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| Section | Objectives |
|---|---|
| AI Governance, Ethics, and Risk | - Risk management, compliance, and regulatory alignment - Responsible AI principles and ethical considerations |
| Data and AI Foundations | - Data lifecycle and preparation for AI use cases - Data governance and data quality for AI systems |
| AI Lifecycle Management | - AI solution development lifecycle (from concept to deployment) - Model development, validation, and iteration processes |
| AI Strategy and Business Alignment | - Organizational AI readiness and transformation planning - AI value identification and business case development |
| AI Operations and Value Realization | - Performance monitoring and continuous improvement - AI deployment and operationalization (MLOps concepts) - Measuring AI business value and outcomes |
PMI PMI-CPMAI 덤프는 고객님의PMI PMI-CPMAI시험패스요망에 제일 가까운 시험대비자료입니다. 많은 자료정리 필요없이 Itcertkr에서 제공해드리는 깔끔한PMI PMI-CPMAI덤프만 있으면 자격증을 절반 취득한것과 같습니다. PMI PMI-CPMAI 덤프를 다운받아 열공하세요.
질문 # 66
In the finance sector, a company is implementing an AI system for credit risk assessment. The project manager needs to identify the data subject matter experts (SMEs) who can help to ensure the accuracy and reliability of the model.
What is an effective method to achieve this objective?
정답：B
설명：
For an AI credit risk assessment system, PMI-style AI governance and lifecycle guidance consistently emphasizes that domain and data expertise must be combined to ensure model accuracy, relevance, and reliability. In the finance context, this means involving: (1) data analysts / data scientists who understand data structures, data quality, feature engineering, and model behavior, and (2) financial / credit risk experts who understand regulatory constraints, lending policies, risk appetite, and real-world meaning of variables and outputs. Together, they validate that input data correctly represents customer risk profiles, that derived features reflect sound credit risk logic, and that model outputs are interpretable and aligned with institutional policies.
Options B, C, and D conflict with good AI practice described in PMI-style guidance. Focusing on SMEs "with experience in noncognitive solutions" is irrelevant to credit risk modeling. Relying on general IT staff ignores the need for specialized financial and data expertise. Selecting SMEs based on availability rather than expertise directly undermines model quality and risk control. Therefore, the effective and expected method in an AI credit risk initiative is to engage internal data analysts and financial experts as data SMEs to support model design, validation, and ongoing monitoring.
질문 # 67
A project manager is tasked with overseeing the implementation of an AI model for financial forecasting.
They need to ensure the model's predictions are reliable.
If the model's error rate exceeds acceptable boundaries, what will occur next?
정답：D
설명：
In PMI-CPMAI, evaluation and validation of AI models are explicitly tied to predefined performance thresholds and acceptance criteria. For a financial forecasting model, reliability is typically expressed using error metrics (such as MAE, MAPE, RMSE, etc.) and acceptable tolerance bands agreed with stakeholders.
PMI describes that if a model's error rate exceeds these agreed boundaries, the model has not met acceptance criteria, and the project must return to an earlier lifecycle stage (typically re-training, re-specification, or data refinement) before operationalization.
This situation has a direct schedule impact: additional cycles of data analysis, feature engineering, hyperparameter tuning, and validation must be performed. Thus, the practical consequence is delay in operationalization until the model can demonstrate acceptable and stable behavior on representative test and validation data. PMI-CPMAI frames this as part of a disciplined, iterative lifecycle rather than a failure; it is expected that some models will require multiple improvement cycles.
The other options do not align with PMI's treatment of performance deviations. An increased error rate does not reduce the need for human oversight; in fact, oversight may need to be increased. Computational cost changes (option C) are secondary and not the primary next step. Stakeholder confidence (option D) generally decreases when error rates exceed agreed limits. Therefore, the realistic and lifecycle-aligned outcome is operationalization delays due to model retraining (option A).
질문 # 68
A team member is analyzing unstructured data compared to structured data and asks the project manager to explain the difference.
What response should the project manager provide?
정답：C
설명：
Comprehensive and Detailed Explanation From PMI Certified Professional in Managing AI Documents:
The best answer is A. Unstructured data lacks organization and is difficult to process.
In AI project management, understanding the differences between structured, semi-structured, and unstructured data is essential for selecting appropriate data preparation and modeling approaches.
Structured data follows a predefined schema, typically organized into rows, columns, and standardized fields.
Examples include transactional databases, customer records, and inventory tables.
Unstructured data does not follow a predefined tabular format. Examples include images, video recordings, audio files, free-text documents, and emails.
Because unstructured data lacks a consistent predefined schema, additional processing techniques may be necessary to extract meaningful information. These techniques can include natural language processing, computer vision, transcription, feature extraction, and embedding generation.
Why the other options are incorrect:
- A: Correct because unstructured data is generally more challenging to process using conventional database methods.
- B: Fixed schemas are characteristic of structured data.
- C: This more closely describes semi-structured data, which combines organizational elements with flexible structures.
- D: Relational databases typically organize structured data.
Under PMI-CPMAI ' s Data Understanding and Data Preparation phases, recognizing data characteristics helps teams determine the appropriate processing methods, resources, and AI solution requirements.
질문 # 69
A project team is defining the requirements for an AI solution to ensure transparency in data selection and algorithm selection. The team needs to assess whether the AI solution is necessary and identify the cognitive parts of the project.
What should the project manager do first?
정답：B
설명：
The best answer is C. Determine the business objective and stakeholder needs . In PMI-CPMAI, the first work in the business understanding phase is to clarify the problem to be solved, define the business question, and align the initiative to stakeholder expectations before deciding whether AI is necessary or which parts are cognitive. PMI's official CPMAI exam outline explicitly includes formulating AI-specific business questions, prioritizing and scoping AI projects, conducting go/no-go assessments, and separating cognitive from non- cognitive components. That sequence strongly suggests that the team must first understand the business objective and stakeholder needs before evaluating alternatives, transparency controls, or data requirements.
Option B is important, but it comes after the project team understands what the business is trying to achieve.
Only then can they assess whether non-cognitive alternatives are sufficient. Option A is also relevant, especially for trustworthy AI, but ethical and transparency requirements should be defined in the context of the business use case. Option D is premature because data identification follows the initial business framing.
PMI's methodology is built around starting with business understanding, then deciding whether AI is appropriate, which pattern applies, and what governance is required.
질문 # 70
A project team at an IT services company is developing an AI solution to enhance network security. They need to define the success criteria to help ensure the project achieves its desired outcomes.
What should the project manager do to define the relevant success criteria?
정답：A
설명：
PMI-CPMAI stresses that AI projects must define clear, measurable success criteria that are directly aligned with the problem the AI is intended to solve. In a network security context, the AI solution is being developed to "enhance network security," which, in operational terms, translates to outcomes like faster incident response and better detection of threats and anomalies.
PMI's guidance on benefits realization and performance management recommends using key performance indicators (KPIs) that are specific, measurable, and time-bound. For security, relevant KPIs typically include metrics such as mean time to detect (MTTD), mean time to respond (MTTR), detection rates, false positive/false negative rates, number of incidents contained, and reduction in successful breaches. By defining success criteria in terms of incident response times and threat detection rates, the project manager ties the AI system's performance directly to business and operational outcomes, making it easier to monitor effectiveness and justify investment.
Implementing ML algorithms (option A) is a technical activity, not a definition of success. SWOT analysis and cost-benefit analysis (options C and D) can inform strategy and justification, but they do not, by themselves, define how success will be measured in day-to-day operations. PMI-CPMAI emphasizes metrics-driven evaluation, so using KPIs for incident response times and threat detection rates (option B) is the correct approach.
질문 # 71
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PMI-CPMAI 100％시험패스 자료: https://www.itcertkr.com/PMI-CPMAI_exam.html
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