Amazon MLS-C01 Exam - Topic 9 Question 66 Discussion

Actual exam question for Amazon's MLS-C01 exam

Question #: 66
Topic #: 9

A power company wants to forecast future energy consumption for its customers in residential properties and commercial business properties. Historical power consumption data for the last 10 years is available. A team of data scientists who performed the initial data analysis and feature selection will include the historical power consumption data and data such as weather, number of individuals on the property, and public holidays.

The data scientists are using Amazon Forecast to generate the forecasts.

Which algorithm in Forecast should the data scientists use to meet these requirements?

AAutoregressive Integrated Moving Average (AIRMA)

BExponential Smoothing (ETS)

CConvolutional Neural Network - Quantile Regression (CNN-QR)

DProphet

Show Suggested Answer

Suggested Answer: B

by Scarlet at Nov 18, 2022, 11:40 PM

Limited Time Offer

25%

Off

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

Contribute your Thoughts:

Submit Cancel

Pearly

4 months ago

Historical data + weather factors? ETS makes the most sense!

upvoted 0 times

...

Bethanie

4 months ago

I heard Prophet is good too, but not sure if it fits here.

upvoted 0 times

...

Cecily

4 months ago

Wait, are they sure about using CNN-QR? Seems overkill for this.

upvoted 0 times

...

Delbert

4 months ago

Definitely, ETS is great for time series data!

upvoted 0 times

...

Glenn

4 months ago

I think they should go with Exponential Smoothing (ETS).

upvoted 0 times

...

Cary

5 months ago

Prophet was mentioned in our last session as being user-friendly for forecasting. I wonder if it can handle the additional features like weather and holidays effectively.

upvoted 0 times

...

Ocie

5 months ago

I practiced a similar question where CNN-QR was suggested for complex patterns. But I'm not confident if that's necessary for this scenario.

upvoted 0 times

...

Clare

5 months ago

I'm not entirely sure, but I think ARIMA is more suited for univariate data. This question seems to involve multiple features, so maybe it's not the best choice?

upvoted 0 times

...

Dorinda

5 months ago

I remember we discussed using ETS for time series data, especially when seasonality is involved. It might be a good fit here.

upvoted 0 times

...

Carlee

5 months ago

I'm not sure about this one. I know machine learning is inspired by the brain, but I'm not familiar with the specific terminology used. I'll have to make an educated guess.

upvoted 0 times

...

Brock

5 months ago

I'm leaning towards folder-level permissions (option C) since that would protect the entire folder containing the sensitive file. But I'm not 100% sure if that's the best approach.

upvoted 0 times

...

Latricia

5 months ago

Hmm, I'm not too familiar with the history of slavery in New York. I'll have to think this through carefully.

upvoted 0 times

...