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Amazon AIF-C01 Exam Syllabus Topics:

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

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Amazon AWS Certified AI Practitioner Sample Questions (Q374-Q379):

NEW QUESTION # 374
A company is building a solution to generate images for protective eyewear. The solution must have high accuracy and must minimize the risk of incorrect annotations.
Which solution will meet these requirements?

Answer: B

Explanation:
Amazon SageMaker Ground Truth Plus is a managed data labeling service that includes human-in-the-loop (HITL) validation. This solution ensures high accuracy by involving human reviewers to validate the annotations and reduce the risk of incorrect annotations.
* Amazon SageMaker Ground Truth Plus:
* It allows for the creation of high-quality training datasets with human oversight, which minimizes errors in labeling and increases accuracy.
* Human-in-the-loop workflows help verify the correctness of annotations, ensuring that generated images for protective eyewear meet high-quality standards.
* Why Option A is Correct:
* High Accuracy: Human-in-the-loop validation provides the ability to catch and correct errors in annotations, ensuring high-quality data.
* Minimized Risk of Incorrect Annotations: Human review adds a layer of quality assurance, which is especially important in use cases like generating precise images for protective eyewear.
* Why Other Options are Incorrect:
* B. Amazon Bedrock: Does not offer a knowledge base for data augmentation; it focuses on running foundation models.
* C. Amazon Rekognition: Provides image recognition and analysis, not a solution for minimizing annotation errors.
* D. Amazon QuickSight: A data visualization tool, not relevant to image annotation or generation tasks.
Thus, A is the correct answer for generating high-accuracy images with minimized annotation risks.


NEW QUESTION # 375
A medical company deployed a disease detection model on Amazon Bedrock. To comply with privacy policies, the company wants to prevent the model from including personal patient information in its responses. The company also wants to receive notification when policy violations occur.
Which solution meets these requirements?

Answer: C

Explanation:
Guardrails for Amazon Bedrock provide mechanisms to filter and control the content generated by models to comply with privacy and policy requirements. Using guardrails ensures that sensitive or personal information is not included in the model's responses. Additionally, integrating Amazon CloudWatch alarms allows for real-time notification when a policy violation occurs.
Option C (Correct): "Use Guardrails for Amazon Bedrock to filter content. Set up Amazon CloudWatch alarms for notification of policy violations": This is the correct answer because it directly addresses both the prevention of policy violations and the requirement to receive notifications when such violations occur.
Option A: "Use Amazon Macie to scan the model's output for sensitive data" is incorrect because Amazon Macie is designed to monitor data in S3, not to filter real-time model outputs.
Option B: "Configure AWS CloudTrail to monitor the model's responses" is incorrect because CloudTrail tracks API activity and is not suited for content moderation.
Option D: "Implement Amazon SageMaker Model Monitor to detect data drift" is incorrect because data drift detection does not address content moderation or privacy compliance.
AWS AI Practitioner Reference:
Guardrails in Amazon Bedrock: AWS provides guardrails to ensure AI models comply with content policies, and using CloudWatch for alerting integrates monitoring capabilities.


NEW QUESTION # 376
A company wants to develop ML applications to improve business operations and efficiency.
Select the correct ML paradigm from the following list for each use case. Each ML paradigm should be selected one or more times. (Select FOUR.)
* Supervised learning
* Unsupervised learning

Answer:

Explanation:

Reference:
AWS AI Practitioner Learning Path: Module on Machine Learning Strategies Amazon SageMaker Developer Guide: Supervised and Unsupervised Learning (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html) AWS Documentation: Introduction to Machine Learning Paradigms (https://aws.amazon.com/machine-learning/)


NEW QUESTION # 377
Which AWS service or feature can help an AI development team quickly deploy and consume a foundation model (FM) within the team's VPC?

Answer: C

Explanation:
Amazon SageMaker JumpStart is the correct service for quickly deploying and consuming a foundation model (FM) within a team's VPC.
* Amazon SageMaker JumpStart:
* Provides access to a wide range of pre-trained models and solutions that can be easily deployed and consumed within a VPC.
* Designed to simplify and accelerate the deployment of machine learning models, including foundation models.
* Why Option B is Correct:
* Rapid Deployment: JumpStart allows for quick deployment of models with minimal configuration, directly within a secure VPC environment.
* Ease of Use: Provides a user-friendly interface to select and deploy models, reducing the time to value.
* Why Other Options are Incorrect:
* A. Amazon Personalize: Focuses on creating personalized recommendations, not deploying foundation models.
* C. PartyRock: Not a recognized AWS service.
* D. Amazon SageMaker endpoints: Endpoints are for deploying specific models, not a feature for quickly starting with pre-trained foundation models.


NEW QUESTION # 378
Which prompting attack directly exposes the configured behavior of a large language model (LLM)?

Answer: C

Explanation:
Comprehensive and Detailed
A prompt template defines how the model is structured and guided (system prompts, roles, guardrails).
An attack that reveals or leaks this prompt template is known as a prompt extraction attack.
The other options (persona switching, exploiting friendliness, ignoring prompts) describe adversarial techniques but do not directly expose the internal configured behavior.
Reference:
AWS Responsible AI - Prompt Injection & Extraction Attacks


NEW QUESTION # 379
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