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Amazon AIF-C01 Exam Syllabus Topics:

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

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Amazon AWS Certified AI Practitioner Sample Questions (Q14-Q19):

NEW QUESTION # 14
A company has petabytes of unlabeled customer data to use for an advertisement campaign. The company wants to classify its customers into tiers to advertise and promote the company's products.
Which methodology should the company use to meet these requirements?

A. Reinforcement learning
B. Unsupervised learning
C. Reinforcement learning from human feedback (RLHF)
D. Supervised learning

Answer: B

NEW QUESTION # 15
Which approach provides human-in-the-loop improvement of foundation models (FMs) throughout the ML lifecycle?

A. Implementing reinforcement learning algorithms that automatically adjust model parameters based on predefined success metrics without human oversight
B. Incorporating continuous human feedback across model development, training, and deployment phases and using performance evaluation to improve model accuracy
C. Using automated testing scripts to validate model outputs and implementing self-correction mechanisms without human intervention
D. Collecting human feedback during only the initial training phase and relying only on automated metrics for subsequent model iterations

Answer: B

Explanation:
The verified answer is C. Incorporating continuous human feedback across model development, training, and deployment phases and using performance evaluation to improve model accuracy. The question asks for human-in-the-loop improvement throughout the ML lifecycle, not a one-time review and not fully automated correction. AWS AI Practitioner guidance describes the foundation model lifecycle as including data selection, model selection, pre-training, fine-tuning, evaluation, deployment, and feedback. Because feedback is part of the lifecycle, human review should not be limited to the first training stage only.
AWS generative AI lifecycle guidance also emphasizes continuous improvement and feedback loops. It explains that operationalizing a generative AI application is not a one-time event, but an ongoing process driven by real-world usage, feedback systems, automated feedback loops, direct user feedback mechanisms, and strategic human-in-the-loop interventions. This supports option C because it combines human feedback across phases with performance evaluation to improve model behavior and accuracy over time.
Option A is incorrect because it removes human intervention. Automated testing is valuable, but it is not human-in-the-loop. Option B is incorrect because collecting human feedback only during initial training is too narrow. It ignores post-training evaluation, deployment monitoring, user feedback, drift, and iterative improvement. Option D is incorrect because it relies on automated reinforcement learning and predefined metrics without human oversight. That again contradicts the "human-in-the-loop" requirement.
The strongest clue is the phrase "throughout the ML lifecycle." A correct human-in-the-loop strategy must include human review, human feedback, and evaluation during development, training, deployment, and ongoing improvement. Therefore, option C is the only answer that satisfies both parts of the requirement:
continuous human involvement and lifecycle-based model improvement.

NEW QUESTION # 16
A company wants to build an ML application.
Select and order the correct steps from the following list to develop a well-architected ML workload. Each step should be selected one time. (Select and order FOUR.)
* Deploy model
* Develop model
* Monitor model
* Define business goal and frame ML problem

Answer:

Explanation:

NEW QUESTION # 17
A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality.
Which action must the company take to use the custom model through Amazon Bedrock?

A. Deploy the custom model in an Amazon SageMaker endpoint for real-time inference.
B. Grant access to the custom model in Amazon Bedrock.
C. Purchase Provisioned Throughput for the custom model.
D. Register the model with the Amazon SageMaker Model Registry.

Answer: A

Explanation:
To use a custom model that has been trained to improve summarization quality, the company must deploy the model on an Amazon SageMaker endpoint. This allows the model to be used for real-time inference through Amazon Bedrock or other AWS services. By deploying the model in SageMaker, the custom model can be accessed programmatically via API calls, enabling integration with Amazon Bedrock.
Option B (Correct): "Deploy the custom model in an Amazon SageMaker endpoint for real-time inference":
This is the correct answer because deploying the model on SageMaker enables it to serve real-time predictions and be integrated with Amazon Bedrock.
Option A: "Purchase Provisioned Throughput for the custom model" is incorrect because provisioned throughput is related to database or storage services, not model deployment.
Option C: "Register the model with the Amazon SageMaker Model Registry" is incorrect because while the model registry helps with model management, it does not make the model accessible for real-time inference.
Option D: "Grant access to the custom model in Amazon Bedrock" is incorrect because Bedrock does not directly manage custom model access; it relies on deployed endpoints like those in SageMaker.
AWS AI Practitioner References:
Amazon SageMaker Endpoints: AWS recommends deploying models to SageMaker endpoints to use them for real-time inference in various applications.

NEW QUESTION # 18
What is the benefit of fine-tuning a foundation model (FM)?

A. Fine-tuning keeps the FM's knowledge up to date by pre-training the FM on more recent data.
B. Fine-tuning improves the performance of the FM on a specific task by further training the FM on new labeled data.
C. Fine-tuning reduces the FM's size and complexity and enables slower inference.
D. Fine-tuning uses specific training data to retrain the FM from scratch to adapt to a specific use case.

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 # 19
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