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?
APhase I
BPhase II
CPhase III
DPhase IV
EPhase V
FPhase VI
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?
AResponsible AI
BEthical AI
CTransparent AI
DExplainable AI
EGoverned AI
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?
AGoal Driven Systems
BPredictive Analytics & Decision Support and Patterns and Anomalies
CRecognition Pattern
DPredictive Analytics & Decision Support
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?
AMake sure that you can use Generative AI solutions on an edge device
BMake sure the model lives in a hybrid environment
CMake sure the model is available over a cloud-based API
DMake sure the model lives on the edge device so it can be used regardless of internet connection
Question 7
CPMAI Methodology
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Question 8
Data for AI
Question 9
Data for AI
Question 10
Managing AI
Question 11
Managing AI
Question 12
Data for AI
Question 13
CPMAI Methodology
Question 14
Managing AI
Question 15
AI Fundamentals
Question 16
Machine Learning
Question 17
Trustworthy AI
Question 18
CPMAI Methodology
Question 19
CPMAI Methodology
Question 20
Trustworthy AI
Question 21
Data for AI
Question 22
Managing AI
Question 23
Managing AI
Question 24
Trustworthy AI
Question 25
Data for AI
Question 26
CPMAI Methodology
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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?
AYou need to make sure the AI Go/No Go questions have been addressed
BNothing is wrong with this workflow. You can move to the next iteration
CTeam members should not be able to move to new projects until senior management signs off
DModel Evaluation requires continuous model evaluation, retraining, and operationalization
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?
ATake all the data your company has as well as purchase additional external data
BTake inventory of all data your company has and use the relevant data
CTake all the existing data you have and apply it to this project
DTake inventory of all data your team has and use the relevant data
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?
AData Deduping to reduce overall size and data complexity.
BData integration focused on reducing the number of data sources.
CData selection and attribute pruning to reduce overall size and data complexity.
DCareful algorithm selection that reduces the need for 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?
ANothing - running automation projects like autonomous projects is the correct thing to do.
BEveryone on the team should understand the differences between automation and autonomous systems.
CSenior management should become involved in the project.
DApplying a hybrid approach of automation and AI best practices would have achieved better results.
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.)
AProcess automation
BText summarization
CMachine Translation
DClassify and categorize content
ECode generation
FImprove search quality
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?
APipelines are model operationalization need specific.
BPipelines are pattern and model need specific.
CPipelines are pattern needs specific so as long as it’s the same pattern then you can reuse the pipeline.
DThere is no issue. Pipelines can be reused as needed between projects
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:
ADecide if it’s a statistics pattern. If it’s statistical then go with the AI project.
BDecide if it’s probabilistic or deterministic patterns. If it’s deterministic then go with the AI project.
CSee if simple rules work. If yes, then pick the right AI solution to solve the problem.
DDecide if it’s probabilistic or deterministic patterns. If it’s probabilistic then go with the AI project.
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.)
AAsk end users what information and technology they need to help them do their job better and build the tool to help with these pain points.
BHave the team that built the technology to relay to employees this tool is to augment, and not replace their jobs.
CHave upper management relay to employees this tool is to augment, and not replace their jobs.
DProvide training for everyone to have all employees feel more comfortable using the technology even if they aren’t using the technology yet.
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?
AUnattended bot
BBusiness Process Outsourcing
CAttended bot
DRPA is not the right solution to this problem
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:
AFalse Negative results
BFalse Positive results
CTrue Positive results
DTrue Negative results
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?
AData privacy issues leading to data sharing concerns
BBiased data sets leading to algorithmic discrimination
CGenerative AI models hallucinating data results
DNothing is wrong, algorithmic decisions will never be 100%
You’ve built your model and now need to see if it actually works as expected. In which phase of CPMAI is this done?
APhase I
BPhase II
CPhase III
DPhase IV
EPhase V
FPhase VI
During CPMAI Methodology Phase IV: Model Development, which of the following is not done during this phase?
AAlgorithm Selection
BModel training
CModel tuning
DModel Selection
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?
ALack of understanding the robot’s usefulness
BThe bot is falling into The “Uncanny Valley”
CBias towards the robot
DSafety and reliability issues that impact bot usefulness
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?
ADescriptive Analytics
BPredictive Analytics
CDiagnostic Analytics
DProjective Analytics
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?
AMake sure to follow relevant data, privacy, and other important laws both in the US and where you’re like to expand to in the coming year
BMake sure to follow relevant data, privacy, and other important laws as it pertains to NYC
CMake sure to follow relevant data, privacy, and other important laws as it pertains to the United States
DYou’re too small of an organization to be worried about laws at the moment
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?
AReal-time prediction
BWeb service / Microservice
CBatch prediction
DStream learning
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?
ATransparent AI
BGoverned AI
CResponsible AI
DExplainable AI
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?
AFeature Engineering
BData Acquisition / Ingest / Capture
CELT pipeline
DRetraining Pipelines
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):
AExplores a proof of concept for an AI project
BEnhance revenue
CSolves a previously unsolved problem
DImprove company competitiveness in the market
EHas the “cool” factor
FSolves an already solved problem but does it better and cheaper