Quick Verdict
| AWS AI Practitioner (AIF-C01) | AWS ML Engineer Associate (MLA-C01) | |
|---|---|---|
| Cost | $100 | $150 |
| Level | Entry-level/Foundational | Associate/Mid-level |
| Duration | 90 minutes | 180 minutes (estimated) |
| Time to prepare | 20–30 hours | 50–80 hours |
| Pass score | 700/1000 | 730/1000 (estimated) |
| Hands-on experience | Not required | 1+ years SageMaker required |
| Focus | AI fundamentals, foundation models | ML systems design, SageMaker mastery |
| Best for | Beginners in AWS AI | Experienced ML engineers |
Quick answer: AWS AI Practitioner ($100, entry-level, 20–30 hours) is for beginners. AWS ML Engineer Associate ($150, associate-level, 50–80 hours) is for experienced ML engineers. The new MLA-C01 is replacing the professional-level ML Specialty. If new to AWS AI, start with AI Practitioner. If experienced, target MLA-C01.
AWS's ML Certification Evolution
AWS is restructuring its ML certification path. The old professional-level AWS Certified Machine Learning Specialty (MLS-C01) is being retired and replaced with AWS Certified Machine Learning Engineer - Associate (MLA-C01). This shift is important: AWS AI Practitioner (AIF-C01, entry-level) and AWS ML Engineer Associate (MLA-C01, associate-level) form the new foundation → mid-level progression.
AWS AI Practitioner (AIF-C01): Entry-Level AI Foundation
AWS AI Practitioner ($100, 65 questions—50 scored + 15 unscored, 90 minutes) is AWS's entry-level AI credential. Designed for professionals new to AWS and AI/ML services.
Exam Domains
- AI Fundamentals (20%): AI vs ML, generative AI, foundation models, LLMs
- ML Development Lifecycle (24%): Problem definition, data collection, training, evaluation, deployment
- Foundation Models (28%): LLMs, prompt engineering, retrieval-augmented generation (RAG), fine-tuning
- Responsible AI (14%): Bias, fairness, explainability, security, privacy
- Security and Compliance (14%): AWS security for AI workloads, data protection, governance
Strengths
- Entry point: Designed for beginners; no hands-on experience required
- Affordable: $100 entry cost
- Fast: 20–30 hours study (3–4 weeks)
- Generative AI heavy: 28% of exam covers foundation models and LLMs
- Clear progression: Natural stepping stone to AWS ML Engineer Associate (MLA-C01)
- Pass rate: ~70–75% (achievable with focused study)
Limitations
- Conceptual only: Does not require hands-on SageMaker experience
- Limited depth: Entry-level breadth; no deep SageMaker specialization
- Not sufficient for ML roles: Employers hiring ML engineers want deeper credentials
AWS ML Engineer Associate (MLA-C01): The New Mid-Level Standard
AWS Certified Machine Learning Engineer - Associate (MLA-C01, $150, estimated 180 minutes, 65–75 questions) is AWS's new mid-level ML credential, replacing the professional-level ML Specialty. Targets experienced ML engineers with 1+ years AWS/SageMaker experience.
Expected Exam Domains (Based on Associate-Level Pattern)
- Foundational ML Concepts (15%): ML algorithms, supervised/unsupervised learning, model evaluation
- ML Development Lifecycle (20%): Problem framing, data engineering, model training, tuning, deployment, monitoring
- SageMaker Features (25%): SageMaker Studio, Feature Store, Model Registry, Pipelines, AutoML
- Generative AI and LLMs (15%): Foundation models on AWS, fine-tuning, deployment, optimization
- MLOps and Governance (15%): Model governance, monitoring, drift detection, responsible AI implementation
- Security and Compliance (10%): AWS security for ML, encryption, audit logging, compliance
Strengths of MLA-C01
- Mid-level credential: Associate-level means it is between entry (AI Practitioner) and professional
- SageMaker focused: Tests practical SageMaker knowledge and MLOps patterns
- Lower cost than old specialty: $150 vs $300 for old MLS-C01
- Generative AI emphasis: Includes LLMs and foundation model deployment
- MLOps focus: Tests model monitoring, governance, and production patterns
- Industry alignment: Reflects modern ML engineering (less theory, more production ML)
- Career progression: Natural step between AI Practitioner and solutions architect roles
Limitations of MLA-C01
- Requires experience: 1+ years hands-on SageMaker/AWS ML experience essential
- Longer study time: 50–80 hours (vs 20–30 for AI Practitioner)
- Hands-on labs required: Cannot pass without practical SageMaker experience
- Not yet established: New credential; less employer brand recognition than retiring MLS-C01
Comparison: AI Practitioner vs ML Engineer Associate
| Dimension | AI Practitioner | ML Engineer Associate |
|---|---|---|
| Target audience | Beginners in AWS AI | Experienced ML engineers |
| Hands-on experience required | None | 1+ years SageMaker/AWS ML |
| Study depth | Breadth (5 domains) | Depth (6 domains, practical) |
| SageMaker coverage | Overview only | Deep: Studio, Feature Store, Pipelines, AutoML |
| Generative AI focus | Heavy (28%): LLMs, foundation models | Moderate (15%): deployment, fine-tuning |
| ML algorithms | Overview | Deep: implementation, tuning, evaluation |
| MLOps/governance | Mentioned | Core (15%): monitoring, drift, governance |
| Cost | $100 | $150 |
| Study time | 20–30 hours | 50–80 hours |
| Difficulty | Entry | Associate (moderate-hard) |
| Pass rate | 70–75% | 60–65% (estimated) |
Which Should You Choose?
Choose AWS AI Practitioner if:
- You are new to AWS and AI/ML
- You want entry-level credentials quickly
- You have limited study time (need to pass in 3–4 weeks)
- Budget is tight ($100)
- You are testing AI/ML interest before deeper commitment
- You have no hands-on AWS ML experience yet
Choose AWS ML Engineer Associate if:
- You have 1+ years AWS and SageMaker experience
- You are an experienced ML engineer moving to AWS
- You want to validate and certify your production ML knowledge
- You are targeting ML engineer or ML architect roles at AWS-focused companies
- You can invest 50–80 hours in serious study and hands-on labs
- You are replacing old ML Specialty (MLS-C01) with new standard
Career Progression Path
Recommended AWS ML Career Path (New, 2026+):