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Amazon AIF-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI/ML Fundamentals24%- Identify foundational terminology and definitions
  • 1. Describe underfitting and overfitting
  • 2. Define models, algorithms, and parameters
  • 3. Explain training, inference, and fine-tuning
  • 4. Define common ML metrics (accuracy, precision, recall, F1 score)
- Explain the fundamental concepts of AI and ML
  • 1. Define generative AI and its key concepts
  • 2. Explain supervised and unsupervised learning
  • 3. Identify common use cases for AI/ML
  • 4. Differentiate between AI, ML, and deep learning
- Understand the AI/ML lifecycle
  • 1. Recognize data collection, preprocessing, and feature engineering stages
  • 2. Identify phases of the ML lifecycle
  • 3. Describe model evaluation and deployment considerations
Topic 2: Responsible AI20%- Understand governance and compliance requirements
  • 1. Recognize regulatory and ethical considerations
  • 2. Explain data privacy regulations affecting AI
  • 3. Describe model interpretability and explainability
- Explain the principles of responsible AI
  • 1. Define fairness, transparency, and privacy in AI systems
  • 2. Describe strategies for bias mitigation
  • 3. Identify potential biases in AI/ML models
- Implement responsible AI best practices
  • 1. Implement appropriate guardrails for AI applications
  • 2. Evaluate AI outputs for quality and safety
  • 3. Apply human oversight in AI decision-making
Topic 3: AWS AI Services Overview26%- Identify appropriate AWS AI services for given scenarios
  • 1. Compare AWS AI services by capability and use case
  • 2. Evaluate use cases for Amazon Bedrock, SageMaker, and other AWS AI offerings
  • 3. Determine when to use pre-trained models vs. custom models
- Describe AWS AI services for specific use cases
  • 1. Describe Amazon Polly, Rekognition, Transcribe, and Translate
  • 2. Explain AWS HealthScribe and other domain-specific services
  • 3. Identify services for NLP, computer vision, and recommendations
- Describe Amazon Bedrock capabilities
  • 1. Explain security, privacy, and compliance features
  • 2. Explain foundation models (FMs) available in Bedrock
  • 3. Describe model invocation, prompts, and responses
  • 4. Identify Bedrock features (agents, knowledge bases, guardrails)
- Describe Amazon SageMaker capabilities
  • 1. Describe built-in algorithms and SageMaker JumpStart
  • 2. Identify SageMaker features for ML workflows
  • 3. Recognize MLOps capabilities in SageMaker
  • 4. Explain model training and deployment options
Topic 4: AI Application Development30%- Understand foundational concepts for building AI applications
  • 1. Explain prompts, tokens, and context windows
  • 2. Identify retrieval-augmented generation (RAG) concepts
  • 3. Explain function calling and tool use in AI applications
  • 4. Describe prompt engineering techniques
- Evaluate and optimize AI applications
  • 1. Implement caching and cost optimization techniques
  • 2. Identify performance bottlenecks and optimization strategies
  • 3. Assess application outputs for relevance and accuracy
  • 4. Apply evaluation frameworks for AI applications
- Implement AI applications using AWS services
  • 1. Implement prompt templates and chain-of-thought reasoning
  • 2. Configure model parameters (temperature, top-p, top-k)
  • 3. Use knowledge bases for context-aware responses
  • 4. Build applications using Amazon Bedrock APIs and SDKs

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Amazon AWS Certified AI Practitioner Sample Questions (Q266-Q271):

NEW QUESTION # 266
A company wants more customized responses to its generative AI models ' prompts.
Select the correct customization methodology from the following list for each use case. Each use case should be selected one time. (Select THREE.)
* Continued pre-training
* Data augmentation
* Model fine-tuning

Answer:

Explanation:

Model fine-tuning adapts a pre-trained model to a specific domain or task using labeled data. This is the preferred approach when you want highly customized behavior for a particular application or subject area.
(Reference: Amazon Bedrock Fine-Tuning)
Data augmentation is used to artificially increase the size of a labeled dataset, usually by transforming or generating variations of the original data. This helps improve model generalization when labeled data is limited.
(Reference: AWS Data Preparation Techniques, AWS AI Practitioner Guide) Continued pre-training (also called domain-adaptive pre-training) further trains a foundation model on large amounts of unlabeled data from a specific domain, improving the model's language understanding or generation for that domain.
(Reference: Amazon Bedrock Customization Options)


NEW QUESTION # 267
An AI practitioner is using Amazon Bedrock Prompt Management to create a reusable prompt. The prompt must be able to interact with external services by calling an external API. Which solution will meet this requirement?

Answer: D

Explanation:
The correct answer is B because Amazon Bedrock Prompt Management supports tool use via tools configuration, which enables a prompt to define tools that can invoke external APIs or services.
According to the AWS documentation:
"You can use the tools configuration in Amazon Bedrock to specify external APIs that a foundation model can call during inference. This enables the model to interact with external services, such as invoking functions, retrieving real-time data, or executing workflows." The tools configuration allows a prompt to describe which external functions (APIs) are available, their parameters, and how they should be invoked, similar to OpenAI's function calling or tool use pattern.


NEW QUESTION # 268
A company wants to implement a large language model (LLM)-based chatbot to provide customer service agents with real-time contextual responses to customers' inquiries. The company will use the company's policies as the knowledge base.

Answer: C

Explanation:
* Retraining or pre-training is costly and unnecessary for just using company policies.
* Fine-tuning adapts models but is inefficient for frequently changing company documents.
* Retrieval-Augmented Generation (RAG) is the best approach - it retrieves relevant policy documents from a knowledge base and feeds them into the model context in real time, ensuring accurate and up-to-date responses.
# Reference:
AWS Documentation - RAG with Amazon Bedrock


NEW QUESTION # 269
Which type of ML technique provides the MOST explainability?

Answer: B

Explanation:
The most explainable model in machine learning is Linear regression. It provides clear mathematical relationships between input features and predicted outcomes, making it highly transparent. According to AWS documentation and Responsible AI best practices, linear regression models allow users to see the exact weight or coefficient assigned to each feature. This makes it easy to explain model decisions to non-technical stakeholders and is especially important in regulated industries like finance and healthcare. Support vector machines, random cut forests, and neural networks are more complex and often operate as black boxes with non-linear transformations that require additional explainability tools like SHAP or LIME. AWS recommends starting with simpler, interpretable models when transparency is a requirement.
Referenced AWS AI/ML Documents and Study Guides:
AWS Responsible AI Whitepaper - Model Transparency and Explainability
Amazon SageMaker Documentation - Interpretable Models


NEW QUESTION # 270
A hospital developed an AI system to provide personalized treatment recommendations for patients. The AI system must provide the rationale behind the recommendations and make the insights accessible to doctors and patients.
Which human-centered design principle does this scenario present?

Answer: B

Explanation:
Explainability refers to the ability of an AI system to make its decision-making process clear and understandable to humans.
* A is correct:
"Explainability is crucial for human-centered AI, especially in healthcare, to ensure that doctors and patients understand the rationale behind AI-driven recommendations." (Reference: AWS Responsible AI)
* B relates to protecting data, not explanations.
* C is about treating groups equally.
* D is about managing data lifecycle, not providing rationales.


NEW QUESTION # 271
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