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| Section | Weight | Objectives |
|---|---|---|
| Identify Data Needs | 26% | - Ensure data quality, privacy, security, and compliance - Define data requirements and sources - Plan data collection, storage, and infrastructure |
| 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 |
| Manage AI Model Development and Evaluation | 16% | - Oversee model design, training, and validation - Monitor performance, accuracy, and reliability - Address model drift, explainability, and limitations |
| Operationalize AI Solution | 17% | - Establish monitoring, maintenance, and improvement processes - Manage change, adoption, and governance post-launch - Deploy AI systems into production |
| Support Responsible and Trustworthy AI Efforts | 15% | - Manage bias, risk, compliance, and societal impact - Establish ethical and governance frameworks - Ensure fairness, transparency, accountability |
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NEW QUESTION # 271
Creating machine learning models can be complicated. Your team wants to use tools called Automated Machine Learning (AutoML) to simplify the process. You know of another team that has used AutoML tools and it's saved the team a lot of time. However, what's the one area you should not have the AutoML tool help with?
Answer: D
Explanation:
Iterative modeling and evaluation require human judgment and domain expertise to interpret results and make strategic decisions, which AutoML tools cannot fully automate effectively.
NEW QUESTION # 272
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: D
NEW QUESTION # 273
Your team is trying to determine which pattern best fits their AI problem. To do this the project team is running through the seven patterns of AI to figure out what pattern best applies to their problem.
Which of the following is the best approach?
Answer: C
NEW QUESTION # 274
In an IT services firm, the AI project team is tasked with developing a virtual assistant to support customer service operations. The assistant must integrate seamlessly with existing customer relationship management (CRM) systems and handle a variety of customer queries.
Which necessary initial task should the project manager take?
Answer: C
Explanation:
A comprehensive data audit should be performed first to identify what customer and CRM data is available, assess its quality and accessibility, and determine integration constraints. This establishes whether the virtual assistant has the reliable data foundation needed to handle customer queries effectively.
NEW QUESTION # 275
A retail bank wants to reduce fraudulent transactions by detecting unusual card activity in near real time. Which AI capability should be used?
Answer: A
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 # 276
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