Amazon Exam MLS-C01 Topic 3 Question 81 Discussion

Actual exam question for Amazon's MLS-C01 exam

Question #: 81
Topic #: 3

A data scientist is working on a public sector project for an urban traffic system. While studying the traffic patterns, it is clear to the data scientist that the traffic behavior at each light is correlated, subject to a small stochastic error term. The data scientist must model the traffic behavior to analyze the traffic patterns and reduce congestion.

How will the data scientist MOST effectively model the problem?

AThe data scientist should obtain a correlated equilibrium policy by formulating this problem as a multi-agent reinforcement learning problem.

BThe data scientist should obtain the optimal equilibrium policy by formulating this problem as a single-agent reinforcement learning problem.

CRather than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using historical data through a supervised learning approach.

DRather than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using unlabeled simulated data representing the new traffic patterns in the city and applying an unsupervised learning approach.

Show Suggested Answer

Suggested Answer: D

by Casie at Oct 21, 2023, 03:24 AM

Limited Time Offer

25%

Off

Get Premium MLS-C01 Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel

Howard

11 months ago

Hmm, that's a good point. But I'm not sure I trust the data scientist's ability to pull off a multi-agent reinforcement learning solution. Sounds a bit risky, especially for a public sector project. I'd stick with the tried and true supervised learning approach.

upvoted 0 times

...

Felton

11 months ago

But what about the stochastic error term? Shouldn't the data scientist try to account for that somehow? Maybe a combination of supervised learning and some sort of reinforcement learning approach could work better.

upvoted 0 times

...

Malika

11 months ago

I agree. Option C seems like the way to go here. Using historical data to build accurate predictors of traffic flow is a practical and effective solution. Plus, it avoids the complexity of trying to find an equilibrium policy.

upvoted 0 times

...

Elza

12 months ago

Hmm, this is an interesting question. I think the data scientist should definitely use a supervised learning approach to model the traffic patterns. The problem is clearly correlated, so finding equilibrium policies might not be the most effective solution.

upvoted 0 times

...