Problem
I want to obtain the AWS Certified AI Practitioner certification what are some resources that can help me pass this exam.
Solution
In this tip, we will help our readers get the AWS Certified AI Practitioner certification (AIF-C01). This is an exam oriented to artificial intelligence. We will answer FAQ and provide some links and material to study.
What is the AIF-C01 exam?
The AWS Certified AI Practitioner certification (AIF-C01) is an exam that measures your level in AI, ML in the Amazon ecosystem.
Is this exam difficult?
This exam is an entry-level certification. It is introductory. It is not very hard to pass the exam.
What is the passing score for the exam?
The minimum score to pass is 700/1000.
What books are recommended for this exam?
The following books will help you to study for the exam:
- AWS Certified AI Practitioner: A Business Professional’s Guide
- AWS Certified AI Practitioner Exam Notes & Practice Tests
- AWS CERTIFIED AI PRACTITIONER | Exam code AIF-C01 | FAST TRACK PR
- AIF-C01 AWS Certified AI Practitioner! 166 QA!!! JULY UPDATES
- AWS Certified AI Practitioner Study Guide: Foundational (AIF-C01) Exam (Sybex Study Guide)
Can you please share some links to study for the AIF-C01 exam?
Yes. The following links can be useful for the exam:
Domain 1: Basics of AI and ML
Task Statement 1.1: Explain core AI concepts and terminology
Objectives:
- Define essential AI terms such as AI, ML, deep learning, neural networks, computer vision, natural language processing (NLP), model, algorithm, training, inferencing, bias, fairness, fit, and large language models (LLM).
- Differentiate between AI, ML, and deep learning.
- Explain various types of inferencing, including batch and real-time.
- Outline different data types used in AI models, including labeled/unlabeled, tabular, time-series, image, text, and structured/unstructured data.
- Understand what is supervised learning, unsupervised learning, and reinforcement learning.
Task Statement 1.2: Identify real-world use cases for AI
Objectives:
- Recognize areas where AI/ML can add value, such as enhancing decision-making, scaling solutions, and automating tasks.
- Understand when AI/ML might not be suitable, for example, in cost-benefit scenarios or when a specific outcome is needed over a prediction.
- Select the right ML techniques for particular cases, such as regression, classification, or clustering.
- Identify real-world AI applications like computer vision, NLP, speech recognition, recommendation systems, fraud detection, and forecasting.
- Understand the capabilities of AWS AI/ML services like SageMaker, Amazon Transcribe, Amazon Translate, Amazon Comprehend, Amazon Lex, and Amazon Polly.
Task Statement 1.3: Understand the ML development lifecycle
Objectives:
- Break down the stages of an ML pipeline: data collection, exploratory data analysis (EDA), data preprocessing, feature engineering, model training, hyperparameter tuning, evaluation, deployment, and monitoring.
- Explore sources for ML models, such as open-source pre-trained models or custom training.
- Learn how to implement models in production using managed API services or self-hosted APIs.
- Identify relevant AWS services for each stage of the ML pipeline, including SageMaker and Amazon SageMaker Model Monitor.
- Understand the principles of ML operations (MLOps), including experimentation, repeatable processes, and model monitoring.
- Evaluate ML models using performance metrics like accuracy, Area Under the ROC Curve (AUC), and F1 score, alongside business metrics such as customer feedback and ROI.
Domain 2: Basics of Generative AI
Task Statement 2.1: Explain foundational concepts of generative AI
Objectives:
- Understand key generative AI concepts like tokens, embeddings, vectors, prompt engineering, transformer-based LLMs, foundation models, and multi-modal models.
- Identify potential applications for generative AI, such as image/video/audio generation, summarization, chatbots, translation, and code generation.
- Explain the lifecycle of foundation models, from data selection and pre-training to fine-tuning and deployment.
Task Statement 2.2: Understand the strengths and weaknesses of generative AI for business solutions
Objectives:
- Outline the benefits of generative AI, including adaptability, responsiveness, and simplicity.
- Recognize the challenges, such as hallucinations, interpretability issues, and inaccuracies.
- Learn the factors involved in selecting the appropriate generative AI model, including performance requirements and compliance considerations.
- Assess business value and metrics for generative AI, such as efficiency, accuracy, and customer lifetime value.
Task Statement 2.3: Learn about AWS technologies for building generative AI apps
Objectives:
- Identify AWS services for developing generative AI applications, such as Amazon SageMaker JumpStart, Amazon Bedrock, and Amazon Q.
- Understand the benefits of using AWS services, including cost-effectiveness, speed, and security.
- Learn about the cost considerations when using AWS generative AI services, including performance, availability, and pricing models.
Domain 3: Application of Foundation Models
Task Statement 3.1: Design applications utilizing foundation models
Objectives:
- Identify criteria for choosing pre-trained models, such as cost, latency, and customization options.
- Understand how inference parameters (e.g., temperature, input/output length) affect model responses.
- Define Retrieval Augmented Generation (RAG) and its use in business applications, such as Amazon Bedrock.
- Explore AWS services that help store embeddings, such as Amazon OpenSearch Service and Amazon Aurora.
- Weigh the cost tradeoffs in customizing foundation models through fine-tuning, pre-training, or RAG.
Task Statement 3.2: Master prompt engineering techniques
Objectives:
- Describe the fundamentals of prompt engineering, including context, instructions, and negative prompts.
- Learn techniques such as chain-of-thought, zero-shot, few-shot, and prompt templates.
- Understand the advantages of prompt engineering, including improved response quality and specificity.
- Recognize the risks of prompt engineering, including exposure to attacks like poisoning or hijacking.
Task Statement 3.3: Training and fine-tuning foundation models
Objectives:
- Understand the key components of training a foundation model, including pre-training and fine-tuning.
- Explore methods for fine-tuning models, such as instruction tuning, domain adaptation, and transfer learning.
- Learn how to prepare data for fine-tuning, including curation, labeling, and reinforcement learning from human feedback (RLHF).
Task Statement 3.4: Evaluate the performance of foundation models
Objectives:
- Learn how to evaluate foundation model performance using methods like human evaluation and benchmark datasets.
- Identify metrics for performance assessment, including ROUGE, BLEU, and BERTScore.
- Determine whether a foundation model effectively achieves business goals, such as increasing productivity or improving user engagement.
Domain 4: Responsible AI Guidelines
Task Statement 4.1: Describe the creation of responsible AI systems
Objectives:
- Recognize the components of responsible AI, including fairness, bias, safety, inclusivity, robustness, and reliability.
- Learn how to utilize tools to assess responsible AI features (e.g., Guardrails for Amazon Bedrock).
- Comprehend best practices for selecting responsible models, such as considering sustainability and environmental impact.
- Identify potential legal risks associated with generative AI, like intellectual property concerns, biased outcomes, reduced consumer trust, end-user risks, and hallucinations.
- Understand dataset characteristics like inclusivity, diversity, and data quality (e.g., curated or balanced datasets).
- Grasp the implications of bias and variance in models, such as inaccuracy, overfitting, underfitting, and how these affect demographic groups.
- Learn about tools that help detect and monitor issues like bias and trustworthiness (e.g., analysing label quality, human audits, subgroup analysis, Amazon SageMaker Clarify, and Amazon Augmented AI [Amazon A2I]).
Task Statement 4.2: Understand the importance of model transparency and explainability
Objectives:
- Differentiate between transparent and explainable models and those that lack these qualities.
- Understand the tools for identifying transparent and explainable models (e.g., Amazon SageMaker Model Cards, open-source models, data, and licensing).
- Recognize the trade-offs between model safety and transparency (e.g., balancing interpretability and performance).
- Familiarize yourself with human-centered design principles in explainable AI.
Domain 5: Security, Compliance, and Governance for AI Solutions
Task Statement 5.1: Understand how to secure AI systems
Objectives:
- Identify AWS services and features designed to secure AI systems (e.g., IAM roles and permissions, encryption, Amazon Macie, AWS PrivateLink).
- Understand the importance of source citation and documenting data origins (e.g., data lineage, data cataloging, SageMaker Model Cards).
- Learn best practices for secure data engineering, such as evaluating data quality, implementing privacy technologies, and controlling data access.
- Comprehend the security and privacy concerns in AI systems (e.g., application security, threat detection, vulnerability management, encryption, and prompt injection).
Task Statement 5.2: Understand governance and compliance regulations for AI systems
Objectives:
- Recognize the compliance standards for AI systems (e.g., ISO, SOC, and algorithm accountability laws).
- Identify AWS services that help with governance and regulatory compliance (e.g., AWS Config, Amazon Inspector, AWS Audit Manager, AWS Artifact).
- Learn about data governance strategies, including data lifecycles, monitoring, retention, and logging.
- Understand how to follow governance protocols through policies, review strategies, governance frameworks like the Generative AI Security Scoping Matrix, and transparency standards.
Next Steps
For more information about this exam, refer to the following links.
- AWS Certified AI Practitioner (AIF-C01) – Full Course to PASS the Certification Exam
- How to pass AWS Certified AI Practitioner (AIF-C01) without any PAID Courses?
- Complete AWS Certified AI Practitioner AIF-C01 Crash Course 2025
- Exam Prep AIF-C01: AWS Certified AI Practitioner
Daniel Calbimonte is a Microsoft Most Valuable Professional, Microsoft Certified Trainer and Microsoft Certified IT Professional for SQL Server. He is an accomplished SSIS author, teacher at IT Academies and has over 10 years of experience as a QE and developer for SQL Server related software. He has worked for the government, oil companies, web sites, magazines and universities around the world. Daniel also regularly speaks at SQL Servers conferences and blogs.
- MSSQLTips Awards: Author of the Year Contender – 2015-2018, 2022, 2023 | Champion (100+ tips) – 2018
