Question # 1

You are working with a dataset that has a high number of dimensions. You’re running into issues because some dimensions don’t have enough real examples to properly train the systems for predictable results. What’s your best course of action? 

A. Keep going as planned and the problem will eventually correct itself 
B. Try to get additional data – at least 5 training examples for each dimension in the representation 
C. Try to get additional information from project lead to see how many examples per dimension are needed 
D. Try to improve the quality of your data through more preparation 

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Question # 2

You’re working on a project and are working with personally identifiable information (PII). What’s the best approach to take when it comes to collecting and using this data? 

A. Use noise reduction techniques to reduce all forms of data noise 
B. Implement a new data privacy policy 
C. Store the data in a data warehouse 
D. If this data is not needed, use Data anonymization techniques to remove it before feeding to models 

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Question # 3

Which of the following best describes the technical definition of Machine Learning? 

A. An approach to using increasing levels of intelligence to solve greater cognitive needs from unintelligent automation to autonomous business process. 
B. A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience 
E. C. The application of pre-defined rules and algorithms to solve complex problems. 
D. The use of computing technology to enable machines to gain cognitive intelligence. 

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Question # 4

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. Share prior experiences with how your last team addressed this problem and how you solved it 
B. Help senior management do research on AI vendors 
C. Share prior experiences with how your last team addressed this problem and their data quality issues 
D. Say nothing and let the team figure it out for themselves 

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Question # 5

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. Make sure to follow relevant data, privacy, and other important laws as it pertains to NYC 
C. Make sure to follow relevant data, privacy, and other important laws as it pertains to the United States 
D. You’re too small of an organization to be worried about laws at the moment 

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Question # 6

Your model has been working fine for the last three months, however recently you notice the model’s performance has greatly declined. What seems to have been overlooked in your workflow pipeline? 

A. Model retraining 
B. Model Operationalization 
C. Model Drift 
D. Model reevaluation 

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Question # 7

You want to create a model to figure out if a customer would be likely to repurchase a certain item. The project owner doesn’t want you to create anything too complicated, and you have a limited data set to work with. 

A. Ensemble models 
B. Naive Bayes 
C. Neural Networks 
D. Generative AI 

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Question # 8

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. 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. 
D. Have one team build all models in-house and the other team use pre-trained models and see which team’s models perform better. 

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Question # 9

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

A. Phase I 
B. Phase II 
C. Phase III 
D. Phase IV 
E. Phase V
 F. Phase VI 

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Question # 10

Your team is working on an NLP model and has just operationalized the first model. Your team makes updates to the model, overwrites the original model, and puts this new model into operation. However, one of the teams using the model has seen a decrease in performance and is asking to use the original model. What critical error did your team make? 

A. They did not have data governance in place 
B. They did not practice model versioning and keep all versions of the model 
C. They did not have a model retraining pipeline that took into account models 
D. They did not practice model iteration and properly iterate on the model 

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