Free CPMAI_v7 Practice Test Questions and Answers (2026)

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Q: 1
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?
Options
30 comments in the community discussion
1
C tbh
1
Decline like that usually means A. Retraining step was skipped here, not totally sure though since drift is a close distractor.
Q: 2
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. Which algorithm is the best choice given these constraints?
Options
37 comments in the community discussion
6
B. saw this type of question on a practice and it was Naive Bayes because it's easy and works fine with small datasets. Not 100% if accuracy is top but for constraints given I'd stick with B.
5
B. Saw this type of question pop up in some practice exams. Naive Bayes works great when you have limited data and want something simple, so that's why I'd choose B here. Open to other takes but pretty sure this is what they want.
Q: 3
An organization is to undertake a multi-pattern AI project. They want to build a robot that is able to roam the halls as well as converse with employees and answer basic questions. What is the best approach for handling this project?
Options
29 comments in the community discussion
1
A is wrong, C looks more tailored for projects with tightly connected but separate requirements. But I keep thinking A makes sense because each pattern could be treated like its own little project phase by phase. Maybe I’m missing a CPMAI hybrid nuance though. Anyone else prefer A for clarity?
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Hybrid model is usually what official guides push for multi-pattern projects like this. So C fits best, since CPMAI recommends combining and splitting phases as needed. Not 100 percent but that's what I keep seeing in practice exams, anyone disagree?
Q: 4
You have been receiving customer data for the past six months. However recently you notice that this data has drastically changed due to the upcoming holiday season. What seems to be taking place?
Options
29 comments in the community discussion
5
Option C. The question just describes the data changing, not model outputs shifting, so data drift fits best.
2
C , since only the incoming data has changed due to the holiday season. No mention of model accuracy or prediction issues, so not D in this case. Pretty sure about this but open to pushback.
Q: 5
Enhancing and cleaning data is an important action during which phase of CPMAI?
Options
34 comments in the community discussion
6
C . Had something like this in a mock and Phase III was all about data prep, cleaning, and enhancement before modeling. Makes sense since prepping the data is key at that stage. Open to other takes but C fits best here.
2
I get why people keep picking C, but D feels right to me for this one.
Q: 6
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?
Options
41 comments in the community discussion
6
Option B
4
Makes sense to pick B, not having model versioning means you can't recover the original when needed.
Q: 7
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?
Options
36 comments in the community discussion
2
A imo, had something like this in a mock. Dimensionality reduction is literally the process for cutting down variables, not rows. Pretty sure that's what PMI wants here, but open to pushback if I'm missing some nuance.
1
Had something like this in a mock before, and A (Dimensionality Reduction) was the pick since it specifically means dropping variables/features to simplify huge datasets. Gradient descent is more about optimization, not reducing variables. Pretty sure it's A, but let me know if you see it differently.
Q: 8
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?
Options
37 comments in the community discussion
2
A tbh. Most recommenders use KNN/collaborative filtering for exactly this, but if it had talked about predicting intent or super complex user vectors, C could possibly edge in. Here, classic approach wins unless "best" means something unusual.
2
Its A, K Nearest Neighbor is classic for collaborative filtering tasks like these. Neural nets are overkill here. Anyone disagree?
Q: 9
Your team is running a simulation-based optimization exercise to increase routing efficiency. Learning for this exercise is done through "trial and error". Which type of machine learning approach is being leveraged for this exercise?
Options
42 comments in the community discussion
2
Not C here. The mention of "trial and error" is a giveaway for B (Reinforcement Learning), since that approach learns by interacting with the environment and getting feedback, not from labeled data sets like Supervised Learning. D is a distractor, because only reinforcement fits that scenario directly. Open to other
2
B , trial and error is classic reinforcement learning. C is tempting but that's usually about labeled data so it's a trap option here.
Q: 10
Enhancing and cleaning data is an important action during which phase of CPMAI?
Options
34 comments in the community discussion
6
C . Had something like this in a mock and Phase III was all about data prep, cleaning, and enhancement before modeling. Makes sense since prepping the data is key at that stage. Open to other takes but C fits best here.
2
I get why people keep picking C, but D feels right to me for this one.
Q: 11
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?
Options
9 comments in the community discussion
1
For me, D since the question is all about the Data Engineering environment. Data engineers handle all the heavy lifting with pipelines and processing so the rest of the AI stack can work. B looks tempting but the wording narrows it to engineering, not scientists or management. Agree?
1
Yeah, it's D. Data engineers are the core staff for this kind of setup.
Q: 12
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?
Options
2 comments in the community discussion
B, not C
My pick: C makes sense since if the pattern is the same, reusing should work.
Q: 13
Senior management has tasked your group to analyze a data set to uncover insights into the dat a. What is the best approach to use to do this?
Options
11 comments in the community discussion
1
D
1
Totally makes sense, D. Since management wants insights, that’s classic data mining/analytics. C would just get the data ready, but D actually finds the patterns. Pretty confident here.
Q: 14
Your team is looking to develop an RPA bot to help assist call center agents while on providing support. What type of bot should your team be creating?
Options
9 comments in the community discussion
1
Nah, not D here-a lot pick it as a trap. B fits since it's for real-time agent support.
Its D for me since unattended bots can handle repetitive back-end tasks without agent involvement. Unless "assist" strictly means real-time, this could fit batch processing needs too. Maybe I'm missing a nuance but seems valid if the support is post-call related, agree?
Q: 15
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?
Options
8 comments in the community discussion
3
Option C
1
Maybe B
Q: 16
When looking to implement AI to help break the Digital Transformation logjam, it's important to:
Options
12 comments in the community discussion
2
A . Without the right culture for AI, even the best tools or patterns won't help break a transformation logjam. C sounds like an implementation step, but PMI really stresses culture as a foundation. Pretty sure that's what they're looking for here, but open to correction.
1
C or D here, honestly. If you're actually breaking a digital transformation logjam, I feel like figuring out the AI patterns needed (C) is practical, since you want to match tools to processes. Maybe I'm missing something on why culture (A) outweighs implementation details?
Q: 17
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?
Options
4 comments in the community discussion
5
B or C? C is about general bias, but the "creepy" comment really fits with the Uncanny Valley (option B) where people get put off by robots that look too close to human but not quite right. Pretty sure it's B here.
C , since bias toward the robot could easily explain why employees avoid it. The term "creepy" might just reflect discomfort or prejudice, not necessarily the uncanny valley. Not 100% sure though, as B is tempting.
Q: 18
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?
Options
6 comments in the community discussion
6
Makes sense to pick A here. Descriptive analytics is all about summarizing and finding patterns in past data, which fits what the question is asking. Predictive would be more for forecasting future trends. Pretty sure about this, but open to other thoughts.
1
A tbh
Q: 19
You have just joined a team and they are working on a new project. The project lead isn't sure what type of technology should be used on this project - AI or a traditional software development approach. What is the best way to determine if you have the criteria for a good AI/ML Project?
Options
7 comments in the community discussion
1
C imo. You need to check if the problem actually requires cognitive/AI tech, not just automation like in A. Choosing B is tempting since scope/budget is always important, but it's not specific to AI/ML projects. Open if anyone disagrees.
Saw something like this on a practice exam, it was definitely C.
Q: 20
A team has started working on their first AI project and they are running this project like a traditional software development project. About two months into the project the team is hitting some major issues, and you're tasked with coming in to help manage this project. Immediately you realize that AI projects need to be treated like data centric projects. What's the next best course of action?
Options
9 comments in the community discussion
1
C or ANot totally confident but feels like A is what they're after since the main problem is the approach, but C could be relevant if skills are lacking. Has anyone seen PMI ever pick "hire new team" as preferred?
A
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