PMI Professional in AI and Machine Learning Exam Practice Test

140 questions available

Preparation for PMI AI/ML professional certification covering AI project management, data literacy, model evaluation, ethical AI, and managing AI-enabled projects. Administered by PMI. Key domains include Identify Business Needs and Solutions, Identify Data Needs, Operationalize AI Solution and Manage AI Model Development and Evaluation. The exam consists of 120 questions over 160 minutes.

Certification exam

120 Exam questions

2 hours 40 minutes Time Limit

Professional Level

Practice bank

140 Practice Questions

2 hours 20 minutes Practice Time

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The bank 140 Practice questions checked against the official objectives.

qf-import140 practice questions61 answers with a checkable referenceBlueprint 1.0Bank updated 2026-05-04

Sample Questions

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61 of 140 answers carry a checkable reference.

Identify Data Needs

A public health agency wants to predict disease outbreaks using clinic visits, pharmacy sales, school absenteeism, and social-media trends. Some sources are public, some require data-sharing agreements, and social data may be noisy. What acquisition plan is best?

Identify Business Needs and Solutions

A factory sponsor wants a generative AI maintenance assistant that answers technician questions. Technicians work offline in noisy areas, manuals are inconsistent by equipment revision, and wrong torque guidance could cause safety incidents. Which adoption risk should drive the solution design?

Support Responsible and Trustworthy AI Efforts

A defense contractor uses an AI summarizer for bid documents. The model may ingest controlled technical information, export-controlled terms, and subcontractor proprietary data. The proposal manager wants to use a public hosted model to meet a deadline. What is the best security response?

Operationalize AI Solution

A model registry shows the production pricing model, but the feature pipeline is changed directly in an orchestration script with no version tag. After a margin incident, no one can reproduce the exact inputs used. What governance practice is missing?

Identify Data Needs

A bank’s AI team wants to use third-party small-business cash-flow data to improve credit scoring. The data provider offers broad industry coverage but cannot document consent for older records before 2024. What acquisition strategy best protects the project?

Why This Certification Opens Doors

In the real world, the majority of AI project failures stem not from flawed algorithms, but from poor project scoping, misaligned expectations, and inadequate governance. This certification matters because it equips professionals to prevent these failures. It provides a proven framework for translating business problems into viable AI projects, managing interdisciplinary teams, mitigating risks like bias and data drift, and ensuring AI solutions deliver measurable ROI and operate within ethical boundaries. It transforms theoretical ML knowledge into actionable, project-driven execution.

Exam Blueprint

Each domain is weighted to match the real certification exam, so a full practice simulation predicts your result.

01Identify Business Needs and Solutions

26%

02Identify Data Needs

26%

03Operationalize AI Solution

17%

04Manage AI Model Development and Evaluation

16%

05Support Responsible and Trustworthy AI Efforts

15%

Exam Details PMI-PfQ | 2 hours 40 minutes

Exam Code PMI-PfQ

Vendor qf-import

Time Limit 2 hours 40 minutes

Exam questions 120

Frequently Asked Questions

I am a project manager with limited coding experience. Is this exam for me?

Yes, absolutely. This certification is specifically designed for professionals like you. While you need to understand the capabilities, limitations, and lifecycle of AI/ML models, the exam focuses on managing the projects that build them, not on writing the code itself. Your project management expertise is the primary foundation.

How does this differ from a pure data science certification?

A data science certification typically validates deep technical skills in statistics, programming, and algorithm building. The PMI AI/ML exam validates the integrative skills needed to manage the entire project ecosystem surrounding those algorithms-securing resources, defining business value, managing risks, and leading the cross-functional team that includes data scientists, engineers, and business units.

What is the recommended preparation path?

PMI provides an authoritative exam content outline and recommended reading list. Start there. Combine formal study guides with practical resources on AI ethics and case studies of AI project deployments. Engaging in discussion forums with other candidates can also provide valuable perspective on applying the concepts.

Will this certification help me transition into AI project management?

It is a powerful credential for such a transition. It signals to employers that you have taken structured, proactive steps to understand the unique paradigm of AI/ML projects. It provides a common language and framework you can reference in interviews to discuss how you would approach leading such initiatives.

Are there prerequisites to sit for the exam?

You should review the current official requirements on PMI's website, as they are subject to change. Typically, prerequisites involve a combination of formal education and professional experience in project management and/or working with AI/ML teams, ensuring candidates have the necessary foundational context.

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