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| Section | Weight | Objectives |
|---|
| Manage AI Model Development and Evaluation | 16% | - Address model drift, explainability, and limitations - Monitor performance, accuracy, and reliability - Oversee model design, training, and validation
|
| Identify Data Needs | 26% | - Define data requirements and sources - Plan data collection, storage, and infrastructure - Ensure data quality, privacy, security, and compliance
|
| Identify Business Needs and Solutions | 26% | - Define requirements, scope, and success criteria - Align AI initiatives with organizational strategy - Evaluate feasibility and value of AI solutions
|
| Support Responsible and Trustworthy AI Efforts | 15% | - Ensure fairness, transparency, accountability - Establish ethical and governance frameworks - Manage bias, risk, compliance, and societal impact
|
| Operationalize AI Solution | 17% | - Establish monitoring, maintenance, and improvement processes - Deploy AI systems into production - Manage change, adoption, and governance post-launch
|
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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q35-Q40):
NEW QUESTION # 35
You're running an image recognition project and realize that you do not have enough data of a certain type of vehicle. What is the best course of action to get the additional labeled data you need?
- A. Perform Data anonymization
- B. Perform Data Transformation & Multiplication
- C. Purchase the data from a third party
- D. Perform Data Sampling
Answer: C
Explanation:
Purchasing additional labeled data from a reputable third party is the most direct way to obtain the specific, high-quality data needed to improve model training when existing data is insufficient.
NEW QUESTION # 36
A team is getting ready to begin working on a ML project. They need to build a data preparation pipeline and someone on the team suggests they reuse the same pipeline they created for their last project. What's wrong with this suggestion?
- A. Pipelines are model operationalization need specific.
- B. Pipelines are pattern needs specific so as long as it's the same pattern then you can reuse the pipeline.
- C. Pipelines are pattern and model need specific.
- D. There is no issue. Pipelines can be reused as needed between projects
Answer: C
Explanation:
Data preparation pipelines must be tailored to the specific pattern and model needs of each project, so reusing pipelines without customization can lead to suboptimal results.
NEW QUESTION # 37
As the project manager, you are leading a brainstorming session with key stakeholders around a new Hyperpersonalization project. What's a key feature for this project that should happen to ensure success?
- A. Develop a unique profile of each type of individual, and have that profile stay the same over the lifetime of that user
- B. Develop a unique profile of each individual, and having that profile both learn and adapt over time as well as be programmed for a wide variety of purposes
- C. Develop a unique profile of each individual, and have that profile learn and adapt over time for a wide variety of purposes
- D. Develop a unique profile of each individual, and manually update that profile over time for a wide variety of purposes
Answer: B
Explanation:
For successful hyperpersonalization, individual profiles must both learn and adapt continuously while being programmed for various purposes to deliver dynamic, personalized experiences.
NEW QUESTION # 38
You just joined a new company and they want to start their first AI project. Senior management thinks the best approach is to just buy AI from a vendor. You know that AI is something you do, not something you buy. What is your next best course of action to address this?
- A. Say nothing and let the team figure it out for themselves
- B. Share prior experiences with how your last team addressed this problem and how you solved it
- C. Share prior experiences with how your last team addressed this problem and their data quality issues
- D. Help senior management do research on AI vendors
Answer: B
Explanation:
Sharing your prior experiences with how your previous team approached AI projects helps educate senior management on the complexities of AI implementation and the importance of doing AI rather than just buying it.
NEW QUESTION # 39
A team is running a forecasting project and wants to use previous user data to better predict future outcomes. However, the team does not have access to all the data they need.
Which action should the project manager take?
- A. Move forward cautiously with the understanding that there may be a need for a pause mid-project.
- B. Do not move forward until access is given to all the necessary data.
- C. Move forward while anticipating data access is given when needed. An iterative approach provides the ability to return to steps as needed later on.
- D. Move forward in order to remain on schedule with the project.
Answer: C
Explanation:
An iterative approach allows the team to begin with the available data while revisiting earlier project activities as additional data becomes accessible. This supports progress without assuming that every data dependency must be resolved before the forecasting project can start.
NEW QUESTION # 40
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