AWS Certified AI Practitioner (AIF-C01) Practice Exam

AWS Certified AI Practitioner (AIF-C01) Practice Exam

About AWS Certified AI Practitioner Exam

The AWS Certified AI Practitioner Exam is the foundational credential validates your understanding of AI, ML, and generative AI concepts, equipping candidates with in-demand knowledge that can sharpen your competitive edge. Ideal for individuals who interact with but don’t necessarily build AI/ML solutions on AWS, this certification positions you for career growth and the opportunity for higher earnings.

Exam Overview

• Exam Category: Foundational
  
• Exam Duration: 120 minutes
• Exam Format: 85 questions
• Exam Languages: English, Japanese
  

Who should take the AWS Certified AI Practitioner exam?

The AWS Certified AI Practitioner certification is perfect for business analysts, IT support staff, marketing professionals, product or project managers, sales professionals, and line-of-business or IT managers familiar with AI/ML technologies on AWS. The certification exam starting point if you are new to IT and the AWS Cloud, with foundational courses like AWS Cloud Practitioner Essentials recommended before taking the exam.

Benefits of Certification

• Becoming an AWS Certified AI Practitioner opens up new career opportunities.
• The certification proves your ability to understand and use AI/ML technologies effectively, making you a valuable asset in various professional roles.
• AWS Certified AI Practitioner dives deep into AI, ML, and generative AI, focusing on these technologies' frameworks, concepts, and associated AWS services. 

Knowledge Evaluated

The AWS Certified AI Practitioner (AIF-C01) exam assesses your ability to:

• Understand AI, ML, and generative AI concepts and strategies on AWS.
  
• Recognize appropriate AI/ML technologies for specific use cases.
• Use AI, ML, and generative AI technologies responsibly.

Recommended Knowledge

The recommended AWS Knowledge: Candidates should be familiar with -

• Core AWS services like Amazon EC2, Amazon S3, AWS Lambda, and Amazon SageMaker.
• The AWS shared responsibility model for security and compliance.
• AWS Identity and Access Management (IAM).
• AWS global infrastructure, including Regions, Availability Zones, and edge locations.
• AWS service pricing models.

Course Outline

The AWS Certified AI Practitioner Exam covers the following topics - 

Module 1: Understanding Fundamentals of AI and ML

1.1: Introduction to AI Concepts and Terminologies

• Define key AI terminology, including AI, ML, deep learning, neural networks, computer vision, NLP, models, algorithms, training and inference, bias, fairness, fit, and large language models (LLMs).
  
• Compare and contrast AI, ML, and deep learning.
• Explain different types of inferencing, such as batch and real-time.
• Categorize types of data in AI models, such as labeled, unlabeled, tabular, time-series, image, text, structured, and unstructured data.
• Outline the differences between supervised, unsupervised, and reinforcement learning.

1.2: Identifying AI Use Cases

• Identify scenarios where AI/ML can add value, such as assisting human decision-making, scaling solutions, and automation.
  
• Determine situations where AI/ML may not be suitable, including cost-benefit analysis and when specific outcomes are required over predictions.
• Choose the correct ML techniques for various use cases, including regression, classification, and clustering.
• Provide real-world examples of AI applications, including computer vision, NLP, speech recognition, recommendation systems, and fraud detection.
• Explain the capabilities of AWS managed AI/ML services like SageMaker, Amazon Transcribe, Amazon Translate, Amazon Comprehend, Amazon Lex, and Amazon Polly.

1.3: Understanding the ML Development Lifecycle

• Outline the components of an ML pipeline, including data collection, exploratory data analysis (EDA), data pre-processing, feature engineering, model training, hyperparameter tuning, evaluation, deployment, and monitoring.
• Discuss sources for ML models, such as open-source pre-trained models and custom model training.
• Describe methods for deploying models in production, including managed API services and self-hosted APIs.
• Identify relevant AWS services and features for each ML pipeline stage, including SageMaker, Amazon SageMaker Data Wrangler, Amazon SageMaker Feature Store, and Amazon SageMaker Model Monitor.
• Understand the core concepts of ML operations (MLOps) such as experimentation, repeatable processes, scalable systems, managing technical debt, production readiness, model monitoring, and re-training.
• Explain both model performance metrics (accuracy, AUC, F1 score) and business metrics (cost per user, development costs, customer feedback, ROI) for evaluating ML models.

Domain 2: Understanding Fundamentals of Generative AI

2.1: Basic Concepts of Generative AI

• Grasp foundational concepts in generative AI, including tokens, chunking, embeddings, vectors, prompt engineering, transformer-based LLMs, foundation models, multi-modal models, and diffusion models.
  
• Identify use cases for generative AI models, such as image, video, and audio generation; summarization; chatbots; translation; code generation; customer service agents; search; and recommendation engines.
• Describe the lifecycle of a foundation model, from data selection and model selection to pre-training, fine-tuning, evaluation, deployment, and feedback.

2.2: Capabilities and Limitations of Generative AI

• Describe the benefits of generative AI, including adaptability, responsiveness, and simplicity.
  
• Identify the drawbacks of generative AI, such as hallucinations, interpretability challenges, inaccuracies, and nondeterminism.
• Evaluate factors for selecting appropriate generative AI models, considering model types, performance requirements, capabilities, constraints, and compliance.
• Determine business value and performance metrics for generative AI applications, including cross-domain performance, efficiency, conversion rates, average revenue per user, accuracy, and customer lifetime value.

2.3: AWS Infrastructure and Technologies for Generative AI

• Identify AWS services and features to develop generative AI applications, including Amazon SageMaker JumpStart, Amazon Bedrock, and PartyRock.
• Explain the advantages of using AWS generative AI services for application development, such as accessibility, lower barrier to entry, efficiency, cost-effectiveness, and speed to market.
• Understand the benefits of AWS infrastructure for generative AI applications, covering aspects like security, compliance, responsibility, and safety.
• Assess the cost tradeoffs of AWS generative AI services, including considerations like responsiveness, availability, redundancy, performance, regional coverage, token-based pricing, provision throughput, and custom models.

Domain 3: Applications of Foundation Models

3.1: Design Considerations for Foundation Model Applications

• Identify criteria for selecting pre-trained models, considering factors like cost, modality, latency, multilingual capabilities, model size, complexity, customization, and input/output length.
  
• Understand the impact of inference parameters on model responses, including aspects like temperature and input/output length.
• Define Retrieval Augmented Generation (RAG) and its business applications, including use cases like Amazon Bedrock and knowledge bases.
• Identify AWS services that support storing embeddings in vector databases, including Amazon OpenSearch Service, Amazon Aurora, Amazon Neptune, Amazon DocumentDB (with MongoDB compatibility), and Amazon RDS for PostgreSQL.
• Explain cost tradeoffs for different foundation model customization approaches, such as pre-training, fine-tuning, in-context learning, and RAG.
• Understand the role of agents in multi-step tasks, including Agents for Amazon Bedrock.

3.2: Effective Prompt Engineering Techniques

• Explain the concepts and constructs of prompt engineering, including context, instruction, negative prompts, and model latent space.
  
• Understand techniques for prompt engineering, such as chain-of-thought, zero-shot, single-shot, few-shot, and prompt templates.
• Identify the benefits and best practices for prompt engineering, including response quality improvement, experimentation, guardrails, discovery, specificity, and concision.
• Define potential risks and limitations of prompt engineering, including exposure, poisoning, hijacking, and jailbreaking.

3.3: Training and Fine-Tuning Foundation Models

• Outline the key elements involved in training a foundation model, including pre-training, fine-tuning, and continuous pre-training.
  
• Describe methods for fine-tuning foundation models, such as instruction tuning, adapting models for specific domains, transfer learning, and continuous pre-training.
• Explain how to prepare data for fine-tuning a foundation model, including data curation, governance, size, labeling, representativeness, and reinforcement learning from human feedback (RLHF).

3.4: Evaluating Foundation Model Performance

• Describe approaches to evaluate foundation model performance, such as human evaluation and benchmark datasets.
  
• Identify metrics to assess the performance of foundation models, including ROUGE, BLEU, and BERTScore.
• Evaluate whether a foundation model effectively meets business objectives, including productivity, user engagement, and task engineering.

Domain 4: Understanding Guidelines for Responsible AI

4.1: Development of Responsible AI Systems

• Identify features of responsible AI, including bias, fairness, inclusivity, robustness, safety, and veracity.
  
• Use tools to identify features of responsible AI, such as Guardrails for Amazon Bedrock.
• Apply responsible practices in model selection, considering environmental impact and sustainability.
• Recognize the legal risks of using generative AI, including intellectual property infringement, biased model outputs, loss of customer trust, and end-user risk.
• Identify dataset characteristics, including inclusivity, diversity, curated data sources, and balanced datasets.
• Understand the effects of bias and variance, such as demographic impact, inaccuracy, overfitting, and underfitting.
• Use tools to detect and monitor bias, trustworthiness, and truthfulness, including label quality analysis, human audits, subgroup analysis, Amazon SageMaker Clarify, SageMaker Model Monitor, and Amazon Augmented AI (A2I).

4.2: Importance of Transparent and Explainable Models

• Understand the differences between transparent, explainable models and those that lack these qualities.
  
• Use tools to identify transparent and explainable models, including Amazon SageMaker Model Cards, open-source models, data, and licensing.
• Weigh the tradeoffs between model safety and transparency, including measuring interpretability and performance.
• Apply principles of human-centered design for explainable AI.

Domain 5: Understanding Security, Compliance, and Governance for AI Solutions

5.1: Methods to Secure AI Systems

• Identify AWS services and features to secure AI systems, including IAM roles, policies, and permissions, encryption, Amazon Macie, AWS PrivateLink, and the AWS shared responsibility model.
  
• Understand the concept of source citation and data origin documentation, including data lineage, data cataloging, and SageMaker Model Cards.
• Describe best practices for secure data engineering, including data quality assessment, privacy-enhancing technologies, data access control, and data integrity.
• Recognize security and privacy considerations for AI systems, such as application security, threat detection, vulnerability management, infrastructure protection, prompt injection, and encryption at rest and in transit.

5.2: Governance and Compliance Regulations for AI Systems

• Identify regulatory compliance standards for AI systems, including ISO, SOC, and algorithm accountability laws.
  
• Use AWS services and features to assist with governance and regulatory compliance, including AWS Config, Amazon Inspector, AWS Audit Manager, AWS Artifact, AWS CloudTrail, and AWS Trusted Advisor.
• Outline data governance strategies, including data lifecycles, user activity monitoring, and maintaining data catalog integrity.
• Recognize regional differences in regulatory compliance for AI systems, including privacy regulations and localization requirements.