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NEW QUESTION # 16
Your team is working on an image recognition project, have collected the appropriate data for the project, and have picked a neural network algorithm. They are now ready to train their model.
In which phase of CPMAI is this done?
Answer: F
Explanation:
Phase IV: Model Development is explicitly where "Model Training / Model Building" occurs. This phase includes tasks for selecting modeling techniques, conducting hyperparameter optimization, and executing the actual training runs on the prepared datasets.
NEW QUESTION # 17
You are leading a project to develop a new predictive maintenance solution. Together with your project team you determine your data needs, see if you have access to the data, and then begin working on the project.
Which phase best describes the work you are performing?
Answer: D
Explanation:
Phase II: Data Understanding is dedicated to identifying data requirements, collecting initial data, assessing data quality, and verifying that necessary datasets are accessible and fit for modeling. Determining what data you need and confirming access are the core activities of this phase .
NEW QUESTION # 18
You want to make sure that in your HR hiring system that applicants have the ability to contest the result. In what layer of the Trustworthy AI framework do we address this need?
Answer: D
Explanation:
In CPMAI's Trustworthy AI requirements, the Explainable AI layer specifically covers "legal, compliance, and risk considerations [that] might require that the AI system used for decision-making ... provide some level of explainability for audit, root cause analysis, or other purposes." Providing applicants with the ability to contest hiring decisions depends on furnishing clear, human-understandable explanations of how and why the model arrived at its result-exactly the focus of the Required AI Explainability Considerations task.
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NEW QUESTION # 19
During which phase of an AI project should you consider Trustworthy AI considerations?
Answer: C
Explanation:
CPMAI's Domain VI: Trustworthy AI is designed to span the entire project lifecycle, embedding ethics, transparency, fairness, privacy, and security checks into every phase-from Business Understanding through Operationalization-so that trustworthy practices are not an afterthought but a continuous, integrated activity .
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NEW QUESTION # 20
Your organization wants to keep an eye on AI systems for Governance purposes. What are the most crucial things to consider? (Select all that apply.)
Answer: C,D,F,H
Explanation:
Continuous System monitoring (C): Phase VI's "Monitoring and maintenance plan" requires teams to define
"What continuous monitoring and management approach and tools will be used for the model in this iteration?" to ensure the model continues to provide expected results in operation .
Data source identification (D): In Phase II: Data Understanding, teams must "Describe Data," including "Data source formats" and "Training data identification," to maintain visibility into where the model's inputs originate-essential for governance and troubleshooting .
Human chain of accountability (F): The "Model Governance Framework" task directs project teams to document "Determination of Governance Team," identifying members who will serve as the "owners" of the model and be responsible for its usage, soliciting feedback, and addressing concerns-establishing a clear accountability structure .
Key Performance Indicators (KPIs) (G): Domain V's "KPI Measurement" task mandates that teams "Align model performance with business key performance indicators" and implement ongoing KPI evaluation as part of quality assurance, providing the metrics by which governance bodies assess model health and business impact .
Options A, B, E, and H fall outside the core ongoing governance activities defined in CPMAI v7. Continuous monitoring of deployed models, clear data lineage, defined human accountability, and KPI tracking are the pillars of robust AI governance.
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NEW QUESTION # 21
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