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| Certification Vendor: | PMI |
|---|---|
| Exam Name: | PMI Certified Professional in Managing AI (CPMAI) Exam |
| Exam Number: | CPMAI |
| Available Languages: | English |
| Exam Format: | Multiple choice |
| Sample Questions: | PMI PMI-CPMAI Sample Questions |
| Pre Condition: | No formal prerequisite is publicly standardized; PMI recommends familiarity with project management and AI concepts. |
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NEW QUESTION # 68
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 # 69
An insurance company ' s AI system analyzes user data to personalize premium rates.
During a risk assessment, the project team learns that the system has a strong tendency to exclude high-risk individuals based on biased historical data.
What should the project manager do?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From PMI Certified Professional in Managing AI Documents:
The best answer is C. Revise the premium rate information while redesigning the AI training process.
PMI-CPMAI emphasizes fairness, bias mitigation, responsible data practices, and continuous validation throughout the AI lifecycle.
In this scenario, the project team has identified biased historical information that may influence the model ' s decisions and cause inappropriate exclusion of certain customer groups.
The project manager should coordinate a structured review of training data, relevant rating factors, model objectives, and evaluation criteria to identify the causes of potentially unfair outcomes.
Corrective actions may include improving data representativeness, examining proxy variables, revising feature selection, applying appropriate fairness constraints, and retraining the model using validated information.
The team must also consider applicable insurance regulations and distinguish legitimate risk-based underwriting factors from unlawful or unjustified discrimination.
Why the other options are incorrect:
- A: Avoiding AI entirely during discovery does not directly address the identified model bias.
- B: Explainability improves transparency but does not automatically eliminate discriminatory outcomes.
- C: Correct because it targets the biased information and model training process.
- D: Deliberately excluding individuals does not constitute a responsible bias mitigation strategy.
PMI-CPMAI ' s trustworthy AI principles require teams to identify, evaluate, mitigate, and monitor unfair model behavior.
The corrected model should undergo appropriate fairness testing and regulatory review before being approved for operational use.
NEW QUESTION # 70
An insurance company is selecting an AI approach to automate simple claim approvals for low-risk cases.
The organization wants the system to take actions with minimal human intervention based on predefined policies. Which AI capability best fits?
Answer: A
Explanation:
In PMI's Seven Patterns of AI, capability selection depends on whether the system is primarily advising humans or acting on their behalf. When the goal is to automate operational actions-approving or routing claims under policy constraints with minimal human intervention-the capability aligns with autonomous systems, which emphasize automated execution within defined rules, safeguards, and operational boundaries.
Predictive analytics (B) can score risk, but it typically supports decision support; autonomous systems extend this by taking actions automatically according to governance-approved policies. PMI-CPMAI's responsible and trustworthy AI principles reinforce that higher-autonomy use cases require stronger controls: clear escalation paths, contingency plans, monitoring, and audit trails to ensure accountability for automated decisions. Conversational (A) and hyperpersonalization (D) do not fit the core need of automated adjudication. Therefore, autonomous systems is the best match for low-risk auto-approvals with predefined guardrails.
NEW QUESTION # 71
An AI project team is in the process of designing a security plan. The team needs to consider various aspects such as transparency, explainability, and compliance with data regulations.
Which action should the project manager take?
Answer: B
Explanation:
In PMI-CPMAI, security planning for AI solutions goes beyond traditional technical controls; it explicitly includes transparency, explainability, and regulatory compliance as part of a responsible AI posture. The guidance states that security and trust in AI depend not only on encryption, access control, and infrastructure hardening, but also on whether stakeholders can understand how decisions are made and whether those decisions comply with applicable laws and policies.
PMI's AI management perspective includes requirements for explainable and auditable decision-making, particularly in public-sector and high-impact domains. This means designing systems so that model behavior can be interpreted, key features and factors identified, and decisions documented in a way that regulators, auditors, and affected users can review. The project manager is therefore expected to ensure that the AI system's design and governance support transparency and explainability, in addition to technical security controls.
Focusing only on technical measures or assuming compliance without review contradicts PMI-CPMAI's emphasis on proactive governance and legal/ethical due diligence. Reliance solely on encryption addresses confidentiality but not fairness, accountability, or understandability. Thus, the correct action is to ensure the AI system's decisions are transparent and explainable, embedded alongside other security and compliance safeguards.
NEW QUESTION # 72
A government agency is implementing an AI-powered tool to enhance data security through anomaly detection. The project manager is assembling the team. To identify the subject matter experts (SMEs) who can provide the best insights and contributions to this project, the project manager needs to consider their experience and expertise in various technical domains.
Which method will help identify the qualified data SMEs?
Answer: D
Explanation:
PMI-CPMAI distinguishes clearly between different types of expertise needed in an AI project: AI/ML specialists, data specialists (data SMEs), domain SMEs, and security or infrastructure experts. When the question specifically asks about data subject matter experts (SMEs), the focus is on people who deeply understand how the organization's data is structured, stored, accessed, and governed.
For an AI-powered anomaly detection tool in a government data security context, qualified data SMEs are those who know the existing data architectures, logging systems, data flows, schemas, and constraints. They can explain where relevant data resides (e.g., network logs, access records, system events), how it is currently managed and protected, and what limitations or quality issues may affect AI performance. Evaluating candidates on their expertise with existing data architectures and their ability to optimize databases directly targets this competency.
Knowledge of neural networks, hyperparameter tuning, or GANs is more characteristic of AI/ML engineers, not data SMEs. PMI-CPMAI guidance emphasizes that AI success depends on the right mix of roles, and data SMEs are vital for defining data requirements, ensuring data suitability, and aligning with security and governance standards. Therefore, the method that best identifies the appropriate data SMEs for this anomaly detection project is to evaluate their expertise with current data architectures and their ability to optimize and manage those data systems.
NEW QUESTION # 73
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