CPMAIPreview

Your team is working on an image recognition system to help identify plants. They have collected a large amount of data but need to get this data labeled.
Which phase of CPMAI is this done?

You want to make sure that in your HR hiring system that applicants have the ability to contest the result. In what layer of the Trustworthy AI framework do we address this need?

Using machine learning and other cognitive approaches to understand how to take past / existing behavior and predict future outcomes or help humans make decisions about future outcomes using insight learned from past behavior / interactions / data is a core part to which pattern(s) of AI?

Your team is ready to operationalize the model they have been working on. It’s a model that is meant to be used on an “edge device”, specifically a mobile phone and the user may sometimes be in remote locations without regular access to the internet.
What’s the most important thing to consider here?

Your team is testing the NLP model they just created to make sure it’s performing as expected. Some of your team members want to move this model to production and move to the next iteration.
What’s wrong with this workflow?

You have been brought on to manage a recognition project, specifically an image recognition project, for an Autonomous Retail application. You know that you need to make sure you have sufficient data for this project.
What’s the best way to approach this?

Your team has collected petabytes of data for your AI project. As the project lead, you understand this is too much data to use for this iteration of the project.
What is the best course of action to take with this data?

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?

You have been tasked at your organization to manage a large language model (LLM) project. Identify what LLMs are useful for. (Choose all that apply.)

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?

Use cognitive technologies/AI when you can’t code the rules or you can’t scale easily with people or automation. As a good rule of thumb when deciding if AI is right for the project you should:

Recently, you implemented an augmented intelligence application at work to help employees do their job better. However, employees have been resistant to this change and aren’t using the application as expected.
What could have been done better to get the team to feel comfortable with this technology and use it? (Choose all that apply.)

Your team is looking to develop an RPA bot to help with back-office processes such as data entry. What type of bot should your team be creating?

The confusion matrix measures how the algorithm performs for a binary classification activity. As your team is running tests to evaluate model performance, they are seeing the model is incorrectly categorizing flowers as trees.
Your model is provided the following:

Your team is working on a new loan decision model that takes a number of factors and data points into consideration and then automatically approves or denies a loan. After a month in operation someone does a review and notices that the system is denying a large number of loans from a certain demographic when all other factors from people in other regions (such as age, salary, and credit score) are the same.
What is most likely happening here?

You’ve built your model and now need to see if it actually works as expected. In which phase of CPMAI is this done?

During CPMAI Methodology Phase IV: Model Development, which of the following is not done during this phase?

Your team has built a new robot that roams the halls at your organization and helps with various things such as small deliveries. However, you notice that many employees are opting not to use the robot. When you ask them why they tell you that the robot looks “creepy” and they would rather not interact with it.
What’s going on here?

Your team has been asked to summarize and highlight patterns in historical purchasing data, identifying prior performance metrics and patterns. What type of analytics is most appropriate to apply for this need?

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?

Your model is going to be used for continuous monitoring of machinery, with need for continuous, instant model predictions. What’s the most appropriate Model Operationalization approach?

Your team is working on a new facial recognition application. Since this technology has the potential to be mis-used you think it’s important to set guidelines for the proper use of this application and you want to make sure the AI system is built for some positive purpose.
What area of Trustworthy AI does this best fall under?

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?

You’re being told by upper management that you need to manage a new AI project. You need to determine the AI project fit to make sure you’re actually solving a real business problem.
During phase I: Business Understanding, you should consider at least one of the following (Choose all that apply):