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| Section | Weight | Objectives |
|---|---|---|
| Support Responsible and Trustworthy AI Efforts | 15% | - Manage bias, risk, compliance, and societal impact - Establish ethical and governance frameworks - Ensure fairness, transparency, accountability |
| Operationalize AI Solution | 17% | - Deploy AI systems into production - Establish monitoring, maintenance, and improvement processes - Manage change, adoption, and governance post-launch |
| Identify Business Needs and Solutions | 26% | - Evaluate feasibility and value of AI solutions - Align AI initiatives with organizational strategy - Define requirements, scope, and success criteria |
| Identify Data Needs | 26% | - Plan data collection, storage, and infrastructure - Define data requirements and sources - Ensure data quality, privacy, security, and compliance |
| Manage AI Model Development and Evaluation | 16% | - Monitor performance, accuracy, and reliability - Address model drift, explainability, and limitations - Oversee model design, training, and validation |
>> CPMAI Test Questions Pdf <<
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NEW QUESTION # 308
An aerospace engineering firm is developing a machine learning model to predict component failures. The project manager needs help to ensure the training data is representative of real- world scenarios. Which method will meet the project manager's objective?
Answer: A
Explanation:
PMI's CPMAI/PMI-CPMAI guidance emphasizes that, in the Data Understanding and Data Preparation phases, the team must identify appropriate datasets, evaluate training data requirements, validate "ground truth" quality, and explicitly assess data representativeness and potential bias issues before moving forward. Using historical data from multiple sources best supports representativeness because it increases coverage across operating conditions, environments, and failure modes that occur in real deployments (different fleets, sensors, maintenance practices, and duty cycles). This directly aligns with PMI's expectation that the project manager ensures readiness of data for model development through quality checks and representativeness assessments as part of go/no-go decisioning. In contrast, relying solely on synthetic data can reduce fidelity and distort real-world distributions if not carefully validated; competitor data often has ownership and fit-for-purpose limitations; and real-time monitoring is useful operationally but does not inherently make the training dataset representative. Therefore, aggregating and reconciling multi-source historical data is the most PMI-aligned method to meet the objective of representative training data prior to model development and evaluation.
NEW QUESTION # 309
You're working on a computer vision application and realize that you do not have enough real world data for the project. You need additional data created to support your training needs.
Specifically, the images you need are of people in different poses. What is the best way to obtain this data?
Answer: C
NEW QUESTION # 310
A machine learning model shows excellent performance on training data but significantly worse results on validation datasets. The team suspects the model memorizes patterns rather than generalizing effectively. Which technique should be applied to address this issue?
Answer: C
Explanation:
Cross-validation helps evaluate generalization and detect overfitting.
NEW QUESTION # 311
One of the key elements of a data-centric methodology is the data requirements phase. During CPMAI Phase II, several unexpected issues have developed and are now threatening the data collection efforts.
What course of action might make the issue worse?
Answer: A
NEW QUESTION # 312
An IT services company is working on a project to develop an AI-based customer support system.
During data preparation, the project manager needs to clean and transform customer interaction logs. What is an effective technique to handle any missing data?
Answer: D
Explanation:
Removing records with missing values can be effective when the amount of missing data is minimal and its removal will not distort the dataset. This helps maintain clean and reliable customer interaction logs before transformation and model training.
NEW QUESTION # 313
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