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| Section | Objectives |
|---|---|
| Topic 1: AI Governance, Ethics, and Risk | - Responsible AI principles and ethical considerations - Risk management, compliance, and regulatory alignment |
| Topic 2: AI Strategy and Business Alignment | - AI value identification and business case development - Organizational AI readiness and transformation planning |
| Topic 3: Data and AI Foundations | - Data governance and data quality for AI systems - Data lifecycle and preparation for AI use cases |
| Topic 4: AI Lifecycle Management | - Model development, validation, and iteration processes - AI solution development lifecycle (from concept to deployment) |
| Topic 5: AI Operations and Value Realization | - Measuring AI business value and outcomes - AI deployment and operationalization (MLOps concepts) - Performance monitoring and continuous improvement |
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問題 #101
An AI project team in the healthcare sector is tasked with developing a predictive model for patient readmissions. They need to gather required data from various sources, including electronic health records (EHR), patient surveys, and clinical notes. The team is evaluating which technique will help to ensure the data is comprehensive and reliable.
What is an effective technique the project team should use?
答案：C
解題說明：
In the PMI-CPMAI body of knowledge, healthcare AI initiatives are repeatedly framed as data-intensive efforts that must integrate heterogeneous sources such as EHRs, patient-reported outcomes, and unstructured clinical narratives. The guidance stresses that "unstructured sources, including physician notes and narrative reports, often contain critical clinical context that will not appear in structured fields," and that project teams must use techniques that can reliably extract this information into analysis-ready form to achieve completeness and reliability of the dataset. This is where natural language processing (NLP) is highlighted as a key enabler: by systematically parsing and extracting diagnoses, treatments, comorbidities, timelines, and outcomes from free-text clinical notes, NLP makes these rich but messy data usable alongside structured EHR fields and survey data.
PMI-CPMAI also emphasizes that simply adding more data or distributing training (such as data augmentation or federated learning) does not guarantee that the underlying data are comprehensive; what matters is that all relevant signals are captured and normalized across modalities. NLP directly supports this by converting unstructured text into standardized features, reducing omissions and manual abstraction errors. Real-time EHR integration improves freshness, but not necessarily coverage across all sources. Therefore, to ensure the data is comprehensive and reliable for a readmission prediction model, employing NLP to extract relevant data from clinical notes is the most effective technique among the options.
問題 #102
A project manager is preparing for an AI model evaluation. The model has shown an overall 70% accuracy rate, but the project key performance indicators (KPIs) require at least 89% accuracy.
Which issue related to accuracy reduction should the project manager investigate first?
答案：D
解題說明：
When an AI model underperforms against defined KPIs (70% accuracy vs required 89%), PMI-style AI evaluation guidance directs project managers to first investigate data-related issues, especially representativeness and quality of the training data, before focusing on algorithms or infrastructure. If the training data is not representative of real-world data (option A), the model may learn patterns that do not generalize to production conditions. For example, it might be overexposed to common, simple cases and underexposed to rare but critical scenarios, specific customer segments, geographies, or newer product types.
This mismatch is one of the most common causes of accuracy degradation between expected and actual performance. Ensuring representativeness involves checking that the data covers the full spectrum of operational scenarios, class distributions, time periods, and user demographics relevant to the use case. Inadequate compute (option B) more often affects training time than final accuracy, assuming the model trains to convergence. Failure to split datasets correctly (option C) leads to unreliable evaluation metrics, but the question already states an accuracy result and a KPI gap, pointing to performance, not just measurement. Algorithm selection (option D) is important but typically evaluated after confirming that the data foundation is sound. Thus, the first issue to investigate is whether training data is representative of real-world data.
問題 #103
A national health insurance company is embarking on a complex AI project to assist in coordinating patient care across its multiple hospital network. The AI system will analyze large amounts of patient data to coordinate care, improve patient outcomes, and optimize resource allocation. Numerous healthcare providers' data needs to be integrated. The data includes private patient information, and the project must comply with data privacy regulations in various countries.
Which critical step should be performed to optimize representative training data?
答案：C
解題說明：
PMI-CPMAI treats data as a central asset and states that representative, high-quality training data is essential for safe and effective AI in sensitive domains such as healthcare. Before sophisticated bias metrics or advanced KPIs are useful, the guidance stresses a phase of data understanding and preparation, where teams analyze data sources, coverage, completeness, and consistency, and ensure that the training set reflects the relevant populations, geographies, and use cases. PMI describes this as "profiling and exploring data to understand distributions, outliers, missingness, and segment coverage, then cleaning, integrating, and transforming it into a trusted, analysis-ready dataset." In a multi-country health insurance scenario, with diverse hospitals and different privacy regimes, this step includes mapping schemas, resolving identifiers, handling missing or noisy records, and ensuring that patients from different regions, demographics, and care pathways are adequately represented without oversampling or excluding key groups. Simply increasing the size of the dataset without ensuring diversity and representativeness may reinforce existing biases or create blind spots. Likewise, KPI enhancement comes later, once the data foundation is sound. Therefore, the critical step to optimize representative training data in this context is to improve data understanding and preparation, ensuring that the integrated dataset is complete, consistent, diverse, and properly structured for training.
問題 #104
A government agency is planning to implement a new AI-driven public service system. The project manager needs to develop a business case to secure funding. The agency's goals are to improve service delivery and reduce response times.
Which method will provide the results that meet the project manager's objective?
答案：B
解題說明：
Within the PMI-CPMAI guidance, developing a strong business case for AI requires evidence-based justification that the proposed solution will deliver measurable value, not just theoretical benefits. For a government agency whose stated goals are improving service delivery and reducing response times, the most convincing way to support a funding request is to demonstrate these improvements in a realistic environment.
A pilot program or proof-of-concept allows the project team to implement the AI-driven public service system on a limited scale, collect operational data, and compare key performance indicators (KPIs) such as response time, throughput, user satisfaction, and error rates before and after AI adoption.
PMI-CPMAI emphasizes that pilots help validate assumptions about feasibility, scalability, and stakeholder acceptance while revealing hidden risks and integration issues early. They provide concrete, context-specific metrics that can be used directly in the business case, strengthening arguments around public value, efficiency gains, and cost-effectiveness. By contrast, case studies and workshops are indirect and qualitative, and ROI projections alone remain hypothetical without empirical evidence. Therefore, conducting a pilot program best meets the project manager's objective of producing robust, measurable results that support a compelling AI business case for funding approval.
問題 #105
A project manager is tasked with explaining the AI model ' s decision-making process to the board of directors. The board members are nontechnical and require a comprehensible explanation to help ensure the model ' s decisions align with business objectives.
Which action should the project manager take?
答案：D
解題說明：
The best answer is B. Illustrate the decision pathway using LIME for localized interpretability . In PMI- CPMAI, one of the core responsibilities under Support Responsible and Trustworthy AI Efforts is to establish explainability requirements for stakeholder communication and to implement model interpretability tools and techniques . That makes this option the strongest match because the board is explicitly described as nontechnical and needs a comprehensible explanation of how the model reached a decision. LIME is an interpretability technique designed to explain individual predictions in a human- understandable way, which fits the scenario far better than performance charts or threshold metrics.
The other options are more focused on evaluation metrics than explainability for business stakeholders .
Confusion matrices, precision-recall trade-offs, and ROC curves are useful for model assessment, but they are typically more technical and less effective for explaining a specific decision pathway to executives. PMI materials emphasize transparency, scrutiny, and understanding of AI outputs, especially when stakeholder trust and alignment with business objectives are important. That is why an interpretability method such as LIME is the most PMI-aligned choice here.
問題 #106
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PMI 的 PMI-CPMAI 考古題覆蓋了最新的考試指南，根據真實的 PMI-CPMAI 考試真題編訂，確保每位考生順利通過 PMI-CPMAI 考試。如果在考試過程中變題了，考生可以享受免費更新一年的考題服務，保障了考生的權利。PMI-CPMAI 考試適合於 PMI 技術人士開發，目的是為了測驗考生基於各種平臺的設計和開發應用知識技能。考生要考取 PMI-CPMAI 認證，必須要擁有兩年開發技術領域的能力。
PMI-CPMAI考題資訊: https://www.kaoguti.com/PMI-CPMAI_exam-pdf.html
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