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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Project Lifecycle Management | 25% | - Deployment and Operations (MLOps) - Model Development and Testing - Monitoring and Maintenance - Data Preparation and Management - AI Development Methodology (CRISP-DM, Agile) |
| Topic 2: AI Team Leadership and Management | 20% | - Cross-functional Collaboration - Conflict Resolution in AI Projects - Building AI Teams - Talent Management and Development |
| Topic 3: AI Program Planning | 20% | - AI Project Scoping and Feasibility Analysis - Resource Planning and Budgeting - Requirements Gathering for AI Projects - Stakeholder Identification and Analysis |
| Topic 4: Risk Management and Compliance | 10% | - AI Risk Identification and Assessment - Security Considerations for AI - Regulatory Compliance (GDPR, CCPA) |
| Topic 5: AI Fundamentals and Strategy | 15% | - AI Business Strategy Alignment - AI Concepts and Terminology - AI Ethics and Governance Frameworks |
| Topic 6: AI Program Evaluation and Optimization | 10% | - Continuous Improvement - Performance Measurement - KPI and Success Metrics |
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質問 # 24
A decision-support system is used across several organizational environments to inform outcomes that affect different population groups. Post-deployment analysis reveals consistent differences in outcomes across groups, even though the system operates as designed. Further examination shows that the data used during development reflected historical patterns that were uneven across those groups. Before drawing conclusions or proposing next steps, reviewers must correctly interpret the underlying reason for the observed behavior.
Which AI failure mode best explains outcome patterns that arise from historical data reflecting existing structural imbalances?
正解：C
解説：
This scenario describes a classic case of algorithmic bias rooted in historical data . The system is functioning correctly from a technical standpoint, but the training data reflects existing societal or structural inequalities , which are then reproduced in the model's outputs.
Bias and fairness issues occur when:
Training data contains imbalances across demographic or population groups Historical patterns encode discrimination or unequal access/opportunity The model learns and perpetuates these patterns in predictions or decisions This leads to systematic differences in outcomes , even without explicit errors in the algorithm.
Other options are not appropriate:
Overfitting relates to memorizing training data and poor generalization, not systemic group disparities Data drift refers to changes in data distribution over time after deployment Edge case failures involve rare or unusual scenarios, not consistent group-level differences CAIPM governance principles emphasize that identifying bias requires understanding data provenance and historical context , not just model performance metrics.
Therefore, the correct answer is Bias and fairness issues , as it directly explains outcome disparities driven by structural imbalances in historical data.
質問 # 25
As part of a newly formalized AI talent development strategy, an enterprise identifies a group of Business Analysts for advanced capability building. These individuals are trained to configure AI tools, tailor workflows to business needs, and act as intermediaries between everyday users and highly technical AI engineering teams, while operating within established governance and risk boundaries. According to the AI talent development framework, which talent tier does this group most accurately represent?
正解：D
解説：
In the CAIPM AI talent development framework, organizations typically classify AI capabilities into tiers such as AI-Aware Workforce, AI Practitioners, AI Specialists, and AI Architects. Each tier represents increasing levels of technical depth, responsibility, and influence in AI adoption.
The group described in the scenario aligns most closely with AI Practitioners. These individuals are not deeply technical engineers but possess sufficient expertise to configure AI tools, customize workflows, and translate business needs into practical AI applications. They serve as a critical bridge between business users and technical teams, enabling effective adoption and operationalization of AI solutions within governance boundaries.
Option C, AI-Aware Workforce, refers to general employees who understand AI concepts but do not actively configure or implement solutions. Option D, AI Specialists, includes highly technical professionals such as data scientists and machine learning engineers who build and optimize models. Option B, AI Architects, operate at a strategic level, designing enterprise-wide AI systems and governance frameworks.
CAIPM emphasizes the importance of AI Practitioners in scaling AI adoption, as they ensure that tools are effectively integrated into business workflows while maintaining compliance and governance standards.
Therefore, the described group is best categorized as AI Practitioners.
質問 # 26
Michael Turner, an Enterprise AI Program Lead at a multinational technology company, structured the initial rollout of a new AI productivity platform by enabling it first within individual departments. Each function received customized training and ownership for adoption. However, within weeks, teams reported inconsistent workflows, handoff delays between departments, and confusion when collaborating on shared processes that spanned multiple functions. These issues slowed enterprise-wide adoption despite strong uptake within individual teams. Based on this outcome, which rollout sequencing approach most directly contributed to the problem encountered?
正解：A
解説：
The rollout strategy described is clearly department/function-based , where each business unit adopts the AI solution independently with customized training and ownership. While this approach can drive strong local adoption, it often creates silos , leading to inconsistencies in workflows, standards, and collaboration across departments.
The key issue highlighted in the scenario is cross-functional friction -handoff delays, inconsistent processes, and confusion when workflows span multiple departments. This is a known drawback of department-based rollout sequencing, where each unit optimizes locally without ensuring enterprise-wide alignment.
CAIPM emphasizes that while department-based rollouts can accelerate early adoption, they must be carefully managed to avoid fragmentation. For enterprise-wide systems, especially those supporting shared processes, approaches such as use-case-based rollout or coordinated hybrid strategies are often more effective in maintaining consistency.
Other options are less relevant:
Geography-based rollout would create regional differences, not functional workflow conflicts.
Use-case-based rollout focuses on end-to-end processes, which would reduce cross-functional issues.
Hybrid approaches aim to balance these challenges rather than cause them.
Therefore, the correct answer is Department/Function , as it directly explains the siloed adoption and resulting cross-functional inefficiencies.
質問 # 27
A retail chain has moved beyond random experimentation to address specific business problems. Elena, the Director of Digital Strategy, notes that while several departments have successfully launched targeted pilots and executive leadership is now actively monitoring the results, the overall approach remains fragmented. She observes that governance relies on informal agreements rather than policy, and data pipelines vary significantly between teams, making repeatability difficult. Which AI maturity stage characterizes this state of high intent but inconsistent execution?
正解：B
解説：
According to the CAIPM AI maturity model, organizations progress through stages such as Initial, Emerging, Defined, and Managed, each representing increasing levels of structure, governance, and scalability. The scenario clearly indicates that the organization has moved beyond the Initial stage, as it is no longer experimenting randomly and has begun targeted AI pilots aligned with business problems.
However, the presence of fragmented execution, inconsistent data pipelines, and reliance on informal governance indicates that the organization has not yet reached the Defined stage. In a Defined stage, processes, governance frameworks, and data standards are formalized and consistently applied across teams, enabling repeatability and scalability.
The described environment reflects the Emerging stage, where organizations demonstrate growing intent and early success through pilots, and leadership begins to engage actively. However, execution remains inconsistent, standards are not yet institutionalized, and coordination across teams is limited. This stage is often characterized by experimentation evolving into structured initiatives, but without enterprise-wide alignment or formal governance mechanisms.
Option D, Managed, represents a more advanced stage where processes are optimized, measured, and continuously improved, which is not evident here. Therefore, the organization's condition of high intent but inconsistent execution aligns best with the Emerging maturity stage.
質問 # 28
Mr. Garp, Head of Revenue Analytics, is reviewing a decision-support system used by pricing teams in the organization. The system evaluates various pricing scenarios and provides likelihood estimates to guide decision-making. Over time, improvements in the system's performance are driven by refining the way business data is represented during model updates. The system remains stable unless explicitly updated through structured, planned revisions.
As part of strategic planning, Mr. Garp must determine which type of AI technology this system uses, to decide on future investments and align them with business goals.
正解：D
解説：
According to EC-Council's AI Program Manager (CAIPM) framework, Machine Learning systems are characterized by their ability to analyze structured or semi-structured data, generate predictions such as probabilities or likelihood estimates, and improve performance through iterative model updates based on refined data representation. The scenario clearly describes a predictive decision-support system that evaluates pricing scenarios and outputs likelihood estimates, which is a core use case of supervised or probabilistic Machine Learning models.
A key indicator is that improvements occur through "refining how business data is represented during model updates." This aligns with Machine Learning practices such as feature engineering, data preprocessing, and retraining cycles. Additionally, the system remains stable unless explicitly updated, which reflects traditional ML lifecycle management where models are periodically retrained rather than continuously adapting in real time.
Deep Learning, while a subset of Machine Learning, is typically associated with complex neural networks handling unstructured data such as images, text, or speech, which is not indicated here. Generative AI focuses on content creation rather than predictive analytics, making it unsuitable. Agent Technologies involve autonomous decision-making and interaction with environments, which is also not described.
Therefore, the system best fits the definition of a Machine Learning-based decision-support system.
質問 # 29
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