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| Certification Vendor: | PMI |
|---|---|
| Exam Name: | PMI Certified Professional in Managing AI (CPMAI) Exam |
| Exam Number: | CPMAI |
| Available Languages: | English |
| Exam Format: | Multiple choice |
| Sample Questions: | PMI PMI-CPMAI Sample Questions |
| Pre Condition: | No formal prerequisite is publicly standardized; PMI recommends familiarity with project management and AI concepts. |
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質問 # 101
An AI project team has prepared the data and is ready to proceed with model development.
Which action should the project manager perform next?
正解：D
解説：
Once data preparation is complete and the team is ready for model development, PMI-aligned AI lifecycle guidance calls for clear definition and documentation of performance metrics and success criteria before training models. The project manager should ensure that everyone agrees on which 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 in evaluation 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 that model 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 to document the performance metrics for the model.
質問 # 102
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?
正解：A
解説：
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.
質問 # 103
A healthcare provider is operationalizing an AI tool to assist in diagnostic processes. To ensure robust model governance, they need to address data privacy and ethical considerations.
What should the project manager do?
正解：A
解説：
Within PMI-CPMAI-aligned responsible AI practices, deploying AI in healthcare diagnostics requires explicit attention to data privacy, regulatory compliance, and ethical impact on patients. A Privacy Impact Assessment (PIA) is a structured method used to systematically identify, analyze, and mitigate privacy and ethical risks associated with data processing and automated decisions. For an operationalized diagnostic AI tool, a PIA helps the project manager map data flows (collection, storage, use, and sharing), determine the legal basis for processing sensitive health data, highlight potential harms (misuse, breaches, inappropriate access), and define safeguards such as minimization, anonymization, consent handling, and access controls.
PMI-CP-consistent AI governance emphasizes documenting how data is used and how decisions affect individuals, as well as demonstrating that privacy and ethical considerations have been proactively assessed before and during operation. While internal frameworks or protocols (such as generic monitoring or controls) may help manage performance and operations, they do not replace a formal, focused assessment of privacy risk and ethical implications. A PIA provides concrete evidence that the organization has anticipated the effect of the AI system on patient rights, confidentiality, and trust, making it the most suitable action in this context.
Therefore, the project manager should develop a detailed privacy impact assessment (PIA).
質問 # 104
A telecommunications company is adopting an AI-based customer service chatbot. They are concerned about potential quality issues affecting customer satisfaction.
What should the project manager do?
正解：C
解説：
From a PMI-CPMAI perspective, concerns about quality and customer satisfaction must be addressed first at the planning level, not only reactively once the chatbot is live. For AI-enabled services such as a customer service chatbot, the project manager is expected to define a formal quality management approach that covers:
what "quality" means for this AI system (e.g., accuracy of responses, relevance, tone, response time), how it will be measured, and which controls and tests will be applied throughout the lifecycle.
A comprehensive quality assurance (QA) plan typically includes: clearly defined quality criteria and success metrics, test strategies (unit tests, conversation flow tests, usability tests, bias checks), acceptance thresholds, evaluation datasets, user journey scenarios, procedures for handling low-confidence outputs, and mechanisms for ongoing monitoring once in production. PMI-CPMAI guidance on AI lifecycle management stresses that these elements must be designed before wide rollout so that risks to customer experience are proactively controlled rather than discovered ad hoc.
Actions like beta testing, setting up monitoring teams, or doing regular performance reviews are valuable, but they are individual techniques that should exist inside an overarching QA framework. The best initial step that a project manager should take, given generalized concern about potential quality issues, is therefore to develop a comprehensive quality assurance plan for the chatbot.
質問 # 105
A project manager is in the data lifecycle phase of an AI solution and needs to ensure the project ' s success.
What should the project manager do first?
正解：C
解説：
Comprehensive and Detailed Explanation From PMI Certified Professional in Managing AI Documents:
The best answer is C. Data collection and preparation.
PMI-CPMAI emphasizes that AI projects depend on acquiring suitable data before model development can begin.
During the Data Understanding and Data Preparation phases, the project team identifies required information, locates relevant data sources, determines data accessibility, gathers necessary datasets, and prepares the information for model development.
Among the available options, data collection and preparation provides the broadest starting activity because it establishes the data foundation for subsequent quality checks, labeling, transformation, and training.
Data collection should follow an understanding of the business objectives and the specific data requirements of the AI use case.
The project manager must ensure the collected information is relevant, appropriately authorized, representative, and available in sufficient quantities.
Why the other options are incorrect:
- A: Data cleaning generally requires data to have already been acquired.
- B: Labeling and annotation are necessary for certain learning approaches but are not universal first steps.
- C: Correct as the broadest available initial data lifecycle activity.
- D: Quality assessment is essential, but it depends on identifying or collecting data that can be evaluated.
Important distinction: PMI ' s full methodology begins with Business Understanding and identifying data requirements. Because neither appears among the options, C is the strongest selection. Data validation should also occur during collection, not only after preparation.
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質問 # 106
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