Certified AI Product Development Specialist

Who this is for

• You have already shipped an AI feature

  And you want a verifiable credential for what you learned deciding scope, cost and risk in practice.

• You decide on AI products

  And you need to hold your ground on feasibility, inference budget and risk exposure without relying only on the technical team.

• You are moving into AI products

  And you want a clear reference for what to master, instead of a tool list that changes every quarter.

What you take with you

• A permanent verification code

  Anyone confirms your credential at pmcert.org/verify, without having to ask you for anything.

• Approval that does not expire

  A credential you earned does not lapse, asks for no annual renewal and charges no subscription.

• LinkedIn and a public profile

  Add it to your licenses and certifications section, or share the link to your profile.

• A certificate with your score by domain

  In PDF, showing how you performed in each area assessed.

What you need to know

Eight domains, drawn at random from a bank written for this exam. Read each one and see where you stand.

• Discovery and opportunity framing

  When AI genuinely solves the problem and when it only adds cost. Telling what the user needs apart from what the model can deliver.

• Feasibility and approach selection

  Whether the error is reversible, and what that changes in the scope. When to use retrieval, when rules are enough, and when to use no model at all.

• Data, context and privacy

  What can come from the knowledge inside the model and what has to come from your own base. Access permission, data deletion and the contract with whoever produces the data.

• Quality evaluation

  How to build an evaluation set that does not lie. Why 95% accuracy can be a useless number, and what to measure instead.

• Experience under uncertainty

  What the interface does when the model gets it wrong. Confirmation, undo, and why user acceptance does not follow measured accuracy.

• Inference economics

  Cost per call, a margin that does not improve with scale the way the rest of software does, and the point where a feature stops paying for itself.

• Launch and monitoring

  How to release without breaking the trust of people already using the product. A switch to turn one feature off on its own, and what support needs to know beforehand.

• Governance and responsible use

  Who approves what, according to the risk of the decision. And the path to contest it, for anyone affected by an automated call.