I recently received a three-month AWS Skill Builder subscription through the AWS AI & ML Scholars 2026 program and want to use it effectively. I'm interested in both cloud computing and artificial intelligence, but I'm unsure how to organize my studies. My goal is to build a solid foundation in AWS, then move into AI and machine learning, especially where cloud services and AI overlap. What courses, hands-on exercises, learning paths, or certifications should I prioritize during these three months?
3 Answers
A practical route would be to start with Cloud Practitioner material, use Cloud Quest for hands-on practice in the AWS console, and then prepare for the Solutions Architect Associate certification. That certification gives you a broad understanding of many major AWS services. The practice exams included with Skill Builder are also useful because they closely resemble the style of the real exams.
Try not to measure progress by how many courses you finish. Combine one structured learning path with small projects so the concepts become practical. For example, you could build a simple application using S3, Lambda, and DynamoDB, then expand it with an AI service. Use the lessons to understand the architecture and Skill Builder exercises to fill in gaps.
You may not need to take the Cloud Practitioner exam if you’re technical and your main goal is the Solutions Architect Associate certification. A good Solutions Architect course will cover most of the foundational material anyway. After that, you can move toward the AI Practitioner content and then more specialized machine learning services.
Cloud Practitioner can still be worthwhile for someone completely new to cloud, but I agree that it may be unnecessary if the learner already has technical experience and is ready to study for Solutions Architect Associate.