Pretty sure it's D since data engineering is what drives the whole environment for AI projects. Data scientists are key too, but if you don't have solid pipelines and infrastructure, their models won't even get decent data. Thoughts?
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
Discussion
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?
Yeah, it's D. Data engineers are the core staff for this kind of setup.
D is the call here. Data engineering is the backbone for big data environments, not much debate if that's the scope.
Option D. but is this question only about engineering or does it include the analytics environment too? If they want the most critical for just building and maintaining data pipelines, D for sure. But if it's broader, maybe B or E?
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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
Discussion
B, not C
My pick: C makes sense since if the pattern is the same, reusing should work.
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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
Discussion
D
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.
Its D, the question says to uncover insights which is classic data mining or analytics, not just cleanup like C.
C , since you can't really analyze data until it's prepped and clean. Data preparation comes before mining or analytics, so pretty sure C would be the right starting point if we're talking approach.
I don't think it's C, since data prep is only about cleaning and organizing. D fits better because analytics is all about actually finding those insights. Saw similar wording on some practice sets. Anybody see a reason it wouldn't be D?
Is the focus on just finding patterns in the data, or are we supposed to clean and get it ready first? If they want only the main analysis step, I'd see D, but if prep is needed first maybe C fits better.
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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
Discussion
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?
Does the question specify if the bot needs to interact with the agent in real time, or could it run tasks in the background without user input? If the agents need help during live calls, B (Attended bot) makes sense, but if it's just doing after-hours processing, D would fit.
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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
Discussion
Option C
Maybe B
C tbh
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Q: 16
When looking to implement AI to help break the Digital Transformation logjam, it's important to:
Options
Discussion
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.
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?
Totally agree, A is key here. If the organization's culture doesn't support AI, any tech or process change just won't stick. Pretty sure this is what PMI pushes in most of their frameworks.
Its A. Official guide and PMAI practice both point to culture as the root issue for transformation logjams.
A won't be true if leadership isn't aligned-even a strong culture can't fix bad direction. Anyone else run into that?
A is the main thing here, since no tech or process change works if people aren’t on board with AI. Culture drives everything else, I think. C is tempting but culture really comes first. Anyone disagree?
A
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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
Discussion
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.
Option C Saw a similar scenario in practice exams, and official guides discuss bias as a factor.
Wait, could this be C? Maybe it's just an employee bias against robots in general rather than anything with its appearance.
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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
Discussion
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.
A tbh
B. not A
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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
Discussion
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.
B tbh. I figured scope, budget, and timeline are always considered first before any tech is chosen, even for AI/ML. That’s also what most project management guides talk about. If anyone’s got a PMI manual handy or can share which exam section covers when to pick AI specifically, that'd be helpful. Not totally sure though, open to correction.
Probably C. It's about checking for cognitive technology and a valid AI/ML use case. Not just automation or generic project fit.
D Nice, this question is really clear about the options.
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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
Discussion
C or A
Not 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?
Not 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
This looks like one from my exam last year in some practice tests, pretty sure the best choice here is A.
Its A
Why not focus on data-centric best practices first? Treating AI like traditional software is the common trap here.
Option B maybe? I figure making Agile work for AI could help if the team just needs a tweak, but not totally sure. Anyone else trying B in practice exams?
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Question 11 of 20 · Page 2 / 2