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問題 #153
A health insurance company is launching an AI project to build a machine learning (ML) diagnostic triage model that flags high-risk findings from incoming radiology scans. The goal is to accelerate prior authorization decisions and reduce missed critical conditions.
The team has a choice between a deep learning computer vision model trained on labeled historical scans and a conventional rule-based image feature algorithm built from expert-defined thresholds.
What should the data analyst do?
答案：C
解題說明：
Comprehensive and Detailed Explanation From PMI Certified Professional in Managing AI Documents:
The best answer is D. Compare the options to identify the stronger approach for accuracy and enterprise operationalization.
PMI-CPMAI emphasizes selecting AI solutions based on business requirements, technical feasibility, measurable performance, and operational suitability rather than automatically choosing the most sophisticated technology.
In this scenario, the organization must compare a deep learning approach with a conventional rule-based alternative.
The deep learning model may identify complex visual patterns that are difficult to represent through fixed rules. However, it may require substantial labeled training data, computational resources, validation, and ongoing monitoring.
The rule-based approach may provide greater simplicity and interpretability but may struggle with complex or previously unseen imaging patterns.
The analyst should compare both approaches using representative clinical data, clinically relevant accuracy measures, operational requirements, integration feasibility, and regulatory constraints.
Why the other options are incorrect:
- A: Focuses on training before comparing solution suitability.
- B: Addresses feasibility but does not directly compare both proposed approaches across performance and operationalization.
- C: Ethical evaluation is essential but insufficient as the sole selection criterion.
- D: Correct because it supports an evidence-based comparison of competing solutions.
For healthcare applications, diagnostic sensitivity, false-negative risk, safety, and clinical validation must also inform the decision. PMI ' s Business Understanding phase explicitly supports comparing AI with traditional approaches.
問題 #154
A team is running a forecasting project and wants to use previous user data to better predict future outcomes. However, the team does not have access to all the data they need.
Which action should the project manager take?
答案：B
解題說明：
CPMAI explicitly frames AI and forecasting projects as iterative and incremental, not rigid, one-shot efforts. The methodology allows teams to progress through phases with the understanding that they may loop back when new data or insights become available. In a forecasting project where not all desired historical user data is accessible yet, the recommended approach is to move forward with what is available, while planning and documenting assumptions about missing data and potential impacts.
PMI/CPMAI guidance stresses that waiting for "perfect" data can stall value delivery and increase project risk. Instead, early iterations using partial but representative data help validate the problem framing, test pipelines, and surface data-access issues early, while governance and data owners work on unlocking additional datasets. The key is to acknowledge explicitly that the project is iterative: you may return to earlier data understanding and preparation steps as new data becomes available. This is exactly what option B describes-moving forward while anticipating additional access and leveraging an iterative lifecycle to revisit earlier steps-rather than freezing the project (C) or blindly pressing ahead without a plan (A or D).
問題 #155
A project team is using a prompt engineering approach to improve AI/machine learning (ML) model outputs.
They started with broad questions and then narrowed down the specific elements. If the team had provided insufficient context, what would be the result?
答案：A
解題說明：
PMI guidance on prompts and prompt engineering states that prompts "supply the system with context and guidance as well as constraints," and that the value of a GenAI system "can only be realized through the instructions provided to it." PMI further explains that while an AI system can respond to very short inputs,
"the less specific a prompt, the more likely the results will be vague or unhelpful," explicitly linking insufficient specificity/context to degraded usefulness. In PMI's recommended "diverge and converge" prompt approach, teams begin broad, then progressively refine, adding details such as industry, region, project type, intended use, and examples-because "the granularity of the input will be directly proportional to the utility of the output received." Therefore, if the team provides insufficient context, the model must "guess" what is intended, which most directly manifests as answers that are not aligned to the actual task needs (i.e., lacking relevance), rather than being more accurate or more efficient.
問題 #156
A financial institution is planning to use AI capabilities to detect fraudulent transactions. The project manager needs to ensure that all necessary requirements are met before proceeding.
What is a necessary initial task?
答案：B
解題說明：
The best answer is C. Identifying the primary stakeholders and their needs . In PMI-CPMAI, the first work in shaping an AI initiative is to understand the business problem, the affected stakeholders, and the requirements that define success. The official exam outline includes gathering business requirements, aligning AI initiatives with organizational goals, defining success criteria, and identifying stakeholders and their expectations as part of the early business understanding and solution-definition work.
This is especially important in fraud detection because multiple stakeholder groups are involved, such as fraud investigators, compliance teams, operations leaders, customers, and executives. Their needs determine what matters most: detection speed, false-positive tolerance, explainability, escalation workflow, auditability, and regulatory alignment. PMI's CPMAI materials also use fraud detection as an example of a pattern and anomaly detection use case, reinforcing that the project should start with the problem context and stakeholder expectations before evaluating model quality, scalability, or downstream ethical controls.
The other choices matter later, but they are not the best initial task. You cannot assess current-method accuracy, AI scalability, or ethical implications well until the key stakeholders and business requirements are clearly defined. That is why stakeholder identification is the strongest PMI-aligned starting point.
問題 #157
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：
The best answer is B. Creating a detailed ROI projection . PMI's CPMAI materials place clear emphasis on developing a business case with financial justification when an AI initiative is seeking approval or funding.
In the official exam outline, under Identify Business Needs and Solutions , PMI explicitly includes Determine ROI , with activities such as calculating expected benefits, estimating total cost of ownership, establishing ROI metrics, and creating cost-benefit analysis for stakeholder decision-making. It also includes Support business case creation by gathering financial data, projected benefits, and cost estimates.
That makes ROI projection the strongest method because the project manager's stated objective is to secure funding . While better service delivery and faster response times are important mission outcomes, decision- makers typically need those outcomes translated into a justified investment case. Analyzing other agencies' case studies can provide supporting evidence, but it does not directly quantify value for this agency.
Stakeholder workshops help alignment, and a pilot program may generate proof later, but neither is the primary method for creating a formal funding justification. PMI's framework is explicit that AI business cases should be supported by measurable projected benefits, cost analysis, and ROI-oriented reasoning, which is exactly what this option provides.
問題 #158
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