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Amazon AIF-C01 Exam Syllabus Topics:

SectionWeightObjectives
AWS AI Services Overview26%- Describe Amazon Bedrock capabilities
  • 1. Explain foundation models (FMs) available in Bedrock
  • 2. Identify Bedrock features (agents, knowledge bases, guardrails)
  • 3. Describe model invocation, prompts, and responses
  • 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 Amazon SageMaker capabilities
  • 1. Describe built-in algorithms and SageMaker JumpStart
  • 2. Explain model training and deployment options
  • 3. Identify SageMaker features for ML workflows
  • 4. Recognize MLOps capabilities in SageMaker
- 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
AI/ML Fundamentals24%- Identify foundational terminology and definitions
  • 1. Define common ML metrics (accuracy, precision, recall, F1 score)
  • 2. Define models, algorithms, and parameters
  • 3. Explain training, inference, and fine-tuning
  • 4. Describe underfitting and overfitting
- 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
- 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
AI Application Development30%- Implement AI applications using AWS services
  • 1. Build applications using Amazon Bedrock APIs and SDKs
  • 2. Use knowledge bases for context-aware responses
  • 3. Implement prompt templates and chain-of-thought reasoning
  • 4. Configure model parameters (temperature, top-p, top-k)
- Evaluate and optimize AI applications
  • 1. Apply evaluation frameworks for AI applications
  • 2. Identify performance bottlenecks and optimization strategies
  • 3. Assess application outputs for relevance and accuracy
  • 4. Implement caching and cost optimization techniques
- Understand foundational concepts for building AI applications
  • 1. Identify retrieval-augmented generation (RAG) concepts
  • 2. Describe prompt engineering techniques
  • 3. Explain prompts, tokens, and context windows
  • 4. Explain function calling and tool use in AI applications
Responsible AI20%- Understand governance and compliance requirements
  • 1. Recognize regulatory and ethical considerations
  • 2. Describe model interpretability and explainability
  • 3. Explain data privacy regulations affecting AI
- Implement responsible AI best practices
  • 1. Evaluate AI outputs for quality and safety
  • 2. Implement appropriate guardrails for AI applications
  • 3. Apply human oversight in AI decision-making
- 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

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Amazon AWS Certified AI Practitioner Sample Questions (Q50-Q55):

NEW QUESTION # 50
A food service company wants to collect a dataset to predict customer food preferences. The company wants to ensure that the food preferences of all demographics are included in the data.

Answer: B

Explanation:
* Diversity in datasets ensures representation of all demographics, reducing bias and improving fairness.
* Accuracy is model performance.
* Recency bias skews towards recent data.
* Reliability measures consistency of results, not representation.
# Reference:
AWS Responsible AI Guidelines - Data Diversity


NEW QUESTION # 51
A company is building a generative Al application and is reviewing foundation models (FMs). The company needs to consider multiple FM characteristics.
Select the correct FM characteristic from the following list for each definition. Each FM characteristic should be selected one time. (Select THREE.) Concurrency Context windows Latency

Answer:

Explanation:


NEW QUESTION # 52
An ecommerce company is using a chatbot to automate the customer order submission process. The chatbot is powered by AI and Is available to customers directly from the company's website 24 hours a day, 7 days a week.
Which option is an AI system input vulnerability that the company needs to resolve before the chatbot is made available?

Answer: D

Explanation:
The ecommerce company's chatbot, powered by AI, automates customer order submissions and is accessible 24/7 via the website. Prompt injection is an AI system input vulnerability where malicious users craft inputs to manipulate the chatbot's behavior, such as bypassing safeguards or accessing unauthorized information. This vulnerability must be resolved before the chatbot is made available to ensure security.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Prompt injection is a vulnerability in AI systems, particularly chatbots, where malicious inputs can manipulate the model's behavior, potentially leading to unauthorized actions or harmful outputs. Implementing guardrails and input validation can mitigate this risk." (Source: AWS Bedrock User Guide, Security Best Practices) Detailed Option A: Data leakageData leakage refers to the unintended exposure of sensitive data during model training or inference, not an input vulnerability affecting a chatbot's operation.
Option B: Prompt injectionThis is the correct answer. Prompt injection is a critical input vulnerability for chatbots, where malicious prompts can exploit the AI to produce harmful or unauthorized responses, a risk that must be addressed before launch.
Option C: Large language model (LLM) hallucinationsLLM hallucinations refer to the model generating incorrect or ungrounded responses, which is an output issue, not an input vulnerability.
Option D: Concept driftConcept drift occurs when the data distribution changes over time, affecting model performance. It is not an input vulnerability but a long-term performance issue.
Reference:
AWS Bedrock User Guide: Security Best Practices (https://docs.aws.amazon.com/bedrock/latest/userguide/security.html) AWS AI Practitioner Learning Path: Module on AI Security and Vulnerabilities AWS Documentation: Securing AI Systems (https://aws.amazon.com/security/)


NEW QUESTION # 53
A company has multiple datasets that contain historical data. The company wants to use ML technologies to process each dataset.
Select the correct ML technology from the following list for each dataset. Select each ML technology one time or not at all. (Select THREE.) Computer vision Natural language processing (NLP) Reinforcement learning Time series forecasting

Answer:

Explanation:

Explanation:

Dataset 1: A dataset that contains text-based customer reviews # Natural language processing (NLP) NLP is designed for analyzing text (sentiment analysis, text classification, etc.).
Dataset 2: A dataset that contains images of animals labeled with their species names # Computer vision Computer vision models classify or detect objects in images.
Dataset 3: A dataset that contains daily sales volumes for products # Time series forecasting Time series forecasting predicts future values based on historical sequential data (like sales, demand, stock prices).


NEW QUESTION # 54
A software company wants to use a large language model (LLM) for workflow automation. The application will transform user messages into JSON files. The company will use the JSON files as inputs for data pipelines.
The company has a labeled dataset that contains user messages and output JSON files.
Which solution will train the LLM for workflow automation?

Answer: D

Explanation:
Fine-tuning is the process of training a pre-trained LLM with a labeled dataset specific to a desired task-in this case, mapping user messages to JSON outputs. Fine-tuning leverages supervised learning to specialize the model's outputs.
C is correct:
"Fine-tuning is a supervised learning approach in which a model is further trained on a custom, labeled dataset to adapt to a specific use case." (Reference: Amazon Bedrock Fine-Tuning, AWS Certified AI Practitioner Study Guide) A is incorrect-unsupervised learning does not use labeled data.
B (continued pre-training) uses unlabeled data.
D (RLHF) uses reward signals and human feedback, not direct labeled input/output pairs.


NEW QUESTION # 55
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