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| Certification Vendor: | PMI (Project Management Institute) |
|---|---|
| Exam Name: | PMI Certified Professional in Managing AI |
| Exam Number: | PMI-CPMAI |
| Related Certifications: | PMI-PBA (Professional in Business Analysis) PMI-ACP (Agile Certified Practitioner) PMP (Project Management Professional) |
| Available Languages: | English |
| Certificate Validity Period: | 3 years |
| Passing Score: | Not publicly disclosed |
| Exam Price: | USD $520 for PMI members / USD $670 for non-members |
| Exam Format: | Multiple Choice |
| Exam Duration: | 150 minutes |
| Real Exam Qty: | 120 |
| Sample Questions: | PMI PMI-CPMAI Sample Questions |
| Exam Way: | Computer-based testing at PMI-authorized Pearson VUE test centers worldwide |
| Pre Condition: | No mandatory prerequisites. However, basic project management knowledge (PMP or equivalent experience) is recommended. Secondary school diploma required if pursuing PMI membership. |
| Official Syllabus URL: | https://www.pmi.org/certifications/certified-professional-managing-ai-cp-ai |
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NEW QUESTION # 118
An AI team is defining success criteria for a customer support chatbot. Leadership wants to approve the project but needs objective measures that reflect both business value and risk. Which set of metrics is most appropriate?
Answer: B
Explanation:
PMI-CPMAI emphasizes establishing acceptable performance metrics and aligning AI outcomes to business value while ensuring responsible and trustworthy practices. For chatbots, business value includes deflection
/containment (how many issues are resolved without human agents), customer experience (satisfaction), and operational performance (latency). Risk measures must also be included because trustworthy AI requires governance and compliance controls (privacy/security, transparency, accountability). Therefore, metrics that combine outcomes and controls-user satisfaction, containment, correct escalation/hand-off, and privacy
/compliance incident rates-are the most PMI-aligned set. Response time alone (A) misses quality and risk.
Features delivered (C) and lines of code (D) are delivery activity measures, not AI value or trust measures.
PMI's approach encourages metrics that support go/no-go decisions and lifecycle monitoring, making option B the best fit.
NEW QUESTION # 119
After completing an AI project, the project manager begins preparing the final report and reflecting on lessons learned. They identified that the project team lacked sufficient AI and data knowledge.
If adequate knowledge was available, how would the result be different?
Answer: D
Explanation:
The best answer is D. The AI project team would have required less external consultation . PMI's CPMAI exam content outline explicitly includes identifying project resources, assessing skill requirements for AI project team composition , and identifying gaps in needed capabilities. That means PMI expects project managers to recognize when internal AI and data expertise is insufficient and when outside specialists, contractors, or other support may be needed to fill those gaps. If the team already had adequate AI and data knowledge, the most direct difference would be reduced dependence on external experts or consultants.
The other options are weaker because they are less certain. Better knowledge can help governance, schedule, and even model performance, but those outcomes also depend on many other factors such as data quality, stakeholder alignment, tooling, and deployment conditions. PMI's framework is careful about linking capability gaps to resourcing and staffing decisions rather than automatically assuming improvements in accuracy or timeline. So the clearest PMI-aligned lesson learned is that stronger in-house knowledge would have reduced the need to seek outside assistance. That interpretation is also consistent with the broader PMI emphasis on building the right team capability mix for AI initiatives before and during delivery.
NEW QUESTION # 120
A team is in the early stages of an AI project. They need to ensure they have the necessary data and technology to support AI solution development.
What is the first step the project team should complete?
Answer: A
Explanation:
In the PMI-CP in Managing AI guidance, early AI project work includes confirming that the data foundation is viable before committing to specific tools or architectures. For AI initiatives, data is the primary constraint: if the right data does not exist, is incomplete, or is of low quality, no choice of technology will rescue the solution. Therefore, before assessing tooling gaps or even detailing the technology stack, teams are expected to verify the availability, accessibility, and quality of the required data for the intended use case.
PMI-CPMAI describes data readiness activities such as identifying key data sources, profiling them for completeness and consistency, assessing coverage of relevant populations and time periods, and checking for legal and regulatory constraints around access and use. Only after this verification can the team meaningfully evaluate whether existing platforms, infrastructure, and tools are sufficient, and then identify gaps.
Assessing team expertise or procuring tools are important, but they follow from the prior understanding of what data exists and what is needed for the model. Thus, the first step the project team should complete to ensure they have what they need for AI development is to verify the availability and quality of the required data.
NEW QUESTION # 121
A healthcare organization plans to develop an AI-driven diagnostic tool. To define the required data, the project manager needs to ensure data consistency and accessibility.
Which method should the project manager use?
Answer: B,D
Explanation:
CPMAI's Data Understanding and Data Preparation phases stress that AI success in domains like healthcare depends on robust data pipelines that ensure consistency, quality, and accessibility before modeling begins. Guidance describes these phases as profiling and assessing data, then performing cleaning, transformation, and structuring so that data are reliable and usable by downstream models.
A data quality assessment combined with ETL (extraction, transformation, loading) processes directly supports these objectives. ETL pipelines standardize formats across disparate systems, enforce validation rules, manage missing values, harmonize coding schemes (for example, diagnosis codes), and centralize data into accessible stores. This is exactly the kind of foundational work CPMAI describes as a prerequisite to effective model development, particularly in regulated sectors such as healthcare where inconsistent or inaccessible data can have clinical and regulatory consequences.
By contrast, using NLP to standardize records (B) is a specialized technique that may help later but does not replace a systematic quality and ETL process. Integrating EHR with ML algorithms (C) and designing hybrid cloud storage (D) are more about later technical integration and infrastructure than about defining and ensuring initial data consistency and accessibility. Thus, in line with CPMAI's data-centric guidance, performing a data quality assessment with ETL processes is the correct method, making option A the best answer.
NEW QUESTION # 122
A government agency is using an AI system to analyze public data for policymaking decisions. The project manager needs to address risks related to data accuracy, privacy, and misuse. What represents the highest risk to the agency?
Answer: A
Explanation:
Within PMI-CPMAI's "Support Responsible and Trustworthy AI Efforts," privacy and security are treated as core, high-severity risks because they can trigger regulatory violations, reputational damage, and harm to individuals. PMI explicitly calls out the need to establish a privacy/security plan with encryption and access controls, privacy impact assessments, and secure handling of personally identifiable information (PII) across the AI lifecycle. If user data is stored in an unsecured database, the agency faces immediate exposure to breach, unauthorized access, and misuse-risks that are typically higher impact than stale data, vendor reliance, or even lack of transparency. In PMI guidance on AI data life cycle management, prolonged retention and weak security increase breach likelihood over time, making insecure storage a critical vulnerability that undermines trust and compliance. While transparency gaps are serious (PMI also emphasizes explainability requirements and audit trails), a direct security failure that exposes user data is generally the most acute and consequential risk because it can cause harm quickly and irreversibly, and it can halt the program through legal and policy intervention.
NEW QUESTION # 123
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