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질문 # 119
A healthcare organization plans to develop an AI-driven diagnostic tool. To define the required data, the project manager needs to ensure data consistency and accessibility.
Which method should the project manager use?
정답：A,D
설명：
CPMAI's Data Understanding and Data Preparation phases stress that AI success in domains like healthcare depends on robust data pipelines that ensure consistency, quality, and accessibility before modeling begins. Guidance describes these phases as profiling and assessing data, then performing cleaning, transformation, and structuring so that data are reliable and usable by downstream models.
A data quality assessment combined with ETL (extraction, transformation, loading) processes directly supports these objectives. ETL pipelines standardize formats across disparate systems, enforce validation rules, manage missing values, harmonize coding schemes (for example, diagnosis codes), and centralize data into accessible stores. This is exactly the kind of foundational work CPMAI describes as a prerequisite to effective model development, particularly in regulated sectors such as healthcare where inconsistent or inaccessible data can have clinical and regulatory consequences.
By contrast, using NLP to standardize records (B) is a specialized technique that may help later but does not replace a systematic quality and ETL process. Integrating EHR with ML algorithms (C) and designing hybrid cloud storage (D) are more about later technical integration and infrastructure than about defining and ensuring initial data consistency and accessibility. Thus, in line with CPMAI's data-centric guidance, performing a data quality assessment with ETL processes is the correct method, making option A the best answer.
질문 # 120
A manufacturing company is operationalizing an AI-driven quality control system. The project manager needs to ensure data privacy and regulatory compliance due to the critical nature of protecting sensitive operational data.
What is an effective technique that addresses these requirements?
정답：C
설명：
PMI-CPMAI repeatedly highlights data privacy and regulatory compliance as core elements of responsible AI, particularly when operational data, trade secrets, or other sensitive information is involved. A key technique recommended in responsible data handling is data anonymization or de-identification, which reduces the risk of sensitive details being exposed while still allowing AI models to learn useful patterns.
From a governance and compliance standpoint, anonymization supports principles such as data minimization and privacy-by-design, both of which are prominent in modern regulatory regimes. Even when the data is not strictly "personal," sensitive operational data can present competitive, security, or safety risks if improperly exposed. Anonymization can involve removing or masking identifiers, aggregating data, and transforming features so that individual entities or critical operational specifics cannot be reverse-engineered, while preserving statistical utility for modeling.
Zero-trust architectures and encryption schemes (options A and D) are important security controls, but they focus primarily on controlling access and protecting data in transit or at rest, not on reducing identifiability of the data itself. Secure multiparty computation (option B) is specialized and often beyond what is pragmatically needed for typical operationalization scenarios. PMI-CPMAI's responsible AI practices emphasize anonymization as a direct and effective privacy technique. Therefore, applying data anonymization to the dataset (option C) is the most appropriate choice.
질문 # 121
After implementing an iteration of an Al solution, the project manager realizes that the system is not scalable due to high maintenance requirements. What is an effective way to address this issue?
정답：A
설명：
When an AI solution is described as "not scalable due to high maintenance requirements," PMI-style AI governance and lifecycle guidance points toward architectural refactoring rather than simply changing technologies or deployment environments. High maintenance often stems from tight coupling, monolithic design, and lack of clear separation between data, model, business logic, and interface layers.
Adopting a modular architecture to isolate different system components (option C) directly addresses this problem. In a modular or microservice-oriented design, each component-data ingestion, feature engineering, model training, model serving, monitoring, etc.-is separated behind clear interfaces. This makes it much easier to update or replace one part of the system without impacting the whole, which reduces maintenance overhead and improves scalability over time. It also supports independent deployment, targeted testing, and selective scaling of the components that receive the heaviest load.
Switching to a rule-based system (option A) typically increases maintenance complexity in dynamic environments. Incorporating generative AI (option B) may change the modeling approach but does not inherently solve structural maintenance issues. Utilizing cloud-based solutions (option D) helps with infrastructure scalability but does not fix architectural coupling. Therefore, the most effective way to address non-scalability caused by high maintenance requirements is to adopt a modular architecture.
질문 # 122
A project team is approaching a go/no-go decision process and needs a data quality assessment technique aligned with AI practices.
Which technique should the project team use?
정답：A
설명：
Comprehensive and Detailed Explanation From PMI Certified Professional in Managing AI Documents:
The best answer is D. Machine-driven outlier and pattern irregularity identification.
PMI-CPMAI emphasizes conducting data quality assessments before determining whether datasets are suitable for model development.
Data evaluation should identify problems involving accuracy, completeness, consistency, representativeness, missing values, and unusual data distributions.
Automated anomaly detection techniques can help identify unusual observations, inconsistent records, unexpected patterns, and potential data quality problems that might be difficult to discover through manual inspection.
For large AI datasets, automated detection can improve the efficiency and consistency of quality assessment.
These techniques may include statistical outlier detection, clustering-based analysis, distribution monitoring, and machine learning approaches for identifying anomalous records.
Why the other options are incorrect:
- A: Information retention supports governance but does not directly evaluate data quality.
- B: Predefined indicators are useful for evaluating data readiness but represent a broader measurement approach rather than the specific AI-oriented detection technique emphasized by this question.
- C: Unchanging baseline expectations may become inappropriate as data characteristics evolve.
- D: Correct because automated detection can reveal data anomalies and irregularities affecting model readiness.
Verification qualification: Option B is also a legitimate and important PMI-aligned data assessment method.
D is selected because the question specifically emphasizes an AI-oriented assessment technique. If the intended emphasis were formal go/no-go acceptance criteria, B could reasonably be selected instead.
PMI ' s ECO supports both evaluating data quality against requirements and identifying unusual distributions before making data-readiness decisions.
질문 # 123
A logistics company wants to optimize its delivery routes while adapting to real-time traffic conditions.
Which AI pattern or patterns meet these goals?
정답：D
설명：
Within CPMAI and PMI's AI pattern framing, predictive analytics is the pattern that focuses on using historical and real-time data to forecast future states-exactly what is needed for route optimization under changing traffic conditions. For a logistics company, the AI system must estimate future travel times, congestion levels, delays, and likely delivery windows. These predictions are then used as inputs to optimization logic that chooses the best routes and adjusts them dynamically as new data arrives.
Recognition/summarization patterns focus on classification or extracting meaning from content (such as images or text), while conversational patterns are aimed at dialog systems like chatbots. Automation and rule-based systems can encode fixed routing rules, but they cannot by themselves learn patterns from historical traffic and adapt to evolving conditions. PMI/CPMAI guidance highlights that when the business problem involves forecasting outcomes to inform better decisions, the appropriate AI pattern is predictive analytics-often implemented with regression, time-series models, or more advanced learning approaches. Therefore, for optimizing delivery routes while adapting to real-time traffic, the correct pattern is predictive analytics, making option D the appropriate choice.
질문 # 124
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