Q48. 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?

Q49. The team is working to build a data preparation pipeline for the conversational chatbot project. Which phase of CPMAI is this done?

Q50. Data Engineering is 80%+ of most AI projects, so building a good Data Engineering Environment is key to AI Project Success. As the manager of this project, you need to make sure you have correct staffing needs.
What’s the most critical role to staff for in the Big Data / Data Engineering Environment?

Q51. When building your model you need to make sure you’re not only checking for performance and making sure the model is giving the expected results. You also need to make sure the model is accomplishing the business objective.
At what phase of CPMAI is this most appropriate to do this?

Q52. For AI projects the code and systems don’t matter as much as the data. In fact, big data is what’s powering much of this latest wave of AI. What’s most important for your company to consider around data?

Q53. You’re in charge of marketing at your organization and you’ve been tasked with using AI to help create marketing images. What’s a good solution for this need?

Q54. The growth of Big Data has led to a desire to be able to do more to process and extract more value from Big Data. Simply storing data and providing analytics is no longer enough anymore to remain competitive.
To keep your organization competitive, you need to:

Q55. Senior management has tasked your group to analyze a data set to uncover insights into the data. What is the best approach to use to do this?

Q56. 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?

Q57. When looking to implement AI to help break the Digital Transformation logjam, it’s important to:

Q58. Your team is working on a new project for finding the most optimal flow of warehouse robots on the warehouse floor. Which type of machine learning approach would be most appropriate to pick for this problem?

Q59. 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?

Q60. In the case that an algorithm you want to use isn’t algorithmically explainable, AI systems should try to do the following:

Q61. You’re running an AI project and want to speed up training of the model so that you can complete your current CPMAI iteration within the two week timeframe the team has set. What’s one approach to speed up model training?

Q62. Your team is working on a project and is running into some issues. You need someone on the team who is able to solve problems in environments of uncertainty, can deal with failure, and has the math and data visualization skills needed to communicate the results with others so the issues can get resolved.

Q63. Your team is using a neural network algorithm to generate a Machine Learning Model. What specific artifacts need to be included? (Select all that apply.)

Q64. 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?

Q65. Your team is planning an AI-enabled chatbot project to help reduce call center load. They are currently determining if the project can get off the ground and working through the AI Go/No Go feasibility questions.
What stage of CPMAI is the team currently working on?

Q66. You have been tasked with creating a model that will recommend products based on what other customers have similarly purchased. Which algorithm is the best choice given this situation?

Q67. Data Engineering is 80%+ of most AI projects, so building a good Data Engineering Environment is key to AI Project Success. As the manager of this project, you need to make sure you have correct staffing needs.
What’s the most critical role to staff for in the Big Data / Data Engineering Environment?