Requirements

• Basic familiarity with AWS core services (EC2
• S3
• IAM
• CloudWatch) and the AWS Console or CLI.
• Fundamental understanding of machine‑learning workflows (data preparation
• model training
• evaluation metrics).
• Very light Python knowledge recommended for optional hands‑on exploration of example notebooks.
• An AWS account (Free‑Tier is sufficient) if students wish to replicate the scenarios referenced in the explanations.
• No prior AWS certification is strictly required—motivated beginners are welcome—but experience at the Associate level (Developer or Solutions Architect) will make the material easier to absorb.

Description

Who this course is for:

• Candidates actively preparing to sit the MLA‑C01 certification who want realistic practice exams with deep‑dive rationales.
• Data Scientists
• Machine‑Learning Engineers
• and Developers already building models on AWS who need a credential to validate their expertise.
• Cloud Architects
• DevOps or Data Engineers expanding into ML solution design
• security
• and cost‑optimisation on AWS.
• Learners who prefer a practice‑first approach—testing knowledge through exam‑style questions instead of lecture‑only courses.