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| Section | Weight | Objectives |
|---|---|---|
| AI System Testing and Evaluation | - Model evaluation and monitoring
| |
| AI Operationalization and Governance | - Deployment and lifecycle management
| |
| AI Model Development and Iteration | - Model building and validation
| |
| Data for AI | - Data identification and governance
| |
| Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
|
| Data Preparation for AI | - Data cleaning and transformation
|
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NEW QUESTION # 264
A team is retraining a model and creating a new version of that model. What's the most important thing for the team to have in place before doing this?
Answer: B
Explanation:
Model governance ensures proper version control, documentation, and compliance, which are critical before retraining and deploying new model versions.
NEW QUESTION # 265
A government agency is developing an AI system to predict infrastructure failures. The data team needs to ensure that the dataset includes historical maintenance records, environmental data, and equipment usage logs. However, they are encountering issues with data from multiple sources being in different formats. Which two methods should be applied to meet the data team's objectives? (Choose two.)
Answer: C,D
Explanation:
A multiformat data integration tool helps combine maintenance records, environmental data, and equipment usage logs from different systems and formats. A data normalization process then standardizes those inputs into consistent structures and values so the dataset can be used reliably for AI model development.
NEW QUESTION # 266
An AI governance review requires ensuring that all model decisions can be traced and justified during audits. The organization must demonstrate accountability to regulators and stakeholders.
Which mechanism should the project manager implement to meet this requirement effectively?
Answer: A
Explanation:
Audit trails and documentation provide traceability, essential for governance and accountability.
NEW QUESTION # 267
You want to create a model to figure out if a customer would be likely to repurchase a certain item. The project owner doesn't want you to create anything too complicated, and you have a limited data set to work with.
Which algorithm is the best choice given these constraints?
Answer: C
NEW QUESTION # 268
A government agency is using an AI system to analyze public data for policymaking decisions.
The project manager needs to address risks related to data accuracy, privacy, and misuse. What represents the highest risk to the agency?
Answer: C
Explanation:
Within PMI-CPMAI's "Support Responsible and Trustworthy AI Efforts," privacy and security are treated as core, high-severity risks because they can trigger regulatory violations, reputational damage, and harm to individuals. PMI explicitly calls out the need to establish a privacy/security plan with encryption and access controls, privacy impact assessments, and secure handling of personally identifiable information (PII) across the AI lifecycle. If user data is stored in an unsecured database, the agency faces immediate exposure to breach, unauthorized access, and misuse--risks that are typically higher impact than stale data, vendor reliance, or even lack of transparency. In PMI guidance on AI data life cycle management, prolonged retention and weak security increase breach likelihood over time, making insecure storage a critical vulnerability that undermines trust and compliance. While transparency gaps are serious (PMI also emphasizes explainability requirements and audit trails), a direct security failure that exposes user data is generally the most acute and consequential risk because it can cause harm quickly and irreversibly, and it can halt the program through legal and policy intervention.
NEW QUESTION # 269
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