AIPM-1 Exam Syllabus

AIPM-1 Exam Syllabus Overview

This AIPM-1 exam syllabus defines the Version 1.2 assessment standard for the APD Certified AI Product Manager credential. It applies to bookings explicitly identified as Version 1.2, and APD must state the applicable version and delivery arrangements before registration. The AIPM-1 exam syllabus below is the single source for what the exam covers and how it is scored.

AIPM-1 certifies a job-ready AI product manager at the Professional Practice level. The practitioner can independently own a bounded AI-native product or meaningful feature from discovery through launch and learning, and can own a defined scope within an integrated product with normal organizational support.

The standard covers the complete product lifecycle. A successful candidate creates usable work products, makes evidence-based trade-offs, coordinates specialists, and takes responsibility for product decisions within the assigned mandate. AI Product Manager is the market-facing role language; AI-Native Product Manager is the underlying professional capability model.

AIPM-1 is based on the APD AI-Native Product Manager Competency Model v3.1, which defines AI Product Manager as the market-facing role language and AI-Native Product Manager as the professional capability model behind excellent AI product work.

AIPM-1 is the first level in the APD Certified AI Product Manager pathway:

What Changed in the AIPM-1 Exam Syllabus v1.2

Version 1.2 of the AIPM-1 exam syllabus redefines AIPM-1 as Professional Practice and adds a required integrated product case.

• Repositions AIPM-1 as Professional Practice with explicit C1 ownership and defined C2 scope.
• Replaces awareness-only outcomes with actionable requirements, workflow, evaluation, release, economics, and governance decisions.
• Retains seven exam domains and their target weights while updating titles, learning objectives, and model mapping.
• Retains 60 online questions, 75 minutes, and a 75% online pass mark; increases applied reasoning and artifact interpretation.
• Adds a required integrated product case or simulation with a separate minimum score, while retaining open entry and no employment-portfolio prerequisite.
• Adds compound systems, bounded agency, memory, builder fluency, cost per successful outcome, and observable lifecycle evidence.

Product Ownership and Scope

C1—Bounded means a low-complexity product with clear users, tasks, authority, and limited integrations. C2—Integrated means a medium-complexity product with multiple capabilities or integrations inside a defined product boundary. C3—Critical or Multi-System means high complexity arising from consequence, autonomy, system dependencies, scale, ambiguity, or organizational reach. C4—Strategic System means a platform, portfolio, or organization-level product system with systemic effects.

Complexity is assessed across consequence of error; model and agent complexity; authority to act; data sensitivity and rights; integrations; scale and reliability; domain and regulatory constraints; and organizational scope. A small product can be high complexity. Review each dimension and any overriding high-consequence condition; do not classify by user count, budget, team size, or number of agents alone.

AIPM-1 owns C1 outcomes end to end and defined C2 scope with access to engineering, design, data, legal, security, and domain specialists. “Independent ownership” means making and coordinating product decisions within a clear mandate, including escalation; it does not mean personally performing every specialist task.

At this level, candidates are expected to:

• qualify the user problem, AI suitability, success criteria, and value hypothesis;
• specify target behavior, requirements, authority boundaries, data needs, and experience;
• prototype or simulate the experience, inspect working evidence, and test representative failures;
• create evaluation cases, rubrics, regression checks, risk controls, and launch decision evidence;
• plan staged release, monitoring, recovery, adoption, value measurement, and the next learning cycle.

Candidate Profile and Entry

AIPM-1 is open to all candidates with no formal prerequisite. It is suitable for product managers transitioning to AI, aspiring product managers, engineers, product owners, business analysts, designers, consultants, and other professionals preparing to own AI-native product work. Prior employment, coding experience, machine learning engineering, and Agile certification are not required.

Candidates without product experience should first build discovery, prioritization, requirements, stakeholder, and delivery capability through supervised projects or simulations. Open entry does not reduce the passing standard. Preparation should include AI Product Practice / Portfolio Building, although an employment portfolio is not required for certification.

What AIPM-1 Does Not Validate

AIPM-1 certifies professional practice within a defined ownership boundary. It does not certify standalone leadership of every AI product context.

The following are outside this standalone credential boundary:

• high-autonomy products;
• safety-critical products;
• heavily regulated product leadership;
• complex multi-agent product leadership;
• enterprise-platform product leadership;
• coding, model training, and production infrastructure implementation;
• specialist security testing;
• legal interpretation.

Product-level technical judgment and collaboration are required. These capabilities are addressed at higher levels of the APD Certified AI Product Manager pathway or in specialized professional roles.

AIPM-1 Exam Format and Passing Rules

AIPM-1 is assessed through two mandatory components.

Both components are mandatory. Overall score = 0.70 × online percentage + 0.30 × case percentage. A high online score cannot compensate for a failed case, and a high case score cannot compensate for a failed online exam. The overall score is reported only with the separate component results. A Version 1.2 certificate requires both passes and completion of applicable integrity checks.

Component B uses one coherent bounded-product scenario, supplied templates, and preconfigured or recorded execution evidence. Setup time is excluded from the timed task. A structured simulation is an equivalent route when it elicits the same observable decisions and work products. No public deployment or employer project is required.

APD Institute may update the exam duration, number of questions, passing score, delivery rules, and assessment policies over time.

AIPM-1 Exam Domain Blueprint and Weightings

AIPM-1 is organized into seven exam domains. Exam-domain identifiers are retained for traceability. Domain titles and objectives are updated for v3.1 capability. Exam D1 and competency-model D1 are separate namespaces; the mapping section provides the relationship. Learning objective IDs take the form AIPM1-D1.1 for the first numbered objective in Domain 1.

The following counts define a standard scored online form. Target percentages describe curriculum emphasis; whole-question allocation necessarily produces small rounding differences. Each question has one primary domain. Practical-case scores are separate from these question counts.The AIPM-1 exam syllabus distributes 60 questions across seven domains as follows.

Forms must use the stated counts. Domain, cognitive level, content family, and response format are independent tags on the same questions; their totals must not be added together.

Cognitive and Question Design

Response-format allocation is 48 single-select questions (80%) and 12 multiple-response questions (20%). Content family is determined by the evidence needed to answer, not stem length. The case assesses integrated creation and decision quality beyond selected-response performance.

Exam Domains and Learning Objectives

Each domain in this AIPM-1 exam syllabus states its weight, purpose, objectives, topics, work products, and scope boundary.

Domain 1: AI-Native Product Ownership and Professional Practice

Weight: 12%

Domain Purpose

Own a bounded product problem and coordinate the complete decision lifecycle.

Learning Objectives

Candidates must be able to:

1. determine whether AI is core to a product’s value mechanism and distinguish AI-native product capability from incidental AI tool use;
2. define an ownership mandate, product boundary, decision rights, and the specialists needed for a C1 or defined C2 scope;
3. translate user evidence into a problem statement, behavior hypothesis, and prioritized outcome roadmap;
4. choose the next discovery, prototype, pilot, launch, or learning step from the evidence available;
5. distinguish product accountability from engineering implementation, legal approval, and governance oversight;
6. use AI-assisted product tools responsibly, verify their outputs, and communicate a traceable decision with assumptions and limitations.

Key Topics

• AI-native definition;
• lifecycle ownership;
• complexity and accountability;
• hypothesis-driven discovery;
• professional judgment;
• cross-functional coordination.

Produce or Evaluate

An ownership brief, stakeholder and decision map, hypothesis register, prioritized next-step decision, and contribution record.

Scope Boundary

C1 product ownership is required; portfolio strategy and organization-wide role architecture are outside the level.

Domain 2: AI Product Opportunity, Value, Adoption, and Economics

Weight: 14%

Domain Purpose

Connect a practical AI opportunity to customer value, adoption, and sustainable economics.

Learning Objectives

Candidates must be able to:

1. validate a user or operational problem, target users, affected stakeholders, and a measurable baseline;
2. compare an AI approach with rules, workflow redesign, human service, or other simpler alternatives;
3. define outcome and guardrail metrics, a value hypothesis, assumptions, and a proportionate discovery experiment;
4. calculate cost per successful outcome from supplied inference, retrieval, tool, review, support, and failure costs;
5. choose a bounded scope using feasibility, value, trust, quality, latency, cost, and risk evidence;
6. plan onboarding, proof of value, adoption measurement, and an initial pricing or internal funding rationale appropriate to the scenario.

Key Topics

• capability–use case fit;
• value baseline;
• user evidence;
• basic unit economics;
• activation;
• adoption;
• outcome measurement;
• AI and non-AI options.

Produce or Evaluate

An opportunity and value brief, simple economics calculation, experiment proposal, and onboarding/value measurement plan.

Scope Boundary

Basic product economics and adoption are assessed; portfolio finance and complex commercialization strategy are not.

Domain 3: Human-AI Workflow, Requirements, and Trustworthy Experience

Weight: 15%

Domain Purpose

Specify a useful, trustworthy human-AI workflow with explicit authority and recoverable failures.

Learning Objectives

Candidates must be able to:

1. decompose a bounded scenario into users, tasks, decisions, inputs, outputs, and handoffs;
2. allocate human and AI responsibilities and define advisory, approval-required, permitted, and prohibited actions;
3. write concise product requirements and testable acceptance criteria for AI behavior, quality, user control, and exceptions;
4. design confirmations, correction, disclosure, source verification, escalation, interruption, and recovery at the points where they are needed;
5. specify permissions, action limits, step or spend budgets, and stop conditions for a simple tool-using workflow;
6. revise a prototype or workflow simulation from user feedback and failed task evidence, keeping the decision rationale traceable.

Key Topics

• human-AI workflow;
• behavior specification;
• trust calibration;
• approval UX;
• least privilege;
• exception paths;
• fallback;
• inclusive interaction.

Produce or Evaluate

A workflow and authority map, requirements/acceptance criteria, and an annotated interaction prototype or simulation.

Scope Boundary

Bounded design is required; enterprise workflow redesign and complex multi-agent orchestration are outside the level.

Domain 4: AI Models, Systems, Agents, and Builder Fluency

Weight: 18%

Domain Purpose

Make informed model and system choices and create inspectable working evidence.

Learning Objectives

Candidates must be able to:

1. explain the product implications of probabilistic model behavior, context limits, hallucination, grounding, and multimodal inputs;
2. compare prompting, retrieval, tools, rules, and agentic workflows against the task; explain when fine-tuning is a specialist option;
3. write a model behavior brief and compare established options using task quality, cost, latency, reliability, and risk;
4. identify how models, instructions, retrieval, tools, memory, interfaces, and controls combine to determine product behavior;
5. create or adapt a small prototype or simulated workflow with an approved tool and inspect a request/response, trace, or evaluation result;
6. identify regression and dependency risks from a provider, model, prompt, tool, or configuration change and specify required checks.

Key Topics

• LLMs;
• generative and multimodal AI;
• compound systems;
• APIs;
• agents;
• build-buy-partner;
• portability;
• versioning;
• builder fluency.

Produce or Evaluate

A model/system options memo, behavior requirements, and a prototype walkthrough or trace-based failure diagnosis.

Scope Boundary

No coding exam, model training, infrastructure build, or framework-specific configuration is required.

Domain 5: Product Data, Knowledge, Retrieval, Memory, and Context

Weight: 12%

Domain Purpose

Define trustworthy information inputs and controlled memory for the owned product.

Learning Objectives

Candidates must be able to:

1. specify required sources, purpose, owners, rights, freshness, provenance, and access boundaries;
2. define retrieval and grounding requirements, citations, insufficient-evidence behavior, and handling of conflicting sources;
3. diagnose whether a failure arises from missing data, poor retrieval, wrong context, permissions, or unsupported generation;
4. separate task context from persisted memory and define consent, retention, correction, access, and deletion requirements;
5. select representative evaluation data and prevent sensitive-data exposure or leakage between development and evaluation sets;
6. define source-update review, regression checks, and feedback mechanisms with named owners.

Key Topics

• RAG;
• knowledge readiness;
• permission-aware retrieval;
• data provenance;
• context assembly;
• controlled memory;
• evaluation data;
• source lifecycle.

Produce or Evaluate

A source/rights register, retrieval and context specification, memory policy, and knowledge-change checklist.

Scope Boundary

Product requirements and evidence interpretation are required; database, embedding, and retrieval-pipeline implementation are not.

Domain 6: Evaluation-Driven Delivery, Readiness, and Lifecycle Learning

Weight: 17%

Domain Purpose

Use evaluation evidence to lead release, operation, and continuous improvement of a bounded product.

Learning Objectives

Candidates must be able to:

1. define a quality model connecting task success, groundedness, safety, trust, latency, cost, and user outcomes;
2. build representative normal, edge, failure, and misuse cases with expected behavior and explicit rubrics;
3. interpret supplied evaluation results, identify failure patterns and weak coverage, and compare alternatives without overstating confidence;
4. define release thresholds, non-negotiable controls, regression checks, and conditions for a restricted pilot, launch, pause, or rollback;
5. plan staged rollout, monitoring signals, alerts, accountable owners, user support, fallback, and incident escalation;
6. use production, adoption, cost, user-feedback, and incident evidence to update requirements and the next experiment or backlog decision.

Key Topics

• offline/online evaluation;
• golden sets;
• rubrics;
• failure taxonomy;
• regression;
• observability;
• release readiness;
• resilience;
• learning loops.

Produce or Evaluate

An evaluation specification and case set, results analysis, release decision, and post-launch learning plan.

Scope Boundary

Meaningful evidence and uncertainty recognition are required; advanced statistics and evaluation infrastructure engineering are not.

Domain 7: Responsible AI, Safety, Security, and Product Governance

Weight: 12%

Domain Purpose

Translate product risks into proportionate requirements, controls, and accountable decisions.

Learning Objectives

Candidates must be able to:

1. assess plausible harms, affected users, sensitive information, consequential actions, and operational dependencies;
2. recognize prompt injection, unauthorized retrieval, unsafe tool use, excessive agency, and insecure handling of model output;
3. specify enforceable permission, validation, approval, data minimization, logging, and recovery requirements with specialists;
4. address transparency, fairness, accessibility, contestability, privacy, and meaningful human oversight in the product design;
5. maintain a risk record linking each material risk to a control, evidence, owner, residual risk, and review trigger;
6. use supplied domain obligations to decide whether to proceed, restrict scope, pause, or escalate; identify decisions outside the PM mandate.

Key Topics

• responsible AI;
• product threat/risk analysis;
• authority controls;
• privacy;
• fairness;
• impact;
• auditability;
• residual risk;
• incident learning.

Produce or Evaluate

A proportionate risk/control register and a justified governance or escalation decision.

Scope Boundary

Candidates translate requirements with specialists; they do not provide legal opinions or implement security controls.

Cross-Cutting Capability Threads

The v3.1 capability threads run through domain questions, case evidence, and assessment rubrics. They do not add a second set of scored domains.

Model and Research Productization

Translate a model capability or limitation into testable behavior and an AI-suitability decision.

Agent Authority and Runtime Control

Define tool permissions, approval points, budgets, stopping conditions, and recovery for bounded tasks.

Evaluation-Driven Development

Use representative evidence, regression checks, and release thresholds from discovery through operation.

AI Product Economics

Calculate cost per successful outcome, including failures and human review, and connect it to user value.

Builder Fluency

Create or adapt a prototype or workflow simulation and inspect API behavior, traces, and evaluation evidence.

Platform and Ecosystem Fluency

Explain API contracts, basic portability, developer/user needs, and dependencies at product level.

Domain and Regulatory Translation

Translate supplied domain rules into requirements and controls; identify specialist decisions.

AI-Native Product Agility

Use adaptive discovery, staged delivery, continuous evaluation, feedback, and accountable decisions to improve value, quality, safety, cost, and readiness. Framework-specific events, role definitions, velocity, story points, and branded scaling methods are outside this syllabus.

Mapping to the Competency Model

The four model clusters and ten competency domains remain the common core. The syllabus groups them into assessable domains at the relevant accountability level. In this table, CM denotes the competency model, and the domain numbers in the right-hand column all refer to that model.

CM-D1 covers strategy and value discovery; D2 commercialization and value realization; D3 human-AI and agentic workflow; D4 model and system decisions; D5 data, knowledge, retrieval, memory, and context; D6 experience, trust, and control; D7 evaluation and intelligence; D8 delivery and lifecycle; D9 responsible AI and governance; and D10 practice, builder fluency, and leadership.

Role-archetype examples may involve applications, agents, models, platforms, enterprise or vertical products, evaluation and governance, growth, or portfolio leadership. Candidates are assessed on the common core at their level; a platform or model example does not require specialist engineering implementation.

Required Integrated Product Case

The case in the AIPM-1 exam syllabus represents a C1 product or a clearly bounded part of C2, such as a policy-grounded service copilot whose recommendations require human approval. APD supplies the user brief, constraints, sample sources, a prototype/sandbox or recorded interactions, evaluation results, operating costs, and a change event. Candidates may conclude that AI is unsuitable or that launch should be delayed when the evidence supports that decision.

Submit one coherent AI Product Minimum Readiness Decision Pack. Concise templates and annotated artifacts are acceptable; volume and visual polish earn no separate credit.

The decision pack must cover:

1. opportunity, users, baseline, AI suitability, value hypothesis, and success/guardrail metrics;
2. workflow, human responsibilities, authority limits, approvals, exceptions, and recovery;
3. requirements and acceptance criteria covering behavior, information, quality, trust, and controls;
4. model/system choice and data, retrieval, context, or memory requirements with assumptions;
5. a prototype or simulation walkthrough and evidence of one tested or reproduced failure and a justified change;
6. at least 12 candidate-authored evaluation cases, a rubric, results interpretation, and release thresholds;
7. a risk/control record and a launch, restricted-pilot, pause, or redesign decision tied to evidence;
8. rollout, monitoring, incident response, adoption, basic economics, and the next learning decision.

The 12-case set must cover normal tasks, missing or conflicting evidence, authorization boundaries, unsafe or malicious input, and a tool/service failure. Each case identifies the input/context, expected behavior, rationale, and pass criterion. Execute or inspect APD-supplied execution evidence for at least six cases and record observed failures separately from hypothetical risks. This minimum tests evaluation design skill; it is not a production-safety coverage standard.

Practical Case Rubric and Critical Criteria

Assessors rate each criterion on a 0–4 scale: 0 = absent or materially incorrect; 1 = fragmented claims with major gaps; 2 = plausible approach with material unresolved evidence or execution gaps; 3 = competent, traceable, feasible performance at the level; 4 = strong performance with well-tested alternatives, uncertainty handling, and useful learning. The component score is the sum of each criterion’s weight multiplied by its rating divided by four. Weights total 100; a score of 75 corresponds to rating 3 throughout. Whole-number ratings are used, with written rationale for each.

Pass requires at least 75/100. In addition, the workflow/authority, evaluation/release, and risk/governance criteria must each score at least 3. A candidate cannot pass while recommending an unauthorized consequential action, hiding a material failed release gate, or presenting fabricated results. Assessors must identify the case evidence, applicable requirement, and rubric basis for a critical failure; a supported pause or no-go decision is valid performance. Borderline and critical-failure decisions receive a second review.

Passing Candidate Standard

A passing candidate demonstrates the full decision chain defined by this AIPM-1 exam syllabus. The workflow, requirements, evaluation cases, controls, economics, and release plan agree with one another. The candidate identifies what is known, what is assumed, what must be tested, and who owns unresolved decisions. This is practical ownership readiness within the stated scope, with normal specialist support.

How to Prepare for the AIPM-1 Exam Syllabus

Use AI Product Practice / Portfolio Building to work through a realistic bounded problem. Read the model and pathway, practice discovery and requirements, create a small prototype or simulation, build evaluation cases, inspect traces, calculate costs, and conduct a simulated readiness review. A completed learning exercise is preparation evidence; the assessed case must still meet the published conditions.

Traditional Product Managers

Traditional PMs should concentrate on uncertain behavior, information quality, evaluation, and authority.

Software Engineers and Technical Professionals

Engineers should strengthen customer discovery, value, experience, adoption, and decision communication.

Candidates New to Product Work

Candidates new to product work should rehearse the full lifecycle with feedback before booking.

Business, UX, Consulting, and Agile Professionals

Business, UX, consulting, and Agile professionals should practice outcome ownership and technical evidence interpretation.

Representative Question Patterns and Examples

The following examples illustrate reasoning and scoring; they are not live exam items. Real forms should include plausible competing actions, sufficient decision evidence, and no artificial difficulty from ambiguous wording.

Example 1 — Release Evidence, Single Select

A policy assistant passes 90 of 100 normal tasks, but exposes another department’s restricted content in two access tests. The approved release rules prohibit cross-department exposure. What is the best next decision?

1. Launch because the overall task score exceeds the 85% target.
2. Raise the model size and repeat only the normal-task tests.
3. Pause release, correct the access path, and rerun authorization and regression tests.
4. Launch with a warning that users must not act on restricted answers.

Answer: C. Average task performance cannot satisfy an explicit authorization gate. The candidate must connect the failure to a corrective action and new evidence.

Example 2 — Economics, Single Select

In one pilot, 100 attempted tasks cost $20 for model/tool use and $30 for review and rework. Eighty tasks meet the agreed success criterion. What is cost per successful outcome for this cost scope?

1. $0.25
2. $0.50
3. $0.625
4. $0.375

Answer: C. ($20 + $30) / 80 = $0.625. Attempted tasks and inference-only cost are the wrong denominator and numerator for the stated scope.

Example 3 — Authority, Multiple Response

An assistant drafts order changes, but policy reserves submission for an authorized employee. Select all requirements that enforce this boundary.

1. Check the acting user’s permission before submitting a change.
2. Treat instructions inside retrieved customer notes as authorization.
3. Require explicit approval of the exact change before execution.
4. Record the approved action, approver, and execution result.
5. Give the assistant a shared administrator account to reduce failures.

Answer: A, C, D. These requirements bind the action to identity, explicit authority, and a traceable record. Retrieved content and broad credentials do not establish valid permission.

Employer Interpretation and Progression

Employers can read the AIPM-1 exam syllabus as a statement of the decisions a certified practitioner can defend. AIPM-1 is relevant to AI product manager roles owning bounded applications, copilots, or workflow features, and to defined product areas within integrated products. It can also support transitions from product ownership, analysis, engineering, UX, or consulting. Employers should review the decision pack and ask candidates to explain a failure, trade-off, release decision, and outcome measure.

AIPM-2 extends ownership to integrated products and major or bounded high-complexity areas. AIPM-3 adds strategic leadership of complex products, platforms, portfolios, and organizational capability. Progression should follow evidence of greater responsibility and outcomes; AIPM-1 is a professional standard in its own right.

Suggested Role Fit

Employers should combine AIPM-1 with interviews, decision-pack review, project evidence, product judgment assessment, and role-specific requirements.

Assessment Integrity, Delivery, and Version Transition

Online Examination

For AIPM-1 and AIPM-2, the online component is closed reference: no external generative AI, search, notes, or assistance. APD supplies any permitted calculator and necessary extracts. Each selected-response question is worth one point. Multiple-response items say “Select all that apply” without revealing the number of correct options. Credit requires selecting every correct option and no incorrect option; otherwise the item earns zero. There is no negative total score or partial credit.

Practical and Strategic Work

The case uses APD-supplied materials and an approved open-reference environment. Approved AI assistance is permitted only within the stated assessment rules. Candidates disclose tools and material assistance, retain relevant working evidence, verify factual and numerical claims, and take responsibility for every submission. Passing depends on candidate judgment and evidence, not the apparent polish of generated text.

Authenticity and Confidentiality

Identify personal contribution, collaborators, assumptions, sources, and whether results are observed, simulated, or projected. Anonymize confidential data while preserving decision context. Do not submit proprietary materials without permission. Fabricated evidence, undisclosed substitution of another person’s work, or prohibited assistance is handled under the published integrity and appeal policy.

Delivery and Fairness

APD must disclose the version, permitted tools, assessment windows, scoring rules, accommodations, retake arrangements, and appeal route before registration. Approved accommodations may change timing or format while preserving the capability standard. Case environments must provide equivalent information and access; vendor outages and tool setup failures must not become hidden competency tests.

Retakes and Review

Candidates receive component-level results and criterion-level feedback. A failed practical component requires corrected evidence and reassessment; a failed online component requires another equivalent form. APD’s published candidate policy determines scheduling, fees, retention of passed components, and appeal deadlines. No automatic deadline or entitlement is created by this syllabus.

Transition

Version 1.2 requires its full assessment design. Earlier online-only passes cannot be represented as completion of the new applied-evidence requirement. Existing credentials retain the rules under which they were issued; any bridge or renewal route must be published separately. The item-writing guides, banks, scoring configuration, case packs, assessor calibration materials, and public booking information must be aligned before v1.2 delivery. This document updates the syllabus; it does not assert that those operational changes are already deployed.

Validity

Certification validity is two years. Renewal follows APD’s published policy and may include reassessment, continuing professional development, updated evidence, or an approved bridge assessment.

Summary

AIPM-1 is designed to validate professional-practice readiness for AI product management at C1 ownership and defined C2 scope.

It confirms that candidates can qualify an opportunity, design a trustworthy human-AI workflow, make informed model and data decisions, build evaluation evidence, judge release readiness, manage risk and governance proportionately, and defend an integrated product decision with evidence.

AIPM-1 is the starting point of the APD Certified AI Product Manager pathway and the professional foundation for AIPM-2 (Advanced Professional Practice) and AIPM-3 (Strategic Product Leadership).