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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Security, Compliance, and Governance for AI Solutions | 14% | - Data protection and privacy in AI workflows - Security controls for AI data and models - Compliance requirements and regulations - Governance frameworks for AI lifecycle |
| Topic 2: Applications of Foundation Models | 28% | - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan - Describe use cases for text, image, video, and code generation - Explain integration of foundation models into applications - Recognize tools for building and deploying generative AI solutions |
| Topic 3: Guidelines for Responsible AI | 14% | - Describe ethical and societal impacts of AI - Define responsible AI principles: fairness, transparency, privacy, safety - Identify risks and mitigation strategies for AI systems - Explain bias detection and reduction |
| Topic 4: Fundamentals of Generative AI | 24% | - Define generative AI and foundation models - Explain capabilities and use cases of generative AI - Differentiate between generative AI and traditional ML - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) |
| Topic 5: Fundamentals of AI and ML | 20% | - Identify types of ML: supervised, unsupervised, reinforcement learning - Define artificial intelligence (AI), machine learning (ML), and deep learning - Describe common ML workflows and lifecycle - Recognize key concepts: data, models, training, inference, evaluation |
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NEW QUESTION # 125
A financial company uses a generative AI model to assign credit limits to new customers. The company wants to make the decision-making process of the model more transparent to its customers.
Answer: C
Explanation:
The correct answer is B because explainable AI (XAI) provides transparency into how models reach specific decisions. According to AWS documentation, techniques such as SHAP values (SHapley Additive exPlanations) or LIME can identify which input features (e.g., income, debt ratio, or credit history) most influenced a model's prediction. This helps financial institutions comply with fairness and transparency requirements under regulatory frameworks like the Equal Credit Opportunity Act. AWS SageMaker Clarify is a built-in service that offers explainability reports and bias detection to enhance trust. Rule-based systems and UIs alone do not satisfy transparency standards, and accuracy improvements do not replace explainability. By implementing explainable AI, customers can understand and trust credit limit decisions, reducing bias concerns and ensuring compliance.
Referenced AWS AI/ML Documents and Study Guides:
Amazon SageMaker Clarify Documentation - Explainability and Feature Attribution AWS Responsible AI Practices - Transparency and Accountability
NEW QUESTION # 126
A company is using Amazon Bedrock to build a customer-facing AI assistant. The company wants to filter out harmful and offensive content. The company also wants to block customer personally identifiable information (PII) from appearing in the AI assistant ' s responses.
Which AWS solution will meet these requirements with the LEAST operational overhead?
Answer: C
Explanation:
Amazon Bedrock Guardrails provides managed safety and privacy controls for generative AI applications.
The service supports content filters that identify potentially harmful or offensive model inputs and outputs.
These filters can detect categories such as hate, insults, violence, and misconduct.
Amazon Bedrock Guardrails also provides sensitive information filters that identify personally identifiable information, including names, addresses, telephone numbers, and other supported identifiers.
Organizations can configure these filters to block content or mask detected sensitive information.
Amazon Macie identifies sensitive information primarily within Amazon S3. Amazon Comprehend provides natural language processing capabilities, while Amazon Inspector identifies software vulnerabilities.
Neither alternative provides the same integrated generative AI safety controls.
Therefore, Amazon Bedrock Guardrails satisfies both requirements while minimizing operational overhead.
NEW QUESTION # 127
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: B
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 # 128
An ecommerce company wants to improve search engine recommendations by customizing the results for each user of the company's ecommerce platform. Which AWS service meets these requirements?
Answer: A
Explanation:
The ecommerce company wants to improve search engine recommendations by customizing results for each user. Amazon Personalize is a machine learning service that enables personalized recommendations, tailoring search results or product suggestions based on individual user behavior and preferences, making it the best fit for this requirement.
Exact Extract from AWS AI Documents:
From the Amazon Personalize Developer Guide:
"Amazon Personalize enables developers to build applications with personalized recommendations, such as customized search results or product suggestions, by analyzing user behavior and preferences to deliver tailored experiences." (Source: Amazon Personalize Developer Guide, Introduction to Amazon Personalize) Detailed Explanation:
* Option A: Amazon PersonalizeThis is the correct answer. Amazon Personalize specializes in creating personalized recommendations, ideal for customizing search results for each user on an ecommerce platform.
* Option B: Amazon KendraAmazon Kendra is an intelligent search service for enterprise data, focusing on retrieving relevant documents or answers, not on personalizing search results for individual users.
* Option C: Amazon RekognitionAmazon Rekognition is for image and video analysis, such as object detection or facial recognition, and is unrelated to search engine recommendations.
* Option D: Amazon TranscribeAmazon Transcribe converts speech to text, which is not relevant for improving search engine recommendations.
References:
Amazon Personalize Developer Guide: Introduction to Amazon Personalize (https://docs.aws.amazon.com
/personalize/latest/dg/what-is-personalize.html)
AWS AI Practitioner Learning Path: Module on Recommendation Systems
AWS Documentation: Personalization with Amazon Personalize (https://aws.amazon.com/personalize/)
NEW QUESTION # 129
A company is using an Amazon Nova Canvas model to generate images. The model generates images successfully.
The company needs to prevent the model from including specific items in the generated images.
Which solution will meet this requirement?
Answer: B
NEW QUESTION # 130
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