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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q87-Q92):

NEW QUESTION # 87
You are being tasked to manage an AI project at your company and you need to identify which project to start with. What's the best way to approach this?

A. Ask key stakeholders from your group and find a small problem that would have a big return on investment and start there.
B. Go through all possible scenarios to come up with the perfect first project.
C. Find a project that requires 100% accuracy in the results and start with that one.
D. Ask key stakeholders from all groups for input about their pain points.

Answer: A

Explanation:
In Phase I: Business Understanding, CPMAI directs teams to "determine business objectives" by engaging stakeholders to surface specific pain points, estimate time-to-ROI, and prioritize projects that deliver tangible business value quickly. Focusing on a narrowly scoped problem with high ROI ensures early success, builds momentum, and validates the AI methodology before tackling larger or more complex initiatives.
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NEW QUESTION # 88
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. All would work equally well
D. Unsupervised Learning

Answer: D

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 # 89
You are working on the data engineering pipeline for the AI project and you want to make sure to address the creation of pipelines to deal with model iteration. What part of the pipeline best deals with this step?

A. ELT Pipeline
B. Data Acquisition / Ingest / Capture
C. Feature Engineering
D. Retraining Pipelines

Answer: D

Explanation:
Model iteration requires regularly updating a deployed model with new data and configuration. The CPMAI Workbook's Task: Fine-Tuning / Re-training of Pre-Trained Models prescribes defining and documenting re- training pipelines as part of the model-building lifecycle to ensure seamless iteration and ongoing performance improvements.

NEW QUESTION # 90
As an organization building an AI solution for your current customers based in NYC, but with possible plans for future expansion, how should you handle worldwide AI laws and regulations?

A. Make sure to follow relevant data, privacy, and other important laws both in the US and where you're likely to expand to in the coming year
B. You're too small of an organization to be worried about laws at the moment
C. Make sure to follow relevant data, privacy, and other important laws as it pertains to the United States
D. Make sure to follow relevant data, privacy, and other important laws as it pertains to NYC

Answer: A

Explanation:
CPMAI's Trustworthy AI - Navigating AI Regulations and Frameworks tasks require continuously monitoring "AI-relevant data privacy laws and regulations" both where you operate today and where you plan to expand . Furthermore, the Workbook's Task: Required Compliance with Regulations and Laws instructs teams to identify all laws and regulations that might apply based on location, industry, and other factors, ensuring legal and liability risks are addressed before operationalization .
=========

NEW QUESTION # 91
You're working with petabytes of data and need to make this dataset more manageable. To do this, you want to reduce the number of variables under consideration. What is the name for this process?

A. Dimensionality Reduction
B. Gradient Descent
C. Data selection
D. Multivariate regression

Answer: A

Explanation:
The process of reducing a dataset's feature set while retaining its most informative components is formally known as dimensionality reduction. CPMAI describes techniques such as Principal Component Analysis (PCA) and t#distributed Stochastic Neighbor Embedding (t-SNE) under this category, enabling teams to simplify high-dimensional data for more efficient modeling.

NEW QUESTION # 92
......

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