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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Governance and Ethics | 20% | - AI ethics principles and frameworks - Responsible AI practices - AI governance structures - Bias identification and mitigation - Transparency and explainability - Regulatory compliance considerations |
| Topic 2: AI Risk and Performance Management | 20% | - AI-specific risk identification - Monitoring and maintenance planning - Technical debt in AI projects - AI failure modes and mitigation - Model performance metrics |
| Topic 3: AI Fundamentals and Context | 15% | - AI business value and use cases - Types of AI (Narrow AI, General AI, Generative AI) - AI concepts and terminology - AI history and evolution - AI technologies and techniques overview |
| Topic 4: AI Project Lifecycle | 25% | - Model testing and validation - Model development and training - AI deployment and monitoring - AI project planning and scoping - Iterative and agile approaches for AI - Data acquisition and preparation |
| Topic 5: AI Team and Stakeholder Management | 20% | - Communication in AI projects - Managing AI specialist expectations - Stakeholder engagement strategies - AI team roles and skills - Cross-functional collaboration |
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NEW QUESTION # 63
A telecommunications company is preparing data for an AI tool. The project team needs to ensure the data is in the right shape and format for model training. In addition, they are working with a mix of structured and unstructured data.
Which method will address the project team's objectives?
Answer: B
Explanation:
According to PMI-CPMAI, preparing data for AI models involves ensuring that data from multiple sources and of multiple types is brought into a consistent, machine-readable, and model-ready form. The guidance highlights that AI projects frequently work with both structured (tables, records) and unstructured data (text, logs, documents) and that "standardization and transformation pipelines are required so that downstream models receive inputs with well-defined schemas, formats, and encodings." Employing a data transformation tool to standardize formats supports exactly this objective. Such tools can normalize date/time formats, unify encoding, align units and categorical labels, and transform unstructured content into structured features or embeddings, all within controlled and repeatable pipelines. PMI emphasizes establishing these pipelines as part of the data readiness and MLOps practices so that the training and inference stages both see data in the same standardized shape. While converting unstructured data into structured form is often part of this process, the broader requirement is end-to-end standardization rather than one-off conversions. A transformation tool also supports governance and traceability by documenting how raw data is transformed. For these reasons, the method that best addresses the project team's stated objective-ensuring that data is in the right shape and format for model training across mixed data types-is employing a data transformation tool to standardize formats.
NEW QUESTION # 64
A project team is preparing to move to the next phase of their AI project. The team needs to ensure that all transparency and explainability requirements are met.
Which activity should the project team perform?
Answer: A
Explanation:
PMI-CPMAI highlights transparency and explainability as core aspects of responsible AI. Transparency requires that stakeholders can understand how and why an AI system reaches its outputs, including underlying logic, features used, limitations, and assumptions. Explainability practices include documenting model design choices, data lineage, performance metrics, and decision rules in a way that is meaningful to technical and non-technical audiences.
PMI's guidance on responsible AI and governance stresses the need to capture and maintain thorough documentation of AI decision-making processes throughout the lifecycle. This documentation typically covers: model architecture, training data characteristics, feature importance, decision thresholds, known failure modes, conditions under which performance degrades, and interpretability artifacts (e.g., example explanations, model cards, or similar summaries). It serves as the primary mechanism for meeting transparency requirements and supporting audits, risk review, and stakeholder communication.
While data quality, ethical guidelines, and feedback mechanisms are all important, they address different aspects (reliability, values, and continuous improvement). The activity that directly ensures transparency and explainability requirements are met is documenting the decision-making process of the AI model.
NEW QUESTION # 65
A project team is using a generative AI assistant to draft stakeholder communications. The drafts are often generic and miss project constraints. What is the most likely cause?
Answer: C
Explanation:
PMI guidance on using GenAI highlights that prompts must provide context, guidance, and constraints; otherwise outputs tend to be vague or unhelpful. If stakeholder communications miss constraints (scope boundaries, timeline, dependencies, risk posture), the most likely cause is insufficient prompt specificity-e.
g., missing audience, intent, tone, project phase, constraints, and success criteria. PMI explains that the utility of GenAI outputs is strongly tied to the granularity of input: when prompts lack detail, results often become generic and misaligned with the real need. In CPMAI-aligned execution, this is addressed by iteratively refining prompts (diverge then converge), adding structured context such as assumptions, constraints, and acceptance criteria, and validating outputs against governance expectations for accuracy and appropriateness.
Compute (C) may affect latency, not relevance; "model efficiency" (B) is not a driver of generic content; monitoring (D) improves trustworthiness rather than causing generic outputs. The PMI-consistent diagnosis is insufficient contextual prompting.
NEW QUESTION # 66
A team needs to identify which parts of the project they are working on will require AI and which will not. In addition, they need to determine technology and data requirements.
Which method should be used?
Answer: A
Explanation:
PMI-CPMAI describes a very practical early-stage activity: breaking down a solution into components or sub-functions and then deciding which components actually require AI and which do not. This is often referred to as a components-based analysis. The idea is to decompose the overall workflow or product into units such as data ingestion, preprocessing, prediction, rule-based decisioning, user interface, reporting, and integration layers.
For each component, the team asks:
Does this require cognitive capability (learning from data, pattern recognition, probabilistic reasoning)?
Or can it be handled by conventional software, rules, or existing systems?
At the same time, they identify technology and data requirements: data sources, data quality, storage, pipelines, compute needs, and integration points for each AI-relevant component. PMI-CPMAI ties this directly into later tasks such as technical feasibility, architecture design, and MLOps planning.
Detailed data mapping (option A) is useful but focuses mainly on information flows, not necessarily on AI vs non-AI partitioning. Technical feasibility assessment (option B) evaluates whether a proposed AI approach is realistic but presumes that the AI portions are already identified. Only components-based analysis (option C) simultaneously answers "which parts need AI, which do not, and what are the tech/data needs for each?", which matches the scenario precisely.
NEW QUESTION # 67
A telecommunications company is adopting an AI-based customer service chatbot. They are concerned about potential quality issues affecting customer satisfaction.
What should the project manager do?
Answer: D
Explanation:
From a PMI-CPMAI perspective, concerns about quality and customer satisfaction must be addressed first at the planning level, not only reactively once the chatbot is live. For AI-enabled services such as a customer service chatbot, the project manager is expected to define a formal quality management approach that covers: what "quality" means for this AI system (e.g., accuracy of responses, relevance, tone, response time), how it will be measured, and which controls and tests will be applied throughout the lifecycle.
A comprehensive quality assurance (QA) plan typically includes: clearly defined quality criteria and success metrics, test strategies (unit tests, conversation flow tests, usability tests, bias checks), acceptance thresholds, evaluation datasets, user journey scenarios, procedures for handling low-confidence outputs, and mechanisms for ongoing monitoring once in production. PMI-CPMAI guidance on AI lifecycle management stresses that these elements must be designed before wide rollout so that risks to customer experience are proactively controlled rather than discovered ad hoc.
Actions like beta testing, setting up monitoring teams, or doing regular performance reviews are valuable, but they are individual techniques that should exist inside an overarching QA framework. The best initial step that a project manager should take, given generalized concern about potential quality issues, is therefore to develop a comprehensive quality assurance plan for the chatbot.
NEW QUESTION # 68
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