Verified SkillNyx certification
Certified SkillNyx AI Foundations Associate (CS-AIFA)
Builds practical AI fundamentals for entry-level roles and cross-functional AI teams.
Artificial IntelligenceINTERMEDIATEAssessments only
This certification covers essential AI concepts, basic modeling intuition, and responsible usage practices. Candidates learn to frame problems, interpret metrics, and communicate limitations clearly while supporting real AI initiatives in a team setting.
Pricing & exam snapshot
₹4,999
Approx. $60
Duration
3h
MCQs
200
Labs
0
Included: Labs, assessments, and a verifiable certificate ID.
Topics covered (syllabus)
- AI/ML basics and common model families
- Data preparation and feature basics
- Training/validation, overfitting, and leakage awareness
- Evaluation metrics and error analysis
- Supervised vs unsupervised learning concepts
- Intro to generative AI and prompting basics
- Responsible AI and privacy fundamentals
Skills covered
- Framing AI problems with clear success criteria
- Performing basic dataset checks and documenting assumptions
- Interpreting metrics and common failure patterns
- Comparing baselines and spotting overfitting signals
- Communicating limitations, risks, and next steps
- Applying responsible AI usage practices
- Collaborating effectively with ML and engineering teams
Job roles and salary range
- AI/ML Associate
- Data Analyst (AI-enabled)
- Junior ML Engineer
- Business Analyst (AI)
- Product Associate (AI)
Salary range per annum
₹6-16 LPA
Alt: $45k-$90k
Exam pattern
- Proctored exam, 180 minutes
- Randomized attempt assembled from a pool of 200 MCQs
- Scenario questions focused on evaluation and responsible usage
- Includes metric interpretation and error-analysis reasoning
- Passing requires consistent performance across concept areas
Labs & assessments
This certification focuses on assessments, with optional lab practice.
Outcomes
- Understand core AI concepts and how models are evaluated
- Explain model behavior using standard metrics and examples
- Identify common data pitfalls like leakage and bias signals
- Support AI projects with responsible, practical contributions