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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q54-Q59):

NEW QUESTION # 54
During CPMAI Phase II of your project, your team is going through their data collection needs. One team member wants to make use of pre-trained models while another member is adamantly against it.
As the project lead, what should you do?

A. Evaluate your data and use only what you have and build all models in house.
B. Evaluate your data and see if using pre-trained models make sense. If so, have the team see what pre- trained models your company already owns and use those.
C. Have one team build all models in-house and the other team use pre-trained models and see which team' s models perform better.
D. Evaluate your data and see if using pre-trained models make sense. If so, have the team do research to find the ones that best suit your project.

Answer: D

Explanation:
The Pre-Trained and Third-Party Model Usage task in Phase II: Data Understanding directs teams to first assess whether external or foundation models are appropriate given the current data and objectives. If so, they should then research and select the specific pre-trained models that best align with the project's domain, performance needs, and integration constraints. This ensures suitability before committing to fine-tuning or ensemble strategies.
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NEW QUESTION # 55
Upper management is looking to roll out a new product and wants to see if there are any patterns and insights that can be discovered from customer data. Your team has been tasked to discover these potential patterns and structures within this data.
Which type of machine learning approach would be most appropriate to pick for this problem?

A. Reinforcement Learning
B. Supervised Learning
C. Unsupervised Learning
D. All would work equally well

Answer: C

Explanation:
When the goal is to uncover hidden structures or groupings in unlabeled data, unsupervised learning-notably clustering algorithms-is the appropriate choice. CPMAI describes clustering as "an unsupervised process that partitions data into groups based on similarity" and calls for applying these methods to discover patterns in unlabeled datasets .
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NEW QUESTION # 56
Your company is insisting on running an automation project and applying AI best practices and methodologies to the project. You understand that automating things is just the act of using machines to repeat tasks, and does not require AI to achieve results. You think it is overkill but the project moves forward as planned.
What would likely have helped avoid this conflict?

A. Applying a hybrid approach of automation and AI best practices would have achieved better results.
B. Everyone on the team should understand the differences between automation and autonomous systems.
C. Nothing - running automation projects like autonomous projects is the correct thing to do.
D. Senior management should become involved in the project.

Answer: B

Explanation:
During Phase I's Cognitive Project Requirements tasks, CPMAI instructs teams to "Determine when to implement automation versus AI." Explicitly distinguishing between simple rule-based automation (RPA) and true cognitive solutions prevents misapplication of AI methodology to non-AI use cases. Ensuring everyone understands this distinction up front would have avoided misalignment on methodology.
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NEW QUESTION # 57
In order for Supervised Learning approaches to work, they must be fed clean, well-labeled data that the system can use to learn from examples. But how do you get Labeled Data?
As a team leader at a small startup, what approach would not be beneficial when trying to gather labeled data?

A. Get your Users to Do it
B. Contract with Third Party Data Labeling Firms
C. Find a source of already labeled data
D. Hire a Contractor Workforce

Answer: A

Explanation:
The Data Labeling task in Phase III: Data Preparation specifies that teams should identify labeling methods such as using internal staff, contracting third-party labelers, leveraging pre-existing labeled datasets, or combining those modes. Soliciting end-users to label data falls outside these recommended approaches and introduces uncontrolled variability and quality issues .
=========

NEW QUESTION # 58
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?

A. Make use of this data from surveillance footage
B. Make use of Synthetic Training Data
C. Make use of data from different departments
D. Make use of this data by having employees pose in the positions required

Answer: B

Explanation:
Synthetic data is "artificially generated data that mimics real-world data, used when actual data is scarce or sensitive." Generating synthetic training images of people in the required poses allows you to rapidly augment your dataset without logistical, privacy, or labeling overhead.
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NEW QUESTION # 59
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