Recognize tools for building and deploying generative AI solutions - Explain integration of foundation models into applications - Describe use cases for text, image, video, and code generation - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan |
| Guidelines for Responsible AI | 14% | - Describe ethical and societal impacts of AI - Explain bias detection and reduction - Identify risks and mitigation strategies for AI systems - Define responsible AI principles: fairness, transparency, privacy, safety |
| Fundamentals of Generative AI | 24% | - Define generative AI and foundation models - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) - Explain capabilities and use cases of generative AI - Differentiate between generative AI and traditional ML |
| Fundamentals of AI and ML | 20% | - Define artificial intelligence (AI), machine learning (ML), and deep learning - Describe common ML workflows and lifecycle - Identify types of ML: supervised, unsupervised, reinforcement learning - Recognize key concepts: data, models, training, inference, evaluation |
| Security, Compliance, and Governance for AI Solutions | 14% | - Data protection and privacy in AI workflows - Compliance requirements and regulations - Security controls for AI data and models - Governance frameworks for AI lifecycle |
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NEW QUESTION # 190
A company wants to use Amazon Q Business for its data. The company needs to ensure the security and privacy of the data.
Which combination of steps will meet these requirements? (Select TWO.)
Answer: D,E
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
To secure data in Amazon Q Business, AWS recommends:
* AWS KMS (A) to encrypt data at rest and manage encryption keys.
* AWS IAM (E) to control authentication and authorization for users and services.
Together, these services ensure:
* Strong access control
* Data confidentiality
* Compliance with enterprise security requirements
Why the other options are incorrect:
* Cross-account access (B) does not inherently ensure privacy.
* Amazon Inspector (C) is for vulnerability scanning, not authentication.
* Public access (D) violates security best practices.
AWS AI document references:
* Amazon Q Business Security Overview
* Data Protection with AWS KMS
* Access Control with AWS IAM
NEW QUESTION # 191
A company used Amazon Bedrock to build an AI assistant. The AI assistant received a user prompt that instructed the assistant to provide the guidelines that the company used to configure the assistant. The prompt also instructed the assistant to display the full text of the initial setup. The AI assistant revealed its system prompt in the response.
Which type of prompt attack has occurred?
Answer: D
Explanation:
The attack is specifically prompt leakage because the user's objective is to obtain confidential system-level instructions or configuration information from the AI application. AWS defines prompt leakage as user prompts designed to "extract or reveal the system prompt, developer instructions, or other confidential configuration details." The scenario precisely matches this definition. The user asks the assistant to disclose the guidelines used to configure it and to reproduce the complete initial setup. The assistant then reveals its system prompt. The defining outcome is therefore unauthorized disclosure of protected prompt instructions.
Prompt leakage is related to the broader category of prompt attacks, and a leakage attempt can involve prompt-injection techniques. However, when AWS asks for the specific type of attack and the attacker is intentionally trying to expose the hidden system prompt or developer instructions, prompt leakage is the more precise classification.
A jailbreak generally attempts to bypass model safeguards or restrictions so the model performs behavior that would normally be prohibited. Although jailbreaks and prompt leakage can overlap, the core purpose in this scenario is disclosure of system configuration rather than simply bypassing a safety policy.
Prompt injection is the broader technique of introducing malicious instructions designed to override or manipulate existing instructions. For example, an attacker might tell the application to ignore previous instructions. AWS includes jailbreaks, prompt injections, and prompt leakages within its prompt-attack filtering capabilities.
A denied topic violation occurs when content concerns a topic explicitly configured as prohibited in Amazon Bedrock Guardrails. It does not describe system-prompt disclosure.
AWS recommends protecting generative AI applications by using Bedrock Guardrails prompt-attack detection, clearly defined system instructions, controlled access to tools and resources, input evaluation, output validation, and security testing.
NEW QUESTION # 192
An ecommerce company is deploying a chatbot. The chatbot will give users the ability to ask questions about the company's products and receive details on users' orders. The company must implement safeguards for the chatbot to filter harmful content from the input prompts and chatbot responses.
Which AWS feature or resource meets these requirements?
Answer: A
Explanation:
The ecommerce company is deploying a chatbot that needs safeguards to filter harmful content from input prompts and responses. Amazon Bedrock Guardrails provide mechanisms to ensure responsible AI usage by filtering harmful content, such as hate speech, violence, or misinformation, making it the appropriate feature for this requirement.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Amazon Bedrock Guardrails enable developers to implement safeguards for generative AI applications, such as chatbots, by filtering harmful content in input prompts and model responses. Guardrails include content filters, word filters, and denied topics to ensure safe and responsible outputs." (Source: AWS Bedrock User Guide, Guardrails for Responsible AI) Detailed Explanation:
* Option A: Amazon Bedrock GuardrailsThis is the correct answer. Amazon Bedrock Guardrails are specifically designed to filter harmful content from chatbot inputs and responses, ensuring safe interactions for users.
* Option B: Amazon Bedrock AgentsAmazon Bedrock Agents are used to automate tasks and integrate with external tools, not to filter harmful content. This option does not meet the requirement.
* Option C: Amazon Bedrock inference APIsAmazon Bedrock inference APIs allow users to invoke foundation models for generating responses, but they do not provide built-in content filtering mechanisms.
* Option D: Amazon Bedrock custom modelsCustom models on Amazon Bedrock allow users to fine- tune models, but they do not inherently include safeguards for filtering harmful content unless explicitly implemented.
References:
AWS Bedrock User Guide: Guardrails for Responsible AI (https://docs.aws.amazon.com/bedrock/latest
/userguide/guardrails.html)
AWS AI Practitioner Learning Path: Module on Responsible AI and Model Safety Amazon Bedrock Developer Guide: Building Safe AI Applications (https://aws.amazon.com/bedrock/)
NEW QUESTION # 193
What is the benefit of fine-tuning a foundation model (FM)?
Answer: B
Explanation:
Comprehensive and Detailed Explanation from AWS AI Documents:
Fine-tuning a foundation model means taking a pre-trained large model and continuing its training on domain- specific or task-specific data to specialize it for a particular use case. Fine-tuning does not retrain the FM from scratch (which would be costly and time-consuming). Instead, it improves model accuracy, relevance, and contextual adaptation for the intended application (e.g., legal, healthcare, customer support).
From AWS Docs:
* "With Amazon Bedrock, you can fine-tune foundation models on your own data to specialize them for your unique use cases."
* "Fine-tuning a foundation model adapts it to a specific task by training on smaller sets of labeled data relevant to the problem domain."
# Reference:
AWS Documentation - Fine-tuning foundation models in Amazon Bedrock
NEW QUESTION # 194
A bank has fine-tuned a large language model (LLM) to expedite the loan approval process. During an external audit of the model, the company discovered that the model was approving loans at a faster pace for a specific demographic than for other demographics.
How should the bank fix this issue MOST cost-effectively?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The best practice for mitigating bias in AI/ML models, according to AWS and responsible AI frameworks, is to ensure that the training data is representative and diverse. If a model demonstrates bias (such as favoring a particular demographic), the recommended, cost-effective approach is to collect additional data from underrepresented groups and retrain (fine-tune) the model with the improved dataset.
A . Include more diverse training data. Fine-tune the model again by using the new data:
"The most effective method to reduce model bias is to curate and include diverse, representative training data, then retrain or fine-tune the model." (Reference: AWS Responsible AI, SageMaker Clarify Bias Mitigation) B (RAG) is unrelated to model fairness or bias mitigation; it's for grounding LLMs with external knowledge.
C (AWS Trusted Advisor) is for AWS resource optimization/security-not for ML model bias detection or mitigation.
D (Pre-train a new LLM) would be extremely costly and is unnecessary; fine-tuning with better data is much more efficient.
Reference:
Responsible AI on AWS
Amazon SageMaker Clarify: Detecting and Mitigating Bias
AWS Certified AI Practitioner Exam Guide
NEW QUESTION # 195
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