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| Section | Objectives |
|---|---|
| Topic 1: AI Operations and Value Realization | - Measuring AI business value and outcomes - AI deployment and operationalization (MLOps concepts) - Performance monitoring and continuous improvement |
| Topic 2: AI Strategy and Business Alignment | - AI value identification and business case development - Organizational AI readiness and transformation planning |
| Topic 3: AI Lifecycle Management | - Model development, validation, and iteration processes - AI solution development lifecycle (from concept to deployment) |
| Topic 4: AI Governance, Ethics, and Risk | - Responsible AI principles and ethical considerations - Risk management, compliance, and regulatory alignment |
| Topic 5: Data and AI Foundations | - Data governance and data quality for AI systems - Data lifecycle and preparation for AI use cases |
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質問 # 99
During the transition to an AI solution, the project manager discovers that certain tasks may not require cognitive AI capabilities and can be handled through traditional automation methods. As a result, the project team starts segregating tasks based on their cognitive requirements.
What should the team consider?
正解：D
解説：
PMI-CPMAI clearly distinguishes between cognitive AI capabilities and traditional automation or noncognitive solutions. The guidance stresses that not every task in a workflow benefits from AI and that
"project leaders should deliberately match solution complexity to problem complexity, reserving cognitive AI for tasks that truly require perception, learning, or sophisticated decision support." For deterministic, rule- based, repetitive tasks, the recommended approach is to use conventional automation technologies (scripts, RPA, rule engines, workflow systems) rather than machine learning models.
When a project team discovers that certain tasks do not require cognition (e.g., simple routing, format conversion, deterministic validations), PMI-CPMAI recommends "segregating cognitive from noncognitive tasks and applying the simplest effective technology to each." This reduces cost, operational risk, and technical debt, while focusing AI engineering effort where it provides differentiated value. Applying AI to noncognitive tasks can introduce unnecessary complexity, additional monitoring and governance overhead, and avoidable model risk. Proceeding only with intelligent functionalities or overanalyzing traditional tasks without acting on the insight misses this key optimization.
Therefore, once tasks have been segregated by cognitive requirements, the team should utilize traditional automation solutions for noncognitive tasks and focus AI design, data, and model work only where cognitive capabilities are justified. This aligns with PMI-CPMAI's principle of "fit-for-purpose" technology selection and responsible, efficient AI adoption.
質問 # 100
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：B
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 # 101
A telecommunications company is implementing an AI solution to optimize network performance. The project team needs to prepare the data for the AI system by addressing data format inconsistencies. Which method should the project manager use?
正解：D
解説：
PMI's CPMAI/PMI-CPMAI guidance places "data preparation and transformation" at the center of getting data into a usable state for model development and operations. The CPMAI v7 outline explicitly includes coordinating data preparation activities such as formulating data preparation requirements and performing data cleansing and enhancement-work that directly addresses inconsistent formats. In addition, CPMAI v7 lists "Executing Data Preparation and Transformation," including methods to improve data quality and accuracy and to clean/enhance data for optimal AI performance. When the issue is format inconsistency (e.g., mismatched schemas, units, encodings, timestamp formats), the PMI-aligned response is to define and execute the required transformation steps (normalize formats, standardize fields, convert units, align timestamps, encode categories) so the dataset meets the model and pipeline requirements. Governance (C) is important but is broader and slower-moving; it does not, by itself, resolve the immediate technical incompatibilities. A data quality report (D) documents problems but does not fix them. Data breach impact (B) is a different risk category. Therefore, the method that best meets the stated objective is determining the necessary data transformation steps.
質問 # 102
A government agency is implementing a natural language processing (NLP) system to analyze public comments on new regulations. The project team needs to ensure the data sources are well-identified and accessible.
What is an effective method to meet the project team's objectives?
正解：B
解説：
According to PMI-CPMAI, before implementing sophisticated platforms (such as catalogs or warehouses), AI initiatives must begin with foundation work on data discovery and inventory. For an NLP system analyzing public comments on regulations, the framework stresses that teams must first "identify, locate, and characterize all relevant data sources, owners, formats, access paths, and constraints," and ensure this information is documented in a consistent, accessible way. This is commonly described as a data inventory or data source audit, where the team systematically lists sources (web forms, email submissions, social media channels, open data portals, scanned documents), their frequency of update, retention policies, legal constraints, and access mechanisms.
PMI-CPMAI notes that this step is critical to ensure that data sources are both well-identified (no major channel missing, clear owners, understood structures) and accessible within regulatory and security constraints. An internal data catalog system can be a longer-term governance mechanism, but it only becomes effective if the underlying inventory work has already been done accurately; otherwise, the catalog simply reflects incomplete or outdated information. Data warehousing or CRM systems address storage or customer data management, not necessarily the breadth of public-comment channels.
Therefore, the most directly effective method to meet the project team's immediate objective-ensuring data sources are well-identified and accessible for the NLP initiative-is conducting a thorough data inventory audit and ensuring it is well documented.
質問 # 103
A team is getting ready to begin working on a machine learning project. They need to build a data preparation pipeline. A team member suggests reusing the same pipeline created for their last project.
What is wrong with this suggestion?
正解：D
解説：
The best answer is A. Pipelines are pattern- and model-needs specific . PMI-CPMAI treats data preparation as something that must be tailored to the AI use case, the data involved, and the model being developed. The official outline includes defining required data, mapping data requirements to business objectives, overseeing data cleaning and preprocessing workflows, managing normalization, augmentation, and feature-related activities, and verifying that preprocessing results are valid before model training. In the CPMAI v7 outline, PMI also emphasizes engineering AI data pipelines, creating separate training and inference pipelines, and addressing AI-specific needs in data preparation . These points strongly support the idea that a previous project's pipeline should not be reused blindly.
This answer is also consistent with PMI's pattern-based thinking: different AI patterns and model approaches require different data structures, labels, transformations, and quality controls. As an inference from PMI's methodology, a pipeline that worked for one project may be unsuitable for another because the new project may have different objectives, preprocessing requirements, or model behaviors. Option B is too broad, Option C is too permissive, and Option D is too narrow because the issue begins before operationalization.
質問 # 104
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