참고: Fast2test에서 Google Drive로 공유하는 무료 2026 PMI PMI-CPMAI 시험 문제집이 있습니다: https://drive.google.com/open?id=1nhZ5OumOEAYHfurYYeqYRZ6BVa3rTWvr
인재도 많고 경쟁도 많은 이 사회에, 업계인재들은 인기가 아주 많습니다.하지만 팽팽한 경쟁률도 무시할 수 없습니다.많은 PMI인재들도 어려운 인증시험을 패스하여 자기만의 자리를 지키고 있습니다.우리Fast2test에서는 마침 전문적으로 이러한 PMI인사들에게 편리하게 시험을 PMI-CPMAI패스할수 있도록 유용한 자료들을 제공하고 있습니다.
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Fast2test의 PMI인증 PMI-CPMAI덤프를 구매하시면 1년동안 무료 업데이트서비스버전을 받을수 있습니다. 시험문제가 변경되면 업데이트 하도록 최선을 다하기에Fast2test의 PMI인증 PMI-CPMAI덤프의 유효기간을 연장시켜드리는 셈입니다.퍼펙트한 구매후는 서비스는Fast2test의 PMI인증 PMI-CPMAI덤프를 구매하시면 받을수 있습니다.
질문 # 83
A consulting firm is preparing data for an AI-driven customer segmentation model. They need to verify data quality before data preparation.
What should the project manager do first?
정답：B
설명：
Before any data preparation or modeling, PMI-CP-style guidance on AI initiatives emphasizes data quality assessment as the first critical activity. Quality must be evaluated before cleaning, enrichment, or labeling so that the team clearly understands the condition of the raw data and the scope of remediation needed. One of the primary quality dimensions to check early is completeness-whether required fields are present, whether key attributes are missing, and whether coverage is sufficient across the population of customers for meaningful segmentation.
If completeness issues are severe, downstream activities such as data cleaning, enhancement, and modeling may propagate bias or produce unstable segments. By systematically assessing data completeness first, the project manager enables the team to: (1) quantify gaps, (2) decide whether to obtain additional data, and (3) prioritize subsequent cleaning and enrichment steps. Data enhancement (option B) and cleaning (option C) are important, but they are remedial actions that should be guided by the initial quality assessment. Data labeling (option D) is more relevant for supervised learning use cases than for unsupervised customer segmentation.
Therefore, to verify data quality prior to preparation, the project manager should first assess data completeness.
질문 # 84
An aerospace company is evaluating whether their sensor data meets the requirements for an AI-based predictive maintenance system. The project team needs to ensure that the data's accuracy, resolution, and timeliness are adequate to predict equipment failures.
Which method addresses the requirements?
정답：C
설명：
For an AI-based predictive maintenance system, PMI-CPMAI-aligned practices emphasize that the fitness of the data for the AI task must be validated in terms of accuracy, resolution, and timeliness before committing to model development. In the context of sensor data, this means confirming that measurements are precise enough to detect early degradation, sampled at a sufficient frequency to capture relevant patterns (resolution), and delivered with low delay so predictions are actionable (latency). A data quality assessment focused on precision and latency directly addresses these concerns by examining how close sensor readings are to true values, how stable they are over time, and how quickly the data flows from the equipment into the AI pipeline.
PMI-CPMAI guidance on data readiness for AI systems stresses profiling and testing data for measurement error, noise levels, sampling intervals, and end-to-end delivery lag before deciding if data is suitable for predictive models. Activities like schema review or feature engineering are important but come after confirming that raw data quality (especially precision and latency) meets the minimum requirements. Implementing governance frameworks or adding more sources does not, on its own, validate whether the existing sensor data is accurate and timely enough. Therefore, the method that best addresses the stated requirements is performing a data quality assessment focusing on precision and latency.
질문 # 85
A healthcare organization plans to use an AI solution to predict patient readmissions. The data science team needs to identify data sources and ensure data quality.
Which method will meet the project team's objectives?
정답：A
설명：
In PMI-CPMAI's treatment of data for AI, especially in sensitive domains like healthcare, the first responsibility of the project and data science teams is to understand and assess data quality and suitability before model development. The guidance states that AI teams should "systematically profile candidate data sources to evaluate completeness, consistency, validity, and coverage of key populations and variables relevant to the use case." Data profiling tools are highlighted as a practical means to inspect distributions, missing values, outliers, and anomalies across structured clinical, administrative, and claims data.
For a patient readmission prediction use case, PMI-CPMAI stresses that teams must identify which sources (EHR, discharge summaries, lab results, prior admissions, demographics, social determinants, etc.) are available and then "quantify data quality metrics such as completeness and timeliness to determine whether the dataset is fit for training and deployment." While techniques such as augmentation or real-time validation might be valuable later, they build upon an initial understanding obtained via profiling. Operationalizing a catalog supports governance and discovery but does not directly satisfy the immediate need to measure data quality.
Therefore, the method that best meets the objective of identifying data sources and ensuring data quality is to use data profiling tools to assess data completeness and other quality dimensions, providing an evidence-based foundation for subsequent preprocessing, feature engineering, and model training.
질문 # 86
A logistics company is operationalizing an AI system to improve delivery times. The project team needs to identify performance constraints that may impact the AI solution.
Which method should the project manager use to meet the team's objective?
정답：C
설명：
When operationalizing an AI system to improve delivery times, PMI-style AI project guidance stresses the importance of identifying constraints and assumptions early, before heavy investment in build-out. A preliminary feasibility study is the standard method to surface key performance constraints that might impact the AI solution. This includes analyzing current logistics processes, data availability and latency, network conditions, service-level expectations (e.g., maximum response times for route optimization), infrastructure capacity, and integration limits with existing systems.
A feasibility study helps the team clarify: what throughput is required, how frequently predictions must be updated, what real-time vs. batch constraints exist, and whether current hardware, APIs, and data pipelines can support those requirements. This aligns with PMI-CPMAI's emphasis on evaluating technical, data, and organizational readiness before committing to full-scale deployment.
Benchmarking competitors (option A) may highlight external performance targets but does not systematically uncover the internal constraints. Implementing advanced visualization tools (option B) can help later with monitoring and communication but does not, by itself, identify constraints. Training employees on AI ethics (option D) is valuable from a governance standpoint, yet it does not address performance limitations. Thus, the method that directly meets the objective of identifying performance constraints is to conduct a preliminary feasibility study.
질문 # 87
A project manager is considering different project management approaches for an AI solution deployment.
They need to ensure the approach allows for iterative improvements and accommodates changing requirements.
Which approach is effective in this situation?
정답：D
설명：
PMI-CPMAI emphasizes that AI projects typically involve uncertainty, experimentation, and evolving requirements. Data can change, model behavior must be tuned, and stakeholders may refine success criteria as they see early results. Because of this, PMI frames AI work as well-suited to adaptive/agile approaches that support short iterations, continuous learning, and rapid feedback loops.
In an adaptive/agile approach, the team plans in smaller increments, regularly reprioritizes the backlog, and refines scope based on empirical evidence from model experiments and pilots. This allows them to update features, retrain models, and adjust data or architecture as new insights are gained. PMI-CPMAI links this directly to AI lifecycles, where experimentation, evaluation, and deployment are repeated cycles rather than one-off phases.
Predictive approaches are more rigid and assume stable, knowable requirements upfront, which is rarely realistic for AI behavior and data-driven insights. Incremental and hybrid can add some flexibility, but adaptive/agile is the explicit choice in PMI's guidance when iterative improvement and changing requirements are primary concerns. Therefore, the most effective approach for an AI solution deployment in this context is adaptive/agile.
질문 # 88
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Fast2test를 선택함으로 여러분은 PMI 인증PMI-CPMAI시험에 대한 부담은 사라질 것입니다.우리 Fast2test는 끊임없는 업데이트로 항상 최신버전의 PMI 인증PMI-CPMAI시험덤프임을 보장해드립니다.만약 덤프품질을 확인하고 싶다면Fast2test 에서 무료로 제공되는PMI 인증PMI-CPMAI덤프의 일부분 문제를 체험하시면 됩니다.Fast2test 는 100%의 보장도를 자랑하며PMI 인증PMI-CPMAI시험을 한번에 패스하도록 도와드립니다.
PMI-CPMAI인기자격증 인증시험덤프: https://kr.fast2test.com/PMI-CPMAI-premium-file.html
Fast2test PMI-CPMAI 최신 PDF 버전 시험 문제집을 무료로 Google Drive에서 다운로드하세요: https://drive.google.com/open?id=1nhZ5OumOEAYHfurYYeqYRZ6BVa3rTWvr