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Amazon AIF-C01 Exam Syllabus Topics:

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

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Amazon AWS Certified AI Practitioner Sample Questions (Q141-Q146):

NEW QUESTION # 141
A company is using Amazon Bedrock to develop an AI assistant. The AI assistant will respond to customer questions about the company's products. The company conducts initial tests of the AI assistant. The company finds that the AI assistant's responses do not represent the company well and might damage customer perception.
The company needs a prompt engineering technique to improve the AI assistant's responses so that the responses better represent the company.
Which solution will meet this requirement?

A. Use zero-shot prompting.
B. Use Retrieval Augmented Generation (RAG).
C. Provide a persona and tone in the prompt.
D. Use chain-of-thought (CoT) prompting.

Answer: C

Explanation:
AWS documentation for Amazon Bedrock explains that prompt engineering is a primary mechanism for controlling the behavior, tone, and style of foundation model outputs. Providing a persona and tone within the prompt allows organizations to align model responses with brand voice, customer expectations, and business values.
In this use case, the AI assistant's responses risk damaging customer perception, which indicates a mismatch in tone, style, or personality, rather than a lack of knowledge. AWS explicitly states that prompts can include role definitions, communication style, formality level, and behavioral constraints to guide the model's outputs. By defining a persona-such as "a professional, friendly company representative"-the model consistently generates responses that better represent the company.
Other options are less appropriate. Zero-shot prompting provides no additional guidance beyond the task itself and does not influence tone. Chain-of-thought prompting is designed to improve reasoning transparency, not brand alignment. Retrieval Augmented Generation (RAG) enhances factual accuracy by injecting external knowledge sources, but it does not inherently control tone or personality.
AWS highlights persona-based prompting as a best practice when building customer-facing generative AI applications, particularly chatbots and assistants. This approach improves consistency, reduces reputational risk, and ensures outputs align with organizational communication standards. Therefore, providing a persona and tone in the prompt is the most effective solution.

NEW QUESTION # 142
A bank is building a chatbot to answer customer questions about opening a bank account. The chatbot will use public bank documents to generate responses. The company will use Amazon Bedrock and prompt engineering to improve the chatbot's responses.
Which prompt engineering technique meets these requirements?

A. Zero-shot prompting
B. Complexity-based prompting
C. Few-shot prompting
D. Directional stimulus prompting

Answer: D

NEW QUESTION # 143
A food service company wants to develop an ML model to help decrease daily food waste and increase sales revenue. The company needs to continuously improve the model's accuracy.
Which solution meets these requirements?

A. Use Amazon CloudWatch to analyze customer orders.
B. Use Amazon Rekognition to optimize the model.
C. Use Amazon Personalize and iterate with historical data.
D. Use Amazon SageMaker AI and iterate with the most recent data.

Answer: D

Explanation:
Amazon SageMaker is AWS's fully managed ML service that supports retraining and deploying models with new, recent data for continuous improvement. This directly meets the requirement to iterate and continuously improve model accuracy.
A is correct:
"Amazon SageMaker enables teams to retrain models using the most recent data, ensuring ongoing improvement in model accuracy." (Reference: Amazon SageMaker Overview)
"Amazon SageMaker enables teams to retrain models using the most recent data, ensuring ongoing improvement in model accuracy." (Reference: Amazon SageMaker Overview) B (Amazon Personalize) is for recommendations, not general ML or waste reduction.
C (CloudWatch) is for monitoring, not ML training or deployment.
D (Rekognition) is for image/video analysis.

NEW QUESTION # 144
What does inference refer to in the context of AI?

A. The use of a trained model to make predictions or decisions on unseen data
B. The process of creating new AI algorithms
C. The process of combining multiple AI models into one model
D. The method of collecting training data for AI systems

Answer: A

Explanation:
* Inference = applying a trained ML model to new, unseen data to make predictions, classifications, or generate outputs.
* A is algorithm research, C refers to ensemble learning, D is data collection.
# Reference:
AWS ML Glossary - Inference

NEW QUESTION # 145
A research company implemented a chatbot by using a foundation model (FM) from Amazon Bedrock. The chatbot searches for answers to questions from a large database of research papers.
After multiple prompt engineering attempts, the company notices that the FM is performing poorly because of the complex scientific terms in the research papers.
How can the company improve the performance of the chatbot?

A. Clean the research paper data to remove complex scientific terms.
B. Use domain adaptation fine-tuning to adapt the FM to complex scientific terms.
C. Use few-shot prompting to define how the FM can answer the questions.
D. Change the FM inference parameters.

Answer: B

Explanation:
Domain adaptation fine-tuning involves training a foundation model (FM) further using a specific dataset that includes domain-specific terminology and content, such as scientific terms in research papers. This process allows the model to better understand and handle complex terminology, improving its performance on specialized tasks.
Option B (Correct): "Use domain adaptation fine-tuning to adapt the FM to complex scientific terms": This is the correct answer because fine-tuning the model on domain-specific data helps it learn and adapt to the specific language and terms used in the research papers, resulting in better performance.
Option A: "Use few-shot prompting to define how the FM can answer the questions" is incorrect because while few-shot prompting can help in certain scenarios, it is less effective than fine-tuning for handling complex domain-specific terms.
Option C: "Change the FM inference parameters" is incorrect because adjusting inference parameters will not resolve the issue of the model's lack of understanding of complex scientific terminology.
Option D: "Clean the research paper data to remove complex scientific terms" is incorrect because removing the complex terms would result in the loss of important information and context, which is not a viable solution.
AWS AI Practitioner Reference:
Domain Adaptation in Amazon Bedrock: AWS recommends fine-tuning models with domain-specific data to improve their performance on specialized tasks involving unique terminology.

NEW QUESTION # 146
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