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CertNexus AIP-210 Exam - Topic 6 Question 24 Discussion

A company is developing a merchandise sales application The product team uses training data to teach the AI model predicting sales, and discovers emergent bias. What caused the biased results?
B) The application was migrated from on-premise to a public cloud.
A) The AI model was trained in winter and applied in summer.
C) The team set flawed expectations when training the model.
D) The training data used was inaccurate.

CertNexus AIP-210 Exam - Topic 6 Question 24 Discussion

Actual exam question for CertNexus's AIP-210 exam
Question #: 24
Topic #: 6
[All AIP-210 Questions]

A company is developing a merchandise sales application The product team uses training data to teach the AI model predicting sales, and discovers emergent bias. What caused the biased results?

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Suggested Answer: B

Workflow design patterns for machine learning pipelines are common solutions to recurring problems in building and managing machine learning workflows. One of these patterns is to represent a pipeline with a directed acyclic graph (DAG), which is a graph that consists of nodes and edges, where each node represents a step or task in the pipeline, and each edge represents a dependency or order between the tasks. A DAG has no cycles, meaning there is no way to start at one node and return to it by following the edges. A DAG can help visualize and organize the pipeline, as well as facilitate parallel execution, fault tolerance, and reproducibility.


by Nada at Jul 14, 2024, 01:10 AM

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Sean
6 months ago
Isn't it possible that B played a role too?
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Marti
7 months ago
Definitely agree with D, bad data leads to bad results.
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Theola
7 months ago
Wait, how does training in winter affect summer sales?
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Ronny
7 months ago
I think C could also be a big factor.
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Lorenza
7 months ago
Sounds like D is the main issue here.
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Annette
7 months ago
I’m a bit confused about the cloud migration part. I don’t see how that would directly cause bias, but I guess it could affect data handling. Maybe option B is less likely?
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Demetra
8 months ago
I feel like I’ve seen a question similar to this before, and it was about seasonal trends affecting sales predictions. So, option A might be relevant here.
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Karina
8 months ago
I’m not entirely sure, but I think if the expectations set during training were unrealistic, it could lead to biased results. That makes me lean towards option C.
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Lezlie
8 months ago
I remember discussing how training data can really impact model performance, so I think option D about inaccurate training data could be the cause of bias.
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Tiffiny
8 months ago
I've seen issues with training data quality causing bias before. That's my best guess for the cause in this case, but I'll double-check the other options just to be sure.
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Tamra
8 months ago
Hmm, I'm a bit confused about what "emergent bias" means in this context. I'll need to think through the potential causes carefully.
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Leatha
8 months ago
This question seems straightforward, but I want to make sure I understand the key details before answering.
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Christa
8 months ago
Okay, I think the key here is to identify what could have led to biased results in the training data. I'll review the options and see which one seems most likely.
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Alyce
8 months ago
Hmm, this one's tricky. I'll have to think it through carefully.
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Cherelle
8 months ago
Hmm, I'm a bit unsure about this one. The question mentions HTML tags that are not XHTML-compliant, so I'll need to think carefully about which XHTML doctype would be most appropriate.
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Helaine
8 months ago
Hmm, I'm not sure about this one. I was thinking the RMSE might give me a sense of the overall error, but I'm not sure if that would tell me the direction of the error.
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Dottie
1 year ago
I'm going to have to go with C. Flawed expectations? Sounds like the team was playing a game of 'Guess the Bias' instead of 'Predict the Sales'.
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Nguyet
1 year ago
C) The team set flawed expectations when training the model.
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Jenelle
1 year ago
B) The application was migrated from on-premise to a public cloud.
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Bettyann
1 year ago
A) The AI model was trained in winter and applied in summer.
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Kelvin
1 year ago
Nah, I'm sticking with option A. Training in winter and applying in summer? That's a recipe for disaster. Looks like the team needed to invest in a seasonal wardrobe for their AI model.
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Janessa
1 year ago
Oh, I'm feeling lucky with B. Migrating to the cloud? That's bound to introduce all kinds of unexpected biases. Gotta love technology, am I right?
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Kattie
1 year ago
C) The team set flawed expectations when training the model.
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Lashonda
1 year ago
Oh, I'm feeling lucky with B. Migrating to the cloud? That's bound to introduce all kinds of unexpected biases. Gotta love technology, am I right?
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Garry
1 year ago
B) The application was migrated from on-premise to a public cloud.
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Brandon
1 year ago
A) The AI model was trained in winter and applied in summer.
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Lashanda
1 year ago
I don't know, D seems like the obvious choice to me. Inaccurate training data is a surefire way to get biased predictions. Maybe the team should have used a crystal ball instead?
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Valentine
1 year ago
Hmm, I'm gonna go with option C. Flawed expectations when training the model could definitely lead to biased results. Rookie mistake, but it happens.
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Gilma
1 year ago
Maybe the team should have set better expectations during training to avoid bias.
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Lera
1 year ago
I agree with Ernest, using inaccurate data can definitely lead to biased results.
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Ernest
1 year ago
I think the biased results were caused by inaccurate training data.
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