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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk and Performance Management | 20% | - Model performance metrics - Technical debt in AI projects - Monitoring and maintenance planning - AI failure modes and mitigation - AI-specific risk identification |
| Topic 2: AI Fundamentals and Context | 15% | - Types of AI (Narrow AI, General AI, Generative AI) - AI technologies and techniques overview - AI history and evolution - AI business value and use cases - AI concepts and terminology |
| Topic 3: AI Project Lifecycle | 25% | - Data acquisition and preparation - AI project planning and scoping - Iterative and agile approaches for AI - Model testing and validation - AI deployment and monitoring - Model development and training |
| Topic 4: AI Governance and Ethics | 20% | - Regulatory compliance considerations - AI governance structures - Bias identification and mitigation - AI ethics principles and frameworks - Responsible AI practices - Transparency and explainability |
| Topic 5: AI Team and Stakeholder Management | 20% | - Communication in AI projects - Stakeholder engagement strategies - AI team roles and skills - Managing AI specialist expectations - Cross-functional collaboration |
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NEW QUESTION # 94
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?
Answer: C
Explanation:
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.
NEW QUESTION # 95
An AI project team is assessing the scalability of a healthcare solution. Which factor should the project manager consider to help ensure the solution is scalable?
Answer: D
Explanation:
Scalability in AI initiatives is defined within PMI-CPMAI as the solution's ability to maintain performance, reliability, and accuracy when subjected to increased data volume, user demand, or computational workload.
The PMI AI Management Framework emphasizes that an AI system must be architected to "expand capacity, data throughput, and model processing without degradation of service quality" (PMI-CPMAI Learning Path:
AI Solution Design and Implementation).
PMI further states that when assessing scalability, project managers must evaluate whether the AI system can
"adapt to higher-than-forecast usage levels, larger datasets, and future feature growth using modular and distributed architectures." The official guidance notes that scalable AI solutions often rely on elastic cloud environments, containerized deployments, and horizontally scalable compute layers. This is captured in PMI's explanation that "AI performance must remain stable as demand increases, requiring testing against progressively higher loads to validate computational capacity, latency thresholds, and throughput expectations" (PMI-CPMAI: AI Technical Foundations).
The project manager's responsibility includes verifying that the model pipelines, data ingestion systems, and inferencing services continue to operate effectively under expanded operational demand. PMI stresses that this factor-ability to handle increased loads-is the cornerstone of scalability evaluation, whereas regulatory compliance, human oversight, and integration concerns, while important, relate to governance, ethics, and interoperability rather than scalability.
Therefore, the correct factor that ensures AI scalability is the solution's ability to handle increased loads.
NEW QUESTION # 96
Different AI project team members are responsible for various parts of the project, both cognitive and non-cognitive. The project manager needs to ensure effective accountability documentation.
Which method will help to ensure accurate documentation?
Answer: A
Explanation:
The PMI-CPMAI framework places strong emphasis on traceability, accountability, and documentation across the entire AI lifecycle-covering both cognitive (ML models, data pipelines) and non-cognitive components (traditional automation, rule engines, integration services). It explains that AI projects typically involve cross-functional roles-data scientists, ML engineers, domain experts, security, compliance, and operations-and that "clear accountability requires that decisions, changes, and artifacts be documented in a way that is shared, searchable, and version-controlled across the team." To achieve this, PMI-CPMAI recommends centralized documentation repositories (for example, a single documentation platform or system-of-record) where all contributors can log design decisions, assumptions, model versions, data lineage, approvals, and test results. Centralization reduces fragmentation, ensures a "single source of truth," and supports audits, governance reviews, and handovers. Periodic reviews by the project manager improve quality but do not, by themselves, create systematic accountability. Splitting protocols for cognitive vs. non-cognitive parts can introduce silos and inconsistencies, and a separate documentation team may distance those doing the work from owning the records.
By contrast, using a centralized documentation system accessible to all team members aligns directly with PMI-CPMAI's call for integrated, lifecycle-wide documentation: every role remains responsible for its own artifacts, but all content lives in a shared, governed environment, enabling accurate, up-to-date accountability documentation.
NEW QUESTION # 97
A manufacturing firm plans to use AI to predict equipment failures. The team can access sensor data but it contains many missing values and out-of-range readings. What should the project manager prioritize first?
Answer: A
Explanation:
PMI-CPMAI stresses that AI delivery is data-driven and iterative, and that teams must manage the Data Understanding work to identify appropriate datasets and validate quality before model development. Missing values and out-of-range readings can materially distort training and inference, so the PMI-aligned priority is to characterize the data: understand sources, sampling frequency, sensor health, definitions, and the nature of missingness (random vs. systematic), then define cleansing/imputation and anomaly-handling strategies as part of data preparation. Deploying quickly (B) increases operational risk and rework. Ignoring the data (C) undermines the predictive objective. UI design (D) is valuable but secondary to data readiness in AI projects.
PMI's methodology supports a disciplined approach: understand and assess data first, then prepare/transform it, then evaluate model performance using agreed metrics and governance controls.
NEW QUESTION # 98
A project manager needs to address potential ethical concerns related to data misuse within a new AI system.
The AI system will handle large volumes of personal data. In addition, the project manager needs to ensure the data is used responsibly.
Which action should the project manager take?
Answer: D
Explanation:
The best answer is B. Create a detailed data usage policy. In PMI's CPMAI framework, trustworthy AI requires more than technical security controls. It also requires clear rules for how data may be collected, accessed, shared, retained, and used responsibly, especially when personal data is involved. PMI's official exam content outline includes establishing governance protocols for personally identifiable information, monitoring regulatory and policy compliance, coordinating with legal and compliance teams, and ensuring privacy and secure handling across the AI lifecycle.
A detailed data usage policy directly addresses the core issue in the question: ethical concerns about misuse. It defines acceptable and unacceptable uses of personal data, clarifies accountability, and supports responsible behavior by everyone involved in the AI system. PMI's trustworthy AI guidance also emphasizes governance, responsibility, transparency, and ethics as foundational elements for building AI systems people can trust.
Option A is important, but access controls mainly restrict who can reach the data; they do not fully define responsible use. Option C is useful but too broad and ongoing rather than the most direct action. Option D improves visibility, but reporting alone does not prevent misuse. A clear data usage policy is the strongest first control for ethical and responsible data use.
NEW QUESTION # 99
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