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PMI PMI-CPMAI Exam Syllabus Topics:

TopicDetails
Topic 1
  • Iterating Development and Delivery of AI Projects (Phase IV): This section of the exam measures the skills of an AI Developer and covers the practical stages of model creation, training, and refinement. It introduces how iterative development improves accuracy, whether the project involves machine learning models or generative AI solutions. The section ensures that candidates understand how to experiment, validate results, and move models toward production readiness with continuous feedback loops.
Topic 2
  • Managing Data Preparation Needs for AI Projects (Phase III): This section of the exam measures the skills of a Data Engineer and covers the steps involved in preparing raw data for use in AI models. It outlines the need for quality validation, enrichment techniques, and compliance safeguards to ensure trustworthy inputs. The section reinforces how prepared data contributes to better model performance and stronger project outcomes.
Topic 3
  • Operationalizing AI (Phase VI): This section of the exam measures the skills of an AI Operations Specialist and covers how to integrate AI systems into real production environments. It highlights the importance of governance, oversight, and the continuous improvement cycle that keeps AI systems stable and effective over time. The section prepares learners to manage long term AI operation while supporting responsible adoption across the organization.
Topic 4
  • Matching AI with Business Needs (Phase I): This section of the exam measures the skills of a Business Analyst and covers how to evaluate whether AI is the right fit for a specific organizational problem. It focuses on identifying real business needs, checking feasibility, estimating return on investment, and defining a scope that avoids unrealistic expectations. The section ensures that learners can translate business objectives into AI project goals that are clear, achievable, and supported by measurable outcomes.
Topic 5
  • The Need for AI Project Management: This section of the exam measures the skills of an AI Project Manager and covers why many AI initiatives fail without the right structure, oversight, and delivery approach. It explains the role of iterative project cycles in reducing risk, managing uncertainty, and ensuring that AI solutions stay aligned with business expectations. It highlights how the CPMAI methodology supports responsible and effective project execution, helping candidates understand how to guide AI projects ethically and successfully from planning to delivery.
Topic 6
  • Identifying Data Needs for AI Projects (Phase II): This section of the exam measures the skills of a Data Analyst and covers how to determine what data an AI project requires before development begins. It explains the importance of selecting suitable data sources, ensuring compliance with policy requirements, and building the technical foundations needed to store and manage data responsibly. The section prepares candidates to support early data planning so that later AI development is consistent and reliable.

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PMI Certified Professional in Managing AI Sample Questions (Q73-Q78):

NEW QUESTION # 73
A government agency plans to increase personalization of their AI public services platform. The agency is concerned that the personal information may be hacked.
Which action should occur to achieve the agency's goals?

Answer: A

Explanation:
PMI's guidance on responsible and trustworthy AI highlights data privacy, security, and protection of personal information as central when deploying AI in public-sector services. For personalization in e-government platforms, PMI notes that organizations must "design AI solutions that safeguard personally identifiable information (PII) and comply with applicable privacy regulations," because public trust is especially fragile in government contexts. Strengthening privacy controls-through techniques such as data minimization, access controls, encryption, anonymization/pseudonymization, and robust cybersecurity practices-is described as a direct way to protect citizens and maintain confidence in AI-enabled services.
The PMI-CPMAI materials also emphasize that user trust is a prerequisite for adoption, particularly when AI uses sensitive personal or behavioral data. They state that AI programs should "embed privacy-by-design and security-by-design into architectures and workflows so that personalization does not compromise confidentiality or expose citizens to heightened risk." While standardizing protocols, educating employees, and improving interfaces have value, they do not address the agency's specific concern about hacking and misuse of personal data. Enhancing data privacy and security directly aligns with both the risk concern (hacking) and the strategic goal (personalized services that users trust), making it the action most consistent with PMI's responsible AI and data governance guidance.


NEW QUESTION # 74
A government agency plans to implement a new AI-driven solution for automating risk analysis. The project team needs to ensure that all stakeholders accept the solution and the project scope is well-defined. They must identify whether the AI approach is the best solution compared to traditional methods.
Which method meets this objective?

Answer: D

Explanation:
In the CPMAI-aligned approach, before committing to an AI solution, teams perform a structured AI go/no- go assessment to determine whether AI is actually the right tool compared with traditional analytical or rules- based methods. This assessment looks at data readiness, technical feasibility, business value, risk, and alignment with stakeholder expectations. It is also where the project scope is clarified and boundaries are set:
what problems AI will address, what remains non-AI, and what success looks like in measurable terms.
CPMAI and PMI-style AI guidance emphasize that you should not jump directly into model building or specific architectures before you have answered the fundamental question: "Is AI the appropriate approach here, given our data and constraints?" The go/no-go assessment explicitly compares AI options with conventional solutions, evaluates whether available data is sufficient and usable, and highlights ethical, regulatory, and operational risks. This process provides a transparent, evidence-based decision that helps gain acceptance from stakeholders because they see that AI was chosen (or rejected) after a systematic evaluation.
Therefore, performing a comprehensive AI go/no-go assessment focusing on technology and data factors is the method that best meets the objective.


NEW QUESTION # 75
An aerospace company is integrating AI for predictive maintenance. The project manager is concerned about potential delays due to external dependencies.
Which initial step should the project manager take?

Answer: C


NEW QUESTION # 76
A project manager is preparing a contingency plan for an AI-enabled underwriting platform. During outages, the business must still make time-sensitive decisions. What strategy best supports business continuity?

Answer: D

Explanation:
PMI-CPMAI highlights the need to manage AI operational risks through structured contingency planning and trustworthy AI governance. A business continuity-aligned contingency strategy is a manual override with clear escalation and decision rules so critical underwriting decisions can continue when the AI platform is unavailable. This is consistent with CPMAI expectations for operational readiness and accountability: define alternate operating modes, ensure decision traceability, and maintain service reliability despite disruptions.
Stopping all underwriting (B) fails the "must still decide" requirement. Running without monitoring (C) violates trustworthy AI controls and increases the chance of unnoticed failures or harmful decisions.
Marketing (D) does not address continuity of operations. A defined manual override aligns with governance principles by preserving accountability and ensuring the organization can meet obligations during system downtime.


NEW QUESTION # 77
A project manager is preparing a contingency plan for an Al-driven customer service platform. They need to determine an effective strategy to handle potential system downtimes.
Which strategy addresses the project manager's objective?

Answer: D

Explanation:
PMI-CP-oriented AI risk and resilience practices emphasize continuity of service and graceful degradation when AI systems fail or are temporarily unavailable. For an AI-driven customer service platform, the contingency plan should ensure that customers still receive some level of assistance even when the main AI system is down. An automated fallback chatbot with limited capabilities (option C) embodies this principle by providing a simplified yet always-available channel.
Such a fallback system might offer only basic FAQs, simple intent handling, or routing to human agents, but it maintains a consistent experience and avoids a complete service outage. This is a classic "fail-soft" or
"degraded mode" strategy often highlighted in AI operations and MLOps guidance: if the primary model or service is unavailable, the system automatically switches to a simpler, more reliable backup.
Logging systems (option A) are important for diagnosis but do not directly serve customers during downtime.
Manual override for critical queries (option B) and extensive staff training (option D) are valuable complementary controls, yet they are human-dependent and slower to activate. PMI-style AI contingency planning stresses automated, pre-defined fallback paths wherever possible. Hence, developing an automated fallback chatbot with limited capabilities best addresses the objective of handling potential system downtimes.


NEW QUESTION # 78
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