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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identify Data Needs | 26% | - Define data requirements and sources - Ensure data quality, privacy, security, and compliance - Plan data collection, storage, and infrastructure |
| Topic 2: Operationalize AI Solution | 17% | - Deploy AI systems into production - Establish monitoring, maintenance, and improvement processes - Manage change, adoption, and governance post-launch |
| Topic 3: Manage AI Model Development and Evaluation | 16% | - Monitor performance, accuracy, and reliability - Oversee model design, training, and validation - Address model drift, explainability, and limitations |
| Topic 4: Support Responsible and Trustworthy AI Efforts | 15% | - Establish ethical and governance frameworks - Ensure fairness, transparency, accountability - Manage bias, risk, compliance, and societal impact |
| Topic 5: Identify Business Needs and Solutions | 26% | - Define requirements, scope, and success criteria - Evaluate feasibility and value of AI solutions - Align AI initiatives with organizational strategy |
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NEW QUESTION # 57
A retail bank wants to reduce fraudulent transactions by detecting unusual card activity in near real time. Which AI capability should be used?
Answer: D
Explanation:
PMI's Seven Patterns of AI describes Predictive analytics & decision support as using data-driven learning to anticipate outcomes and support decisions under uncertainty. Fraud detection is a classic predictive use case: the system analyzes historical and current transaction behaviors to estimate the probability of fraud and recommend actions (approve, decline, escalate). In CPMAI- aligned delivery, the project manager ensures the AI capability matches the business objective and defines measurable performance metrics and thresholds (e.g., false positives, fraud loss reduction, detection latency). PMI-CPMAI also emphasizes responsible and trustworthy AI practices--particularly around privacy, governance, and monitoring--because fraud models can affect customers' access to funds and may introduce bias if training data is skewed. Predictive analytics best fits because it supports classification/risk scoring decisions; the other options focus on interaction (conversational), tailored experiences (hyperpersonalization), or self-directed control (autonomous systems).
NEW QUESTION # 58
A healthcare project manager is evaluating whether to implement an AI-powered diagnostic tool.
The initial cost is US$500,000 with an expected return on investment (ROI) of 15% within the first year. The project needs to satisfy multiple stakeholders including hospital administrators and medical staff. Which method will maximize a positive ROI for the AI implementation?
Answer: D
Explanation:
Monitoring AI model performance against key performance indicators helps confirm that the diagnostic tool is producing measurable business and clinical value after implementation.
Tracking outcomes such as diagnostic accuracy, workflow efficiency, cost savings, and adoption supports adjustments that improve the likelihood of achieving a positive return on investment.
NEW QUESTION # 59
In what way would you be using Generative AI if you used the results of the Generative AI solution to improve and accelerate your job?
Answer: A
NEW QUESTION # 60
An AI project team has completed an AI go/no-go assessment. They have discovered several technology and data factors to be insufficient. Which action should occur?
Answer: A
Explanation:
When a go/no-go assessment shows insufficient technology and data factors, the team should not proceed until key readiness concerns are addressed. Verifying data quality and stakeholder alignment helps confirm whether the project can meet business goals, technical needs, and implementation expectations before development continues.
NEW QUESTION # 61
An AI system recommends loan approval decisions. Regulators require that a qualified employee make the final determination and be able to override the system.
What control does this requirement describe?
Answer: D
Explanation:
Human-in-the-loop places a person in the decision path with authority to review, confirm, or override the system's output. This is a common requirement in regulated, high-consequence domains where full automation is not permitted.
NEW QUESTION # 62
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