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Amazon AIF-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Responsible AI	20%	- Implement responsible AI best practices 1. Implement appropriate guardrails for AI applications 2. Apply human oversight in AI decision-making 3. Evaluate AI outputs for quality and safety - Understand governance and compliance requirements 1. Recognize regulatory and ethical considerations 2. Describe model interpretability and explainability 3. Explain data privacy regulations affecting AI - Explain the principles of responsible AI 1. Describe strategies for bias mitigation 2. Identify potential biases in AI/ML models 3. Define fairness, transparency, and privacy in AI systems
Topic 2: AI/ML Fundamentals	24%	- Understand the AI/ML lifecycle 1. Describe model evaluation and deployment considerations 2. Identify phases of the ML lifecycle 3. Recognize data collection, preprocessing, and feature engineering stages - Explain the fundamental concepts of AI and ML 1. Identify common use cases for AI/ML 2. Explain supervised and unsupervised learning 3. Differentiate between AI, ML, and deep learning 4. Define generative AI and its key concepts - Identify foundational terminology and definitions 1. Explain training, inference, and fine-tuning 2. Describe underfitting and overfitting 3. Define common ML metrics (accuracy, precision, recall, F1 score) 4. Define models, algorithms, and parameters
Topic 3: AI Application Development	30%	- Implement AI applications using AWS services 1. Use knowledge bases for context-aware responses 2. Configure model parameters (temperature, top-p, top-k) 3. Build applications using Amazon Bedrock APIs and SDKs 4. Implement prompt templates and chain-of-thought reasoning - Evaluate and optimize AI applications 1. Implement caching and cost optimization techniques 2. Apply evaluation frameworks for AI applications 3. Identify performance bottlenecks and optimization strategies 4. Assess application outputs for relevance and accuracy - Understand foundational concepts for building AI applications 1. Explain prompts, tokens, and context windows 2. Explain function calling and tool use in AI applications 3. Describe prompt engineering techniques 4. Identify retrieval-augmented generation (RAG) concepts
Topic 4: AWS AI Services Overview	26%	- Describe Amazon Bedrock capabilities 1. Describe model invocation, prompts, and responses 2. Identify Bedrock features (agents, knowledge bases, guardrails) 3. Explain foundation models (FMs) available in Bedrock 4. Explain security, privacy, and compliance features - Identify appropriate AWS AI services for given scenarios 1. Evaluate use cases for Amazon Bedrock, SageMaker, and other AWS AI offerings 2. Determine when to use pre-trained models vs. custom models 3. Compare AWS AI services by capability and use case - Describe AWS AI services for specific use cases 1. Identify services for NLP, computer vision, and recommendations 2. Describe Amazon Polly, Rekognition, Transcribe, and Translate 3. Explain AWS HealthScribe and other domain-specific services - Describe Amazon SageMaker capabilities 1. Describe built-in algorithms and SageMaker JumpStart 2. Identify SageMaker features for ML workflows 3. Explain model training and deployment options 4. Recognize MLOps capabilities in SageMaker

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最新的 AWS Certified AI AIF-C01 免費考試真題 (Q295-Q300):

問題 #295
A company's large language model (LLM) is experiencing hallucinations.
How can the company decrease hallucinations?

A. Use data pre-processing and remove any data that causes hallucinations.
B. Use a foundation model (FM) that is trained to not hallucinate.
C. Set up Agents for Amazon Bedrock to supervise the model training.
D. Decrease the temperature inference parameter for the model.

答案：D

解題說明：
Hallucinations in large language models (LLMs) occur when the model generates outputs that are factually incorrect, irrelevant, or not grounded in the input data. To mitigate hallucinations, adjusting the model's inference parameters, particularly the temperature, is a well-documented approach in AWS AI Practitioner resources. The temperature parameter controls the randomness of the model's output. A lower temperature makes the model more deterministic, reducing the likelihood of generating creative but incorrect responses, which are often the cause of hallucinations.
Exact Extract from AWS AI Documents:
From the AWS documentation on Amazon Bedrock and LLMs:
"The temperature parameter controls the randomness of the generated text. Higher values (e.g., 0.8 or above) increase creativity but may lead to less coherent or factually incorrect outputs, while lower values (e.g., 0.2 or 0.3) make the output more focused and deterministic, reducing the likelihood of hallucinations." (Source: AWS Bedrock User Guide, Inference Parameters for Text Generation) Detailed Option A: Set up Agents for Amazon Bedrock to supervise the model training.Agents for Amazon Bedrock are used to automate tasks and integrate LLMs with external tools, not to supervise model training or directly address hallucinations. This option is incorrect as it does not align with the purpose of Agents in Bedrock.
Option B: Use data pre-processing and remove any data that causes hallucinations.While data pre-processing can improve model performance, identifying and removing specific data that causes hallucinations is impractical because hallucinations are often a result of the model's generative process rather than specific problematic data points. This approach is not directly supported by AWS documentation for addressing hallucinations.
Option C: Decrease the temperature inference parameter for the model.This is the correct approach. Lowering the temperature reduces the randomness in the model's output, making it more likely to stick to factual and contextually relevant responses. AWS documentation explicitly mentions adjusting inference parameters like temperature to control output quality and mitigate issues like hallucinations.
Option D: Use a foundation model (FM) that is trained to not hallucinate.No foundation model is explicitly trained to "not hallucinate," as hallucinations are an inherent challenge in LLMs. While some models may be fine-tuned for specific tasks to reduce hallucinations, this is not a standard feature of foundation models available on Amazon Bedrock.
Reference:
AWS Bedrock User Guide: Inference Parameters for Text Generation (https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html) AWS AI Practitioner Learning Path: Module on Large Language Models and Inference Configuration Amazon Bedrock Developer Guide: Managing Model Outputs (https://docs.aws.amazon.com/bedrock/latest/devguide/)

問題 #296
A company wants to use large language models (LLMs) with Amazon Bedrock to develop a chat interface for the company's product manuals. The manuals are stored as PDF files.
Which solution meets these requirements MOST cost-effectively?

A. Use prompt engineering to add all the PDF files as context to the user prompt when the prompt is submitted to Amazon Bedrock.
B. Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to Amazon Bedrock.
C. Use all the PDF documents to fine-tune a model with Amazon Bedrock. Use the fine-tuned model to process user prompts.
D. Upload PDF documents to an Amazon Bedrock knowledge base. Use the knowledge base to provide context when users submit prompts to Amazon Bedrock.

答案：D

問題 #297
A company needs to build its own large language model (LLM) based on only the company's private dat a. The company is concerned about the environmental effect of the training process.
Which Amazon EC2 instance type has the LEAST environmental effect when training LLMs?

A. Amazon EC2 C series
B. Amazon EC2 P series
C. Amazon EC2 Trn series
D. Amazon EC2 G series

答案：C

解題說明：
The Amazon EC2 Trn series (Trainium) instances are designed for high-performance, cost-effective machine learning training while being energy-efficient. AWS Trainium-powered instances are optimized for deep learning models and have been developed to minimize environmental impact by maximizing energy efficiency.
Option D (Correct): "Amazon EC2 Trn series": This is the correct answer because the Trn series is purpose-built for training deep learning models with lower energy consumption, which aligns with the company's concern about environmental effects.
Option A: "Amazon EC2 C series" is incorrect because it is intended for compute-intensive tasks but not specifically optimized for ML training with environmental considerations.
Option B: "Amazon EC2 G series" (Graphics Processing Unit instances) is optimized for graphics-intensive applications but does not focus on minimizing environmental impact for training.
Option C: "Amazon EC2 P series" is designed for ML training but does not offer the same level of energy efficiency as the Trn series.
AWS AI Practitioner Reference:
AWS Trainium Overview: AWS promotes Trainium instances as their most energy-efficient and cost-effective solution for ML model training.

問題 #298
A company has deployed an ML model. The company wants to provide external customers with secure access to the model through the customers' own applications.
Which solution will meet these requirements?

A. Use a custom script in the customers' application for authentication.
B. Create a secure API endpoint that customers can use.
C. Store model credentials and share them with the customers directly for authentication.
D. Embed the model directly into the customers' applications.

答案：B

解題說明：
AWS documentation recommends exposing deployed machine learning models through secure, managed API endpoints to provide controlled and scalable access for external consumers. Creating a secure API endpoint allows customers to invoke the model using standard HTTPS requests while maintaining strict security boundaries.
AWS services such as Amazon SageMaker support deploying models behind managed endpoints that integrate with AWS security features, including authentication, authorization, encryption in transit, throttling, and monitoring. By using an API-based approach, the company can ensure that external customers interact with the model without gaining direct access to the underlying infrastructure or credentials.
The other options introduce significant security risks. Sharing credentials directly violates AWS security best practices and could lead to unauthorized access. Custom authentication scripts in customer applications are difficult to standardize and maintain. Embedding the model directly into customer applications removes centralized control and makes updates and security management impractical.
AWS positions secure API endpoints as the preferred mechanism for exposing ML inference capabilities to external clients, making this the correct solution.

問題 #299
Which task represents a practical use case to apply a regression model?

A. Cluster movies based on movie ratings and viewers.
B. Suggest a genre of music for a listener from a list of genres.
C. Create a picture that shows a specific object.
D. Use historical data to predict future temperatures in a specific city.

答案：D

解題說明：
Comprehensive and Detailed
Regression predicts continuous numerical values (e.g., stock prices, temperatures).
A is classification (genre selection).
B is clustering.
D is generative AI/computer vision.
Reference:
AWS ML Glossary - Regression

問題 #300
......

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