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| Section | Weight | Objectives |
|---|---|---|
| Identify Business Needs and Solutions | 26% | - Problem framing and business alignment
|
| AI System Testing and Evaluation | - Model evaluation and monitoring
| |
| AI Model Development and Iteration | - Model building and validation
| |
| AI Operationalization and Governance | - Deployment and lifecycle management
| |
| Data for AI | - Data identification and governance
| |
| Data Preparation for AI | - Data cleaning and transformation
|
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NEW QUESTION # 24
You're running an AI project and want to speed up training of the model so that you can complete your current CPMAI iteration within the two week timeframe the team has set.
What's one approach to speed up model training?
Answer: C
NEW QUESTION # 25
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: A
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 # 26
You're working with a small inexperienced team on a new ML project. Choosing the best algorithm with the best settings given the training and test data is proving to be very hard for them. You lack the critical data science resources available on your team, and can't wait weeks until a data science resource becomes available to join your team. What's your best course of action?
Answer: C
Explanation:
AutoML tools automate algorithm selection and hyperparameter tuning, enabling less experienced teams to develop models efficiently without waiting for specialized data science resources.
NEW QUESTION # 27
Your company is insisting on running an automation project and applying AI best practices and methodologies to the project. You understand that automating things is just the act of using machines to repeat tasks, and does not require AI to achieve results. You think it is overkill but the project moves forward as planned.
What would likely have helped avoid this conflict?
Answer: A
NEW QUESTION # 28
At the start of an AI initiative, stakeholders are unclear about desired outcomes and success criteria. The team risks building a technically sound model that fails to deliver business value.
Which lifecycle phase should address this issue?
Answer: C
Explanation:
Problem framing aligns objectives, metrics, and stakeholder expectations.
NEW QUESTION # 29
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