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| Section | Weight | Objectives |
|---|---|---|
| Fundamentals of Generative AI | 24% | - Differentiate between generative AI and traditional ML - Explain capabilities and use cases of generative AI - Define generative AI and foundation models - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) |
| Guidelines for Responsible AI | 14% | - Define responsible AI principles: fairness, transparency, privacy, safety - Describe ethical and societal impacts of AI - Identify risks and mitigation strategies for AI systems - Explain bias detection and reduction |
| Fundamentals of AI and ML | 20% | - Describe common ML workflows and lifecycle - Define artificial intelligence (AI), machine learning (ML), and deep learning - Identify types of ML: supervised, unsupervised, reinforcement learning - Recognize key concepts: data, models, training, inference, evaluation |
| Applications of Foundation Models | 28% | - 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 - Recognize tools for building and deploying generative AI solutions |
| 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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問題 #326
A company is using a generative AI model to develop a digital assistant. The model's responses occasionally include undesirable and potentially harmful content. Select the correct Amazon Bedrock filter policy from the following list for each mitigation action. Each filter policy should be selected one time. (Select FOUR.)
* Content filters
* Contextual grounding check
* Denied topics
* Word filters
答案：
解題說明：
Reference:
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: Configuring Guardrails (https://aws.amazon.com/bedrock/)
問題 #327
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to know how much information can fit into one prompt.
Which consideration will inform the company's decision?
答案：A
解題說明：
The context window determines how much information can fit into a single prompt when using a large language model (LLM) like those on Amazon Bedrock.
* Context Window:
* The context window is the maximum amount of text (measured in tokens) that a language model can process in a single pass.
* For LLM applications, the size of the context window limits how much input data, such as text for sentiment analysis, can be fed into the model at once.
* Why Option B is Correct:
* Determines Prompt Size: The context window size directly informs how much information (e.
g., words or sentences) can fit in one prompt.
* Model Capacity: The larger the context window, the more information the model can consider for generating outputs.
* Why Other Options are Incorrect:
* A. Temperature: Controls randomness in model outputs but does not affect the prompt size.
* C. Batch size: Refers to the number of training samples processed in one iteration, not the amount of information in a prompt.
* D. Model size: Refers to the number of parameters in the model, not the input size for a single prompt.
問題 #328
An accounting firm wants to implement a large language model (LLM) to automate document processing. The firm must proceed responsibly to avoid potential harms.
What should the firm do when developing and deploying the LLM? (Select TWO.)
答案：C,E
解題說明：
I'll continue with more questions. Stay tuned!
問題 #329
A company wants to apply responsible AI dimensions to understand and evaluate its generative AI (GenAI) system outputs.
Which responsible AI dimension applies to the company's use case?
答案：B
解題說明：
The correct responsible AI dimension is Explainability. AWS defines explainability in its Responsible AI framework as "Having mechanisms to understand system behavior." The AWS Generative AI Lens similarly characterizes explainability as "Understanding and evaluating system outputs." The wording of the question directly matches this AWS definition. The organization wants to understand and evaluate outputs produced by its generative AI system. Explainability concerns the mechanisms, methods, and information that allow developers, operators, auditors, customers, and other stakeholders to understand why an AI system produces particular behavior or outputs.
Explainability can involve examining influential features, reasoning about model behavior, analyzing response patterns, providing evidence or supporting information, and using interpretability techniques appropriate to the model architecture. For generative AI systems, the goal is not necessarily to expose every internal mathematical operation of a foundation model. Instead, organizations establish mechanisms that make system behavior sufficiently understandable for the intended use case and associated risks.
Controllability is different. AWS defines controllability as having mechanisms to monitor and steer AI system behavior. Examples include configuring guardrails, adjusting system behavior, establishing user controls, or restricting what an AI application can do.
Safety focuses on blocking or reducing harmful outputs and preventing misuse. It addresses risks where AI behavior might cause harm to users or other stakeholders.
Fairness concerns the different effects that an AI system can have on groups of stakeholders and seeks to detect or mitigate unwanted disparities or bias.
AWS recommends identifying responsible AI risks and selecting appropriate evaluation metrics for the relevant dimensions throughout development and deployment. Explainability is specifically measured when teams need to assess whether stakeholders can understand system behavior.
Because the requirement explicitly centers on understanding and evaluating AI-generated outputs, A. Explainability is the exact AWS-aligned answer.
問題 #330
A retail company is tagging its product inventory. A tag is automatically assigned to each product based on the product description. The company created one product category by using a large language model (LLM) on Amazon Bedrock in few-shot learning mode.
The company collected a labeled dataset and wants to scale the solution to all product categories.
Which solution meets these requirements?
答案：B
解題說明：
When you have a labeled dataset and need to scale a generative AI solution for more complex or diverse product categories, fine-tuning the foundation model with your dataset is the best approach for consistent, accurate tagging.
D is correct:
"Fine-tuning a foundation model with your labeled data allows the model to generalize to new categories and improve tagging accuracy for your inventory." (Reference: Amazon Bedrock Fine-Tuning, AWS Generative AI) A (zero-shot) and B (prompt templates) do not leverage the labeled data or scale as accurately.
C (continued pre-training) uses unlabeled data, not labeled.
問題 #331
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