PMI-CPMAI Dumps With Exact Questions and Answers

For an AI-based fraud detection system, PMI-CPMAI–aligned guidance on benefits realization and performance management stresses that success metrics must bequantified 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 firstestablish 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 emphasizescontinuous 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)andestablishing a baseline using historical data comparisons (D).

PMI-CPMAI treatsdata privacy, governance, and securityas central pillars of responsible AI, highlighting that AI projects often deal with sensitive and regulated information.LPCentre+1When evaluating threats that could lead tounauthorized accessto sensitive aerospace manufacturing data, the framework encourages looking atattack surface, distribution of data, and control complexity.

A decentralized data storage system (option C) significantly increases the potential risk: data is distributed across multiple locations or nodes, making consistent access control, identity management, logging, and incident response more challenging. Misconfigurations or weak endpoints in such an environment can create numerous entry points for attackers, magnifying exposure of proprietary designs, safety-critical parameters, or personal data. PMI-CPMAI’s guidance on data governance stresses centralized policies, clear stewardship, and controlled data flows precisely to reduce this risk.

By contrast, proprietary software with no open-source review (A) may present transparency concerns but does not inherently imply broader data exposure. Lack of regular data updates (B) is more a model performance and drift issue than a direct security threat. Option D describes amitigation—securing APIs and enforcing governance—not a risk. Therefore, thehighestsecurity risk for unauthorized access in this scenario isoperationalizing a decentralized data storage system.

Within PMI-CPMAI, one of the key strategic levers for AI projects isreusing existing AI assets, including pretrained models, to accelerate delivery and reduce initial development complexity. PMI describes pretrained and foundation models as allowing organizations to “leverage previously learned representations so that teams can focus effort on adaptation, integration, and value realization rather than building models from scratch.” This often results in ashorter experimentation cycle, reduced training time, and faster deployment, especially when speed-to-market is a primary objective.

PMI emphasizes that such reuse is particularly valuable in early iterations or minimum viable products (MVPs), where the aim is to “deliver functional AI capability quickly, validate value hypotheses, and gather user feedback.” While the team still needs to handle integration, fine-tuning, and risk controls, the heavy lifting of initial training on massive datasets has already been done by the pretrained model provider. This is contrasted with full custom model development, which PMI characterizes as more resource-intensive and time-consuming, requiring substantial data preparation, training, and optimization. Potential challenges such as compatibility or scalability must be managed, but they are not thekey, primary effectidentified by PMI. The most central and intended result of using pretrained models in this context is thatthe overall project timeline is reduced, enabling the company to reach the market faster.