CertNexus Exam AIP-210 Topic 6 Question 24 Discussion

Actual exam question for CertNexus's AIP-210 exam

Question #: 24
Topic #: 6

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?

AThe AI model was trained in winter and applied in summer.

BThe application was migrated from on-premise to a public cloud.

CThe team set flawed expectations when training the model.

DThe training data used was inaccurate.

Show Suggested Answer

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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Dottie

2 months 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

23 days ago

C) The team set flawed expectations when training the model.

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Jenelle

24 days ago

B) The application was migrated from on-premise to a public cloud.

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Bettyann

1 months ago

A) The AI model was trained in winter and applied in summer.

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Kelvin

2 months 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

2 months 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

13 days ago

C) The team set flawed expectations when training the model.

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Lashonda

16 days 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

18 days ago

B) The application was migrated from on-premise to a public cloud.

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Brandon

1 months ago

A) The AI model was trained in winter and applied in summer.

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Lashanda

2 months 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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