Deal of The Day! Hurry Up, Grab the Special Discount - Save 25% - Ends In 00:00:00 Coupon code: SAVE25
Welcome to Pass4Success

- Free Preparation Discussions

PMI-CPMAI Exam - Topic 1 Question 9 Discussion

A manufacturing company is implementing an AI system to optimize production schedules. The project manager needs to gather the required data from machine sensors, production logs, and supply chain databases. During data collection, they notice discrepancies in machine sensor data.What should the project manager do first?
D) Implement a robust data validation and correction process.
A) Develop a data integration framework to harmonize formats.
B) Outsource data preprocessing to an external vendor.
C) Replace machine sensors for real-time data accuracy.

PMI-CPMAI Exam - Topic 1 Question 9 Discussion

Actual exam question for PMI's PMI-CPMAI exam
Question #: 9
Topic #: 1
[All PMI-CPMAI Questions]

A manufacturing company is implementing an AI system to optimize production schedules. The project manager needs to gather the required data from machine sensors, production logs, and supply chain databases. During data collection, they notice discrepancies in machine sensor data.

What should the project manager do first?

Show Suggested Answer Hide Answer
Suggested Answer: D

The best answer is D. Implement a robust data validation and correction process. In PMI-CPMAI, data understanding and data preparation require the team to evaluate training data requirements, validate data quality, perform data cleansing and enhancement, and make go/no-go decisions based on whether the data is fit for model development. When discrepancies are detected during collection, the first priority is to validate the data, identify the source of the inconsistency, and correct or isolate bad records before moving further into integration or modeling.

Option A may eventually be necessary, especially when combining sensor, log, and database sources, but harmonizing formats should not come before confirming whether the sensor data is accurate and reliable. Option B is not a first-step governance response and does not directly address the quality issue. Option C could be appropriate only if the validation process shows that the sensors themselves are faulty; replacing hardware before confirming the root cause would be premature. PMI's methodology consistently stresses data quality validation and cleansing as foundational activities in AI projects. Since the scenario explicitly mentions discrepancies, the most appropriate first action is to validate and correct the data so later integration and model-building decisions are based on trustworthy inputs.


Contribute your Thoughts:

0/2000 characters
Ivette
9 days ago
D ensures accuracy. We need reliable data for AI to work!
upvoted 0 times
...
Stephane
14 days ago
B is risky. Outsourcing can complicate things.
upvoted 0 times
...
Malissa
19 days ago
C seems extreme. Replacing sensors might not be necessary.
upvoted 0 times
...
Dewitt
24 days ago
A could work too, but fixing the data first is key.
upvoted 0 times
...
Larae
30 days ago
Agreed! Discrepancies can lead to big issues later.
upvoted 0 times
...
Luke
1 month ago
I think D is the best choice. Validating data is crucial.
upvoted 0 times
...
Roy
1 month ago
D ensures accuracy before anything else.
upvoted 0 times
...
Eve
2 months ago
B is risky. Better to handle it in-house.
upvoted 0 times
...
Candida
2 months ago
C seems extreme. Sensors might not be the issue.
upvoted 0 times
...
Hillary
2 months ago
A could work too, but validation is priority.
upvoted 0 times
...
Lavera
2 months ago
Agreed! Discrepancies need fixing first.
upvoted 0 times
...
Lorenza
2 months ago
I think D is the best choice. Validating data is crucial.
upvoted 0 times
...
Moira
2 months ago
How do we know the discrepancies aren't just a one-time glitch?
upvoted 0 times
...
Katina
3 months ago
Disagree, outsourcing might lead to more issues.
upvoted 0 times
...
Carrol
3 months ago
Surprised they didn't just replace the sensors right away!
upvoted 0 times
...
Dacia
4 months ago
A data integration framework sounds good too, but not first.
upvoted 0 times
...
Bettyann
4 months ago
I think D is the best option here. Data validation is key!
upvoted 0 times
...
Lashandra
5 months ago
I recall a case study where discrepancies led to major issues. I think implementing a validation process is crucial, so I lean towards D as well.
upvoted 0 times
...
Carma
5 months ago
Replacing the sensors sounds drastic. I feel like we should check the data quality first, so D makes sense, but I wonder if A could help too.
upvoted 0 times
...
Idella
5 months ago
I'm not entirely sure, but I think we practiced a similar question where we had to address data quality first. Could it be A instead?
upvoted 0 times
...
Ben
5 months ago
I remember we discussed the importance of validating data before any integration. It seems like D might be the best first step.
upvoted 0 times
...

Save Cancel