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최신 CPMAI PMI-CPMAI 무료샘플문제 (Q116-Q121):

질문 # 116
A project manager is preparing a contingency plan for an Al-driven customer service platform. They need to determine an effective strategy to handle potential system downtimes.
Which strategy addresses the project manager's objective?

A. Implementing a manual override system for critical customer queries
B. Providing extensive training to customer service representatives on handling Al failures
C. Developing an automated fallback chatbot with limited capabilities
D. Creating a robust customer service logging system to quickly identify and resolve issues

정답：C

설명：
PMI-CP-oriented AI risk and resilience practices emphasize continuity of service and graceful degradation when AI systems fail or are temporarily unavailable. For an AI-driven customer service platform, the contingency plan should ensure that customers still receive some level of assistance even when the main AI system is down. An automated fallback chatbot with limited capabilities (option C) embodies this principle by providing a simplified yet always-available channel.
Such a fallback system might offer only basic FAQs, simple intent handling, or routing to human agents, but it maintains a consistent experience and avoids a complete service outage. This is a classic "fail-soft" or "degraded mode" strategy often highlighted in AI operations and MLOps guidance: if the primary model or service is unavailable, the system automatically switches to a simpler, more reliable backup.
Logging systems (option A) are important for diagnosis but do not directly serve customers during downtime. Manual override for critical queries (option B) and extensive staff training (option D) are valuable complementary controls, yet they are human-dependent and slower to activate. PMI-style AI contingency planning stresses automated, pre-defined fallback paths wherever possible. Hence, developing an automated fallback chatbot with limited capabilities best addresses the objective of handling potential system downtimes.

질문 # 117
A healthcare organization is preparing training data for an AI model that predicts patient readmissions. The team discovers inconsistent coding across clinics for the same diagnosis. Which action best addresses the problem during data preparation?

A. Determine and apply data transformation and standardization steps
B. Ignore the inconsistency because the model will learn patterns anyway
C. Skip validation to save time
D. Replace real data with only synthetic data

정답：A

설명：
PMI-CPMAI aligns data preparation with executing data cleansing and enhancement activities so that datasets meet model and operational requirements. Inconsistent clinical coding is a data quality issue that threatens accuracy, fairness, and interpretability, because identical conditions may be represented differently across sources. The PMI-aligned response is to determine and apply the necessary transformation steps- standardizing codes to a controlled vocabulary, mapping local codes to a canonical schema, normalizing formats, and documenting rules and lineage so the process is auditable. Ignoring inconsistencies (B) increases noise and can embed systematic bias (e.g., certain clinics appearing "higher risk" due to coding artifacts).
Relying only on synthetic data (C) can reduce fidelity if the synthetic process fails to reflect true clinical distributions. Skipping validation (D) violates responsible delivery expectations because it undermines patient safety and data integrity. PMI's responsible and trustworthy framing supports disciplined data readiness work before model development proceeds.

질문 # 118
A project involves integrating AI systems across multiple departments, each with different access levels. This complex AI project has presented the project manager with significant issues related to data misuse. The project team has been focused on their ethics guidelines but continues to experience data misuse. The project involves different regional data protection regulations which further increases the complexity.
What issue will cause these challenges to occur?

A. Limited awareness of explainability requirements
B. Failure to implement robust encryption for data security
C. Overlooking algorithmic bias and fairness concerns
D. Lack of a detailed plan addressing a governance strategy

정답：D

설명：
In PMI-CPMAI, persistent issues like data misuse across departments and jurisdictions point directly to weaknesses in AI and data governance, not just ethics awareness. While ethics guidelines are important, they are only one element of a complete governance framework. PMI's AI governance view stresses the need for a detailed, actionable governance strategy that defines roles (owners, stewards, custodians), access controls, data classification, data use policies, approval workflows, and compliance processes that consider regional regulations (e.g., differing data protection laws).
Without such a governance plan, teams may unintentionally share or use data in ways that conflict with internal policies or external regulations, even if they know and care about ethics. Algorithmic bias (option C) and explainability (option A) are important but do not directly address cross-department access management and regional regulatory differences. Failure to implement robust encryption (option D) concerns technical security of data in transit/at rest; it does not, by itself, prevent misuse by authorized but improperly governed users.
Therefore, the root issue causing these challenges is the lack of a detailed plan addressing a governance strategy (option B), which should integrate ethics, regulatory requirements, and operational controls for data use across departments and regions.

질문 # 119
A financial services firm is operationalizing an AI-driven fraud detection system. The project manager needs to ensure the tool complies with relevant data privacy laws while providing secure data access to only authorized personnel.
What is an effective technique to address these requirements?

A. Conducting a privacy impact assessment (PIA) to identify risks
B. Implementing real-time data verification (RTDV) processes
C. Developing a comprehensive data classification policy (DCP)
D. Utilizing role-based access control (RBAC) to limit data access

정답：D

설명：
In an AI-driven fraud detection context, PMI-CP/CPMAI guidance on data governance stresses that compliance with privacy laws and the principle of "least privilege" must be enforced with technical access controls as well as policies. While a data classification policy and privacy impact assessments are important, they mainly describe and analyze risks; they do not by themselves prevent unauthorized access.
An effective technique that directly addresses "secure data access to only authorized personnel" is role-based access control (RBAC). RBAC ties access rights to defined roles (e.g., fraud analyst, data scientist, auditor), ensuring that users see only the data necessary for their job and nothing more. This supports compliance with privacy regulations that require data minimization, access limitation, and accountability. It also provides an auditable structure for who can access what, which is critical during regulatory reviews or incidents.
Within AI projects, RBAC should be applied across data stores, model monitoring dashboards, and operational interfaces so that sensitive transaction and identity data are protected end to end. Therefore, among the options presented, utilizing role-based access control (RBAC) to limit data access is the most direct and effective technique to satisfy both legal compliance and secure, authorized-only access.

질문 # 120
A government agency is adopting an AI/machine learning (ML) model to analyze large sets of public data for policy making. It is crucial that the project team ensures the accuracy of the model ' s predictions.
If the project team needs to validate the model, which action should they perform?

A. Ensure adherence to coding standards.
B. Utilize a diverse set of test cases.
C. Conduct a single comprehensive validation.
D. Implement continuous integration testing.

정답：B

설명：
The best answer is C. Utilize a diverse set of test cases . PMI-CPMAI's model evaluation domain focuses on building comprehensive evaluation plans and formulating appropriate evaluation questions and criteria.
Validation is not treated as a one-time technical check, but as a structured process designed to test model behavior across a range of relevant conditions, edge cases, and data contexts. Using a diverse set of test cases is the best way to assess whether predictions are accurate, robust, and dependable enough for a public-sector policy setting.
Option A is useful for software quality but does not validate predictive performance. Option B is weaker because a single validation exercise can miss important failure modes, bias, or context-specific weaknesses.
Option D supports engineering discipline, but continuous integration testing focuses more on code and deployment workflow than on validating model prediction quality itself. PMI's CPMAI framework emphasizes comprehensive evaluation design, iteration, and addressing performance issues such as drift and changing conditions. That makes broad and varied test coverage the most PMI-aligned approach to model validation. In practical terms, diverse test cases provide stronger evidence that the model will generalize beyond a narrow sample and support trustworthy decision-making.

질문 # 121
......

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