• Home
  • AWS Certified AI Practitioner AIF-C01 Training

AWS Certified AI Practitioner AIF-C01 Exam Prep

Course Information

  • Sessions 2 days
  • Duration 15 hrs
  • Level Beginner
  • Assessment NA

Venue

12 Woodlands Square #07-85/86/87 Woods Square Tower 1, Singapore 737715. 5 mins walk from Woodlands (NS9) MRT station.

The venue is disabled-friendly.

Download Course Brochure

Certification

  • Certificate of Completion from Tertiary Courses - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Completion from Tertiary Courses.

AWS Certified AI Practitioner AIF-C01 Training

Course Code: C19

4 Review(s) | Add Your Review

What's This Course About

Prepare to excel in the AWS Certified AI Practitioner AIF-C01 exam with our comprehensive course. Covering essential AI concepts, machine learning fundamentals, and AWS-specific AI tools, this course ensures you gain the necessary skills to succeed. Our expert instructors provide in-depth training on AWS services such as Amazon SageMaker, Amazon Q Business, and Amazon Bedrock, focusing on practical applications and real-world scenarios.

The course includes detailed modules on generative AI, responsible AI practices, and the development of machine learning solutions. You'll explore AI use cases, learn how to implement AI models, and understand the principles of AI security and compliance. With hands-on labs, case studies, and mock exams, you'll be well-prepared to achieve certification and advance your career in the AI and cloud computing fields.

Bonus: Free Practice Exams

Get exam-ready on our Practice Exam Portal — train in realistic Practice Mode and timed Exam Mode, then retake them as many times as you like before the real exam.

Start Practising →

Funding Options

No funding is available for this course

For WSQ funding, please checkout the details at WSQ - AWS Certified AI Practitioner Training (AIF-C01)

Course Fee

BTN44,100.00 (GST-exclusive)
BTN48,069.00 (GST-inclusive)

Course Date

Course Time

* Required Fields

Additional Note

Please bring your own laptop for hands-on training. If you don't have laptop, we can provide spare laptop for training use.

Post-Course Support

  • We provide free consultation related to the subject matter after the course.
  • Please email your queries to enquiry@tertiaryinfotech.com and we will forward your queries to the subject matter experts.

Cancellation & Reschedule Policy

  • You can register your interest without upfront payment. There is no penalty for withdrawal of the course before the class commences.
  • We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% for any paid amount.
  • Note the venue of the training is subject to changes due to availability of the classroom.

Course Details

Course Details

What You'll Learn

This course prepares you for the AIF-C01 certification exam, covering all official exam domains and their approximate weightings:

Domain 1 Domain 1: Fundamentals of AI and ML (20%)

  • Explain basic AI/ML terminology (AI, ML, deep learning, neural networks, NLP, LLM, GenAI, agentic AI) and differentiate AI, ML, GenAI, deep learning, and agentic AI
  • Describe types of AI/ML learning (supervised, unsupervised, reinforcement) and types of data/inferencing (batch, real-time, labeled/unlabeled, etc.)
  • Identify practical AI/ML use cases, select appropriate techniques (regression, classification, clustering), and recognize when AI/ML is NOT appropriate
  • Explain capabilities of AWS managed AI/ML services (Amazon SageMaker AI, Transcribe, Translate, Comprehend, Lex, Polly)
  • Describe the AI/ML development lifecycle/pipeline and MLOps fundamentals (experimentation, monitoring, retraining, production readiness)
  • Describe model performance metrics (accuracy, precision, recall, F1 score) and business metrics (cost per user, ROI) to evaluate ML models

Domain 2 Domain 2: Fundamentals of GenAI (24%)

  • Define foundational GenAI concepts (tokens, chunking, embeddings, vectors, prompt engineering, transformer-based LLMs, foundation models, diffusion models)
  • Identify GenAI use cases (image/video/audio generation, summarization, AI assistants, code generation, customer service agents)
  • Describe the foundation model (FM) lifecycle: data selection, model selection, pre-training, fine-tuning, evaluation, deployment, feedback
  • Understand capabilities and limitations of GenAI for business problems (hallucinations, interpretability, nondeterminism) and model selection factors
  • Describe AWS infrastructure/services for building GenAI applications (Amazon Bedrock, SageMaker AI/JumpStart, Strands Agents, Bedrock AgentCore)
  • Describe the token-based pricing model and cost tradeoffs of AWS GenAI services

Domain 3 Domain 3: Applications of Foundation Models (28%)

  • Identify FM selection criteria (cost, modality, latency, model size, customization) and the effect of inference parameters (e.g. temperature)
  • Define Retrieval Augmented Generation (RAG) and identify AWS vector database services (Amazon OpenSearch Service, Aurora, Neptune, RDS for PostgreSQL)
  • Choose effective prompt engineering techniques (chain-of-thought, zero-shot, few-shot, prompt templates) and recognize risks (prompt injection, jailbreaking, hijacking)
  • Describe FM training and fine-tuning methods (instruction tuning, transfer learning, continuous pre-training, RLHF) and data preparation practices
  • Describe methods/metrics to evaluate FM performance (ROUGE, BLEU, BERTScore, LLM-as-a-judge, benchmark datasets, human-in-the-loop)
  • Identify approaches to evaluate FM-based applications (RAG, agents, workflows) against business objective alignment metrics

Domain 4 Domain 4: Guidelines for Responsible AI (14%)

  • Identify features of responsible AI: bias, fairness, inclusivity, robustness, safety, veracity
  • Explain tools to identify/monitor bias, trustworthiness, and truthfulness (Amazon SageMaker Clarify, SageMaker Model Monitor, Amazon A2I, Bedrock Guardrails)
  • Identify legal and ethical risks of working with GenAI (IP infringement claims, biased model outputs, hallucinations, loss of customer trust)
  • Recognize the importance of transparent and explainable models and related tools (SageMaker Model Cards, Bedrock Model Evaluations)
  • Describe tradeoffs between model safety and transparency, and principles of human-centered design for explainable AI

Domain 5 Domain 5: Security, Compliance, and Governance for AI Solutions (14%)

  • Identify AWS services/features to secure AI systems (IAM roles/policies, encryption, Amazon Macie, AWS PrivateLink, Bedrock Guardrails, Bedrock AgentCore Identity)
  • Describe security and privacy considerations for AI systems (prompt injection, data leakage prevention, output filtering/validation, audit trail and logging)
  • Describe hallucination detection methods and grounding techniques (RAG grounding, output validation, confidence scoring)
  • Identify AWS services for governance and regulation compliance (AWS Config, Amazon Inspector, AWS Audit Manager, AWS Artifact, AWS CloudTrail, Trusted Advisor)
  • Describe data governance strategies (data lifecycles, residency, retention, monitoring) and governance frameworks (e.g. Generative AI Security Scoping Matrix)

Course Info

Promotion Code

Your will get 10% discount voucher for 2nd course onwards if you write us a Google review.

Minimum Entry Requirement

Knowledge and Skills

  • Able to operate using computer functions
  • Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)

Attitude

  • Positive Learning Attitude
  • Enthusiastic Learner

Experience

  • Minimum of 1 year of working experience.

Target Age Group: 18-65 years old

Minimum Software/Hardware Requirement

Software:

TBD

Hardware: Window or Mac Laptops

Job Roles

Job Roles

  • AI Engineer
  • Machine Learning Engineer
  • Data Scientist
  • Cloud Solutions Architect
  • AI Solutions Architect
  • Data Analyst
  • AWS Cloud Practitioner
  • AI Consultant
  • Machine Learning Specialist
  • AI Research Scientist
  • Cloud Developer
  • Data Engineer
  • AI Product Manager
  • Software Developer
  • IT Consultant
  • Technical Support Engineer
  • Business Intelligence Developer
  • AI System Developer
  • Cloud Infrastructure Engineer
  • Technology Analyst

Trainers

Trainers

CY Quah is an ACLP-certified trainer and data science professional with extensive experience in Python, NLP, and machine learning. He has led AI training programs for SAP, Temasek Polytechnic, and IMDA under the SGUnited Mid-Career Pathways initiative, and has delivered corporate workshops on text analytics, recommender systems, and chatbot development. His expertise includes applying NLP tools such as NLTK, spaCy, and Gensim for sentiment analysis, topic modeling, and text classification.
Agus Salim is an experienced IT solutions and cybersecurity professional with a strong foundation in cloud infrastructure and project management. With over a decade of experience in systems integration, software development, and IT security across both enterprise and consulting environments, he brings a practical understanding of secure system design and deployment. His credentials include PMP, CompTIA Security+, CEH, and AWS Certified Cloud Practitioner, reflecting his balanced expertise in governance, risk management, and cloud operations. Agus has worked with leading organizations such as Citi and Check Point Software Technologies, providing hands-on technical and security support across multi-cloud platforms.

Anil  is a ACLP certified trainer. He is an Enterprise Cloud and DevOps Consultant , responsible for  helping clients to move Virtual data centre to Private Cloud based on OpenStack and Public Cloud ( AWS, Azure and Google cloud) . Consulting and training experience on Devops tool chain like github , Jenkins, Sonarqube, Docker & kubernetes, Cloud foundry, Openshift, Ansible and SaltStack. Lot of my Role is involved design and implementation of a solution and training

Peter Cheong is an IT and knowledge management professional with strong expertise in networking, cybersecurity, and information systems. He has completed the Cisco Networking Academy Introduction to Packet Tracer course and has participated in international ICT and knowledge management conferences such as the IFLA Knowledge Management Satellite Meeting. With professional experience in IT systems and infrastructure, Peter brings both technical knowledge and global exposure to his training. As an adult educator, Peter focuses on building learners’ foundational skills in cybersecurity, network defense, and risk management aligned to CompTIA Security+ objectives. His sessions emphasize real-world security scenarios, equipping participants to recognize vulnerabilities, manage threats, and implement effective security controls. His combination of practical training and industry exposure ensures learners are well-prepared for both the certification exam and workplace application.

Ben is an experienced IT Infrastructure professional with more than 20 years of working experience in IT sector. Due to Corporate Digital Transformation and COVID-19 during early 2020 he shifted his focus to Cloud Computing specialized in Cloud Infrastructure Solutioning. He is  an AWS Certified Solution Architect Associate, Google Certified Cloud Engineer, Microsoft Certified Azure Fundamentals and Alibaba Cloud Associate.

Review

Customer Reviews (4)

might recommend Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
More examples done and shown by the trainer before the students try it out by themselves. (Posted on 10/2/2022)
will recommend Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
. (Posted on 2/10/2021)
might recommend Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Perhaps, can recommend people to go for "html & css" course first before going for this dreamweaver course as advised by the trainer. So we can understand css better and apply when using Dreamweaver more efficiently. (Posted on 3/16/2020)
Might Consider Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
should provide desktop rather than laptop, mouse not provided, class were too small, the TV to project from the laptop were not connected something is wrong.

lesson should conduct in 2 full days rather than 1full day so the lesson will not be so pack. (Posted on 8/12/2019)

Write Your Own Review

You're reviewing: AWS Certified AI Practitioner AIF-C01 Training

How do you rate this product? *

  1 star 2 stars 3 stars 4 stars 5 stars
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment