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Web-Based Practice Test PMI PMI-CPMAI Exam Questions
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PMI Certified Professional in Managing AI Sample Questions (Q94-Q99):NEW QUESTION # 94
In an aerospace project focused on predictive maintenance using AI, the project team is facing challenges in coordinating the AI models' operationalization across various manufacturing sites. Strong governance and corporate guardrails are established, but each site has different computational capabilities and network latencies.
What is an effective method that helps to ensure consistent AI performance across these sites?
- A. Implementing a centralized AI model repository
- B. Operationalizing a decentralized AI architecture
- C. Using site-specific AI model tuning
- D. Utilizing cloud-based AI services uniformly
Answer: D
Explanation:
PMI-CPMAI's guidance on AI operationalization and MLOps highlights the importance of consistency and reliability across deployment environments, especially in distributed or multi-site organizations. In this aerospace predictive maintenance scenario, each manufacturing site has different computational capacity and network characteristics, which can lead to inconsistent model performance and latency if models are hosted and executed locally. To mitigate this, PMI-aligned practices emphasize standardizing the runtime environment and centralizing critical AI services wherever feasible.
By utilizing cloud-based AI services uniformly, the organization can ensure that all sites call the same models, same versioning, same configuration, and same infrastructure stack, regardless of local hardware constraints. This reduces variability in inference behavior, simplifies monitoring, and supports unified logging, performance tracking, and governance enforcement across sites. A centralized model repository alone does not standardize execution; it only manages artifacts. Decentralized architectures and extensive site-specific tuning tend to increase divergence and complexity, making performance less consistent. Therefore, the most effective method to help ensure consistent AI performance across sites with different local capabilities is to utilize cloud-based AI services uniformly as the operational backbone.
NEW QUESTION # 95
A government project plans to implement an AI-based fraud detection system and the project team needs to define the success criteria. They identified potential improvements in detection accuracy, reduction in investigation time, and cost savings as key performance indicators (KPIs). However, they are unsure how to effectively quantify these KPIs.
Which two approaches should be used? (Choose 2)
- A. Rely on only qualitative feedback from stakeholders
- B. Establish a baseline using historical data comparisons
- C. Implement a continuous performance monitoring system
- D. Set fixed performance targets based on theoretical models
- E. Use random benchmarks without industry comparison
Answer: B,C
Explanation:
For an AI-based fraud detection system, PMI-CPMAI-aligned guidance on benefits realization and performance management stresses that success metrics must be quantified against a clear baseline and monitored continuously over time. To properly define and measure KPIs such as detection accuracy, reduced investigation time, and cost savings, the project team should first establish a baseline using historical data comparisons (D). That means analyzing historical fraud cases, prior detection rates, average investigation duration, and historical financial losses to understand "pre-AI" performance. This provides a reference point against which improvements can be measured in a verifiable way.
In addition, PMI-CPMAI emphasizes continuous performance monitoring (B) as part of AI lifecycle governance. Fraud patterns, transaction volumes, and user behavior evolve, so model performance relative to KPIs must be tracked on an ongoing basis using dashboards and periodic evaluations. This supports early detection of performance degradation, allows recalibration of thresholds, and validates that business benefits (e.g., decreased losses, reduced workload) are being sustained.
Relying only on qualitative feedback, random benchmarks, or purely theoretical targets does not meet PMI-CPMAI expectations for evidence-based measurement and governance. Therefore, the two appropriate approaches are: implementing a continuous performance monitoring system (B) and establishing a baseline using historical data comparisons (D).
NEW QUESTION # 96
A project manager is tasked with ensuring that an AI project complies with data regulations before data collection begins. This involves identifying all necessary requirements for trustworthy AI, including ethical considerations, privacy, and transparency.
What should the project manager do first?
- A. Draft a detailed data governance framework to be reviewed later
- B. Perform a comprehensive assessment of data regulations and compliance requirements
- C. Develop a high-level strategy for data collection and aggregation
- D. Schedule a meeting with stakeholders to discuss potential data collection compliance issues
Answer: B
Explanation:
For AI projects handling regulated data (such as financial or personal information), PMI-aligned guidance for Managing AI emphasizes that regulatory and compliance requirements must be understood upfront, before data is collected, processed, or shared. The very first step is to perform a comprehensive assessment of data regulations and compliance requirements across all applicable jurisdictions (e.g., privacy laws, banking/financial regulations, sectoral rules, cross-border data transfer constraints, retention rules, and consent requirements).
This assessment provides the foundation for trustworthy AI, because ethical principles, privacy safeguards, transparency mechanisms, and accountability structures must map directly to concrete legal and regulatory obligations. Only when these requirements are clearly identified can the project manager design an appropriate data governance framework, define lawful bases for processing, set access controls, and specify documentation and audit-trail expectations.
Drafting governance (option B), stakeholder meetings (option C), or high-level data collection strategies (option D) are useful later steps, but if they are done before a regulatory and compliance assessment, they risk misalignment with the law and may require costly rework. Therefore, in line with PMI-CPMAI's focus on responsible and compliant AI lifecycle management, the project manager should first perform a comprehensive assessment of data regulations and compliance requirements.
NEW QUESTION # 97
A logistics company wants to optimize its delivery routes while adapting to real-time traffic conditions.
Which AI pattern or patterns meet these goals?
- A. Conversational
- B. Automation and rule-based systems
- C. Predictive analytics
- D. Recognition and content summarization
Answer: C
Explanation:
Within CPMAI and PMI's AI pattern framing, predictive analytics is the pattern that focuses on using historical and real-time data to forecast future states-exactly what is needed for route optimization under changing traffic conditions. For a logistics company, the AI system must estimate future travel times, congestion levels, delays, and likely delivery windows. These predictions are then used as inputs to optimization logic that chooses the best routes and adjusts them dynamically as new data arrives.
Recognition/summarization patterns focus on classification or extracting meaning from content (such as images or text), while conversational patterns are aimed at dialog systems like chatbots. Automation and rule-based systems can encode fixed routing rules, but they cannot by themselves learn patterns from historical traffic and adapt to evolving conditions. PMI/CPMAI guidance highlights that when the business problem involves forecasting outcomes to inform better decisions, the appropriate AI pattern is predictive analytics-often implemented with regression, time-series models, or more advanced learning approaches. Therefore, for optimizing delivery routes while adapting to real-time traffic, the correct pattern is predictive analytics, making option D the appropriate choice.
NEW QUESTION # 98
A hospital wants to develop a medical records system with the primary goal of minimizing or eliminating paper records. They have identified where the cognitive AI solution will be applied. In addition, business objectives have been quantified and key performance indicators (KPIs) have been determined.
What else needs to be done to progress to the next Cognitive Project Management for AI (CPMAI) phase?
- A. Explore external data sources
- B. Determine the project ROI
- C. Begin prototype development
- D. Create interdepartmental strategies
Answer: B
Explanation:
CPMAI's Phase I - Business Understanding focuses on clearly defining the business problem, aligning AI efforts with organizational goals, and establishing measurable success criteria including ROI expectations. PMI's own overview of CPMAI notes that in this phase, teams should "set success criteria" and define both KPIs and ROI expectations so that everyone understands what success and failure look like before moving on Other CPMAI-oriented resources describe Phase I artefacts such as a problem statement, AI pattern fit, stakeholder analysis, and a preliminary ROI sheet that quantifies expected benefits and costs. In the scenario, the hospital has already identified where the cognitive solution will be applied, quantified business objectives, and defined KPIs. What is still missing from the core Phase I deliverables is a clear view of the project's expected ROI, linking reduced paper records and process improvements to financial and operational value.
Beginning prototype development (B) belongs to later modeling phases, exploring external data sources (D) is part of Data Understanding, and interdepartmental strategies (C) are broader organizational actions rather than a specific Phase I gating item. To progress to the next CPMAI phase in a way that matches the methodology, the team must determine the project ROI, making option A the correct answer.
NEW QUESTION # 99
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