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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Fundamentals | 16% | - Concepts and terminology of Artificial Intelligence - AI capabilities and limitations - Types of AI and Machine Learning |
| Topic 2: CPMAI Methodology | 41% | - Phase III: Algorithm Selection & Model Training
|
| Topic 3: Data for AI | 13% | - DataOps concepts - Data strategy and governance - Data preparation and preprocessing |
| Topic 4: Machine Learning | 13% | - Algorithms and models (e.g., NLP, Computer Vision) - Deep Learning and Neural Networks - Supervised, Unsupervised, and Reinforcement Learning |
| Topic 5: Managing AI | 8% | - Managing AI project teams and resources - Risk management in AI projects - Stakeholder management |
| Topic 6: Trustworthy AI | 9% | - Transparency and explainability - Privacy and security - Ethical considerations and bias |
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NEW QUESTION # 42
An organization is to undertake a multi-pattern AI project. They want to build a robot that is able to roam the halls as well as converse with employees and answer basic questions.
What is the best approach for handling this project?
Answer: D
NEW QUESTION # 43
In the early stages of an AI project, the team needs to determine the types of environments and devices where the AI solution will be used. This information is crucial to ensure a successful implementation. Which action should the project manager implement first?
Answer: A
Explanation:
Drafting a detailed usage scenario analysis identifies where, how, by whom, and on which devices the AI solution will be used. This provides the practical context needed to define environments, user interactions, deployment needs, and implementation constraints early in the project.
NEW QUESTION # 44
A logistics company wants an AI system that continuously monitors sensor readings from refrigerated containers and alerts operations staff when readings deviate from expected behavior.
Which AI pattern best describes this requirement?
Answer: B
Explanation:
The patterns and anomalies pattern applies when a system identifies data points that deviate from established norms. Detecting abnormal temperature or humidity readings against expected operating ranges is a direct application of anomaly detection.
NEW QUESTION # 45
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: D
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 # 46
An AI model trained on historical transaction data performs well initially. Over time, customer behavior changes due to market conditions, causing declining prediction accuracy. The team observes a mismatch between training and current data distributions. What is this issue called?
Answer: D
Explanation:
Concept drift occurs when data patterns change over time, degrading model performance.
NEW QUESTION # 47
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