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CertNexus Exam AIP-210 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.


Contribute your Thoughts:

Janessa
2 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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Lashanda
4 days 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
7 days 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
21 days ago
Maybe the team should have set better expectations during training to avoid bias.
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Lera
22 days ago
I agree with Ernest, using inaccurate data can definitely lead to biased results.
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Ernest
25 days ago
I think the biased results were caused by inaccurate training data.
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