The AWS Certified AI Practitioner (AIF-C01) exam is a foundational-level certification designed to help professionals understand and apply artificial intelligence (AI), machine learning (ML), and generative AI concepts using AWS services.
Being a Senior Systems trainer at Lumify Work, I wanted to get into and understand the interplay of AI and the AWS platform.
I recently sat the AWS Certified AI Practitioner (AIF-C01) exam, and I wanted to share my general impressions to help others prepare, while respecting the NDA I signed.
As always, I recommend you use official and authorised training materials (such as the AWS CloudUp - AI Practitioner boot camp, or the AWS Certified AI Practitioner courses on Generative AI Essentials on AWS and Exam Prep: AWS Certified Practitioner) and the official AWS AIF C0F exam guide to prepare.
Overall Format and Feel of AIF C01 from AWS
Some students have recently asked about the level of difficulty of AWS Certified AI Practitioner (AIF-C01) exam questions. To me, the exam felt comprehensive but fair.
It tested both conceptual understanding and practical application knowledge across AWS AI/ML services. Many questions were simple and scenario-based (with one or two sentences, and not an entire case study) rather than pure memorisation, which I appreciated.
One bit of advice I have is to address each question by making sure you not only know what a given service or model does. This is so you can recognise when it is the right tool for the job. Given that this is a Foundational level exam, there were also quite a few questions that were basically “What service would you use to do x?”.
In terms of time allotment, the exam was 65 questions in 90 minutes. I didn’t feel particularly rushed. I was able to go through it at a comfortable pace, giving each question suitable attention, and still had plenty of time to review those I had marked for review.
General Guidance on AWS Certified AI Practitioner Certification
Based on key topic areas, let me share what I've found out about assumptions and dominant areas in the AWS Certified AI Practitioner Exams.
AI/ML Fundamentals
The exam assumes you understand basic AI/ML concepts - supervised vs unsupervised learning, common use cases, models, parameters, metrics and when to apply different approaches.
Pro tip: Make sure you're comfortable with terminology. Ensure you have a good understanding of the steps in developing and deploying ML models, especially around managing the datasets for training and testing. It’s not enough to recognise a thing, you have to know when to use it.
Example: Be able to choose the correct performance metric (recall, accuracy, precision) to address a specific concern. You won’t have to do the math, but you will need to know what each one actually calculates.
AWS AI Services Landscape
You need broad familiarity with the AWS AI service portfolio. Know which services solve which problems; this is more important than memorisng every feature detail.
Pro tip: Pay close attention to all the various forms and features of AWS SageMaker. Even ones that may not be heavily mentioned in the training materials.
Responsible AI
This was more prominent than I expected. AWS's responsible AI principles came up in various contexts. There were more questions on this than I kind of expected, but they were pretty straightforward.
Pro tip: Understand fairness, bias, transparency, explainability, and privacy considerations.
Generative AI Concepts
Given the timing of this AWS Certified AI Practitioner certification, generative AI features heavily. Foundation models, prompt engineering basics, and RAG (Retrieval Augmented Generation) concepts appeared throughout.
Pro tip: Don’t skimp on learning how to fine-tune your models, and how to control them. Make sure you understand the similarities and differences between guardrails, system prompts and model parameters. And as mentioned, knowing when to apply a concept is just as important as recognising that it is a concept.
AWS AI C01 Exam Surprises and Tips
There were things I didn't expect while taking the test for the AWS Certified AI Practitioner certification. I’ve listed them below to help students leading up to their own exams.
Breadth over depth: The exam covers a wide range of services rather than going extremely deep on any single one. Having said that, there were a couple of questions that were a little more technical than I was expecting.
Real-world scenarios: The AWS Certified AI Practitioner (AIF-C01) exam questions often present business problems, requiring you to recommend appropriate solutions.
Cost and security awareness: A few questions required understanding the pricing models of the various AI services. It’s worth reviewing the options for things like Bedrock and EC2 instance types. Security best practices mattered more than I anticipated.
Service integration: Several questions involved how services work together rather than in isolation. I would put the various questions about monitoring, alerts and repositories in this category. Most of these are mentioned in the exam documentation. Don’t skip these when preparing. If you know what things like Amazon CloudWatch and Amazon KMS do, then those questions are easy.
How to Prepare for AWS Certified AI Practitioner Certification
Below are my recommendations for preparing for the AWS Certified AI Practitioner exams. You can follow these steps as you work to gain the credential.
Step 1: Take the official AWS training course.
These can include the AWS CloudUp - AI Practitioner boot camp, or the AWS Certified AI Practitioner courses on Generative AI Essentials on AWS.
Step 2: Get hands-on experience with the major AI services through the console.
AWS Educate offers free access to 18+ hands-on labs using a simulated AWS Console—no credit card required. It includes labs on Generative AI, Machine Learning, and AI fundamentals. Alternatively, you can ask your manager about setting up sandbox environments via AWS Organisations.
Step 3: Understand use cases and service selection criteria.
Review AWS Decision Guides on service categories, use cases and trade-offs like cost, scalability and complexity. You can consult with your team about hypothetical scenarios related to business goals, workload types and team expertise. There are service selection frameworks you can refer to. Different frameworks focus on Architecture mapping, Traffic analysis, Security & compliance and Performance benchmarks.
Step 4: Review AWS whitepapers on AI/ML and responsible AI.
Learn about the core dimensions of responsible AI, according to AWS. Find out about Guardrails, Model evaluation, Bias and explainability, Human-in-the-loop and Governance.
Step 5: Practise with scenario-based thinking.
You can look for AWS Certified AI Practitioner Practice Questions. Beyond thinking about why a specific AWS Service would be the right fit, understand why it would be inappropriate. These are critical for mastering nuanced service selection. AWS Skill Builder offers interactive labs and simulations like PartyRock (to build GenAI apps with no code), AWS DeepRacer (to train ML models in a racing game) and Cloud Quest: Machine Learning (a role-playing simulation for AI engineers).
All in all, the AWS Certified AI Practitioner (AIF-C01) Exam is very achievable with proper preparation. Focus on understanding when and why to use services, not just what they do.
Good luck! (Although with good preparation, you won’t need luck.)
