PMI PMI-CPMAI Exam Dumps - Actual Questions Answers

PMI-CP–oriented AI risk and resilience practices emphasizecontinuity of service and graceful degradationwhen 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. Anautomated 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 automaticallyswitches 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 arehuman-dependentand slower to activate. PMI-style AI contingency planning stressesautomated, pre-defined fallback pathswherever possible. Hence, developing anautomated fallback chatbot with limited capabilitiesbest addresses the objective of handling potential system downtimes.

Once data preparation is complete and the team is ready for model development, PMI-aligned AI lifecycle guidance calls forclear definition and documentation of performance metrics and success criteria before training models. The project manager should ensure that everyone agrees onwhich metrics will be used (e.g., accuracy, precision, recall, F1, AUC, business KPIs)and what thresholds will be considered acceptable. This supports traceability, objective evaluation, and transparent go/no-go decisions in later stages.

Because the question states that the data is already prepared and the team is ready to proceed, it implies that initial data quality activities have already occurred. Repeating a “final assessment of data quality” (option A) is less critical at this specific point than locking inevaluation metrics. Go/no-go questions (option C) and scalability reporting (option D) depend on having those metrics explicitly defined; they are downstream decisions and artifacts. PMI-style AI guidance stresses thatmodel development should be driven by pre-defined, documented performance metrics that connect technical outputs to business value and risk tolerances. Therefore, the next action for the project manager is todocument the performance metrics for the model.

In CPMAI’sData Understandingphase, the methodology emphasizes identifying data sources, ownership, quality, and the people who truly understand those data assets. Data subject matter experts (SMEs) are not defined purely by generic analytics skills or by having worked on AI before; they are defined bydeep familiarity with the specific datasets and domain contextthat drive the AI solution.

For predictive policing, the key datasets are historical crime data, socioeconomic data, and real-time incident reports. CPMAI guidance stresses that teams must understand how these datasets are generated, what biases they may contain, their limitations, and how they relate to the real-world processes they represent. Therefore, the best way to identify appropriate data SMEs is toevaluate who on the team (or in the wider organization) already has strong familiarity with these concrete data sources, their structures, and usage history.

Options focusing on prior AI tools, workshops on a single data stream, or generic analytics certifications do not guarantee deep, source-specific knowledge. Aligning with CPMAI’s data-centric approach,evaluating the team’s familiarity with historical crime and socioeconomic datais the most appropriate method, making option C correct.