Amazon MLS-C01 Exam - Topic 2 Question 94 Discussion

Actual exam question for Amazon's MLS-C01 exam

Question #: 94
Topic #: 2

A company is planning a marketing campaign to promote a new product to existing customers. The company has data (or past promotions that are similar. The company decides to try an experiment to send a more expensive marketing package to a smaller number of customers. The company wants to target the marketing campaign to customers who are most likely to buy the new product. The experiment requires that at least 90% of the customers who are likely to purchase the new product receive the marketing materials.

...company trains a model by using the linear learner algorithm in Amazon SageMaker. The model has a recall score of 80% and a precision of 75%.

...should the company retrain the model to meet these requirements?

ASet the target_recall hyperparameter to 90% Set the binaryclassrfier model_selection_critena hyperparameter to recall_at_target_precision.

BSet the targetprecision hyperparameter to 90%. Set the binary classifier model selection criteria hyperparameter to precision at_jarget recall.

CUse 90% of the historical data for training Set the number of epochs to 20.

DSet the normalize_jabel hyperparameter to true. Set the number of classes to 2.

Show Suggested Answer

Suggested Answer: D

The best visualization for this task is to create a bar plot, faceted by year, of average sales for each region and add a horizontal line in each facet to represent average sales. This way, the data scientist can easily compare the yearly average sales for each region with the overall average sales and see the trends over time. The bar plot also allows the data scientist to see the relative performance of each region within each year and across years. The other options are less effective because they either do not show the yearly trends, do not show the overall average sales, or do not group the data by region.

References:

pandas.DataFrame.groupby --- pandas 2.1.4 documentation

pandas.DataFrame.plot.bar --- pandas 2.1.4 documentation

Matplotlib - Bar Plot - Online Tutorials Library

by Dorathy at Jun 03, 2024, 04:36 PM

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Sherman

5 months ago

Not sure if precision should take priority here, recall is key!

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Jesusita

5 months ago

I think option A makes the most sense for the recall focus.

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Otis

5 months ago

90% recall seems a bit unrealistic with the current numbers.

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Ardella

5 months ago

Definitely should retrain to hit that 90% target!

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Rasheeda

6 months ago

The model's recall is only 80%, needs improvement.

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Jenise

6 months ago

I vaguely recall that using historical data can help improve model accuracy, but I'm not confident if just increasing epochs would solve the recall issue.

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Bettina

6 months ago

I feel like I might be mixing up precision and recall. Should we really set the target recall to 90%? I thought we were supposed to balance both metrics.

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Antonette

6 months ago

This question reminds me of a practice problem where we had to adjust hyperparameters to improve model performance. I think we should focus on recall since that's the priority here.

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Ruthann

6 months ago

I remember we discussed the importance of recall in targeting customers, but I'm not sure if 80% is enough to meet the 90% requirement.

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Kristeen

6 months ago

Hmm, I'm not sure increasing the training data size or the number of epochs is the right approach here. The model's recall and precision metrics are the more important factors to address.

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Trinidad

6 months ago

I'm a bit confused by all the different hyperparameters and model selection criteria. Maybe I should review the course materials on supervised learning models before attempting this.

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Tonette

6 months ago

Okay, let's see here. The key is to target the recall score, since we need at least 90% of the likely buyers to receive the marketing materials. I think option A is the way to go.

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Eun

6 months ago

Hmm, this seems like a tricky one. I'll need to carefully consider the model's performance metrics and how to adjust the hyperparameters to meet the 90% recall requirement.

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Gearldine

7 months ago

This looks straightforward enough. I'd go with option B and focus on optimizing the precision at the target recall level. That should help us meet the requirements.

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Chanel

7 months ago

Hmm, this seems like a tricky one. I'll need to carefully analyze the flow of each smart campaign to figure out the right modifications.

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Barney

7 months ago

Hmm, this looks like a tricky one. I'll need to think carefully about the options and what I know about IBM Cloud Pak for Data System.

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Tandra

7 months ago

This looks like a pretty straightforward Cisco UCS configuration question. I think I've got a good handle on SAN boot policies, so I'll give this a shot.

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Bette

11 months ago

I'm torn between options A and B. Maybe I should just flip a coin and hope for the best. Or maybe I should just ask the AI assistant to make the decision for me. It's not like I'm paying attention anyway.

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Darci

10 months ago

Maybe you should consider the pros and cons of each option before making a decision.

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Peggy

10 months ago

I disagree, option B might actually be the better choice in this scenario.

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Yuki

11 months ago

I think you should go with option A. It seems like the best choice based on the requirements.

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Sunshine

12 months ago

Setting the normalize_label hyperparameter to true and the number of classes to 2 seems like a strange solution. I don't think that's going to help with the recall and precision requirements.

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Mira

10 months ago

C: D) Set the normalize_label hyperparameter to true. Set the number of classes to 2.

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Hana

10 months ago

B: I agree, setting the target_recall hyperparameter to 90% seems like the right approach to meet the requirements.

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Lindsay

11 months ago

A: A) Set the target_recall hyperparameter to 90% Set the binaryclassrfier model_selection_critena hyperparameter to recall_at_target_precision.

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Mertie

12 months ago

Using 90% of the historical data for training and setting the number of epochs to 20 doesn't sound right to me. Shouldn't we be focusing on the model's performance metrics?

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Ellsworth

11 months ago

Using 90% of the historical data for training and setting the number of epochs to 20 doesn't sound right to me. Shouldn't we be focusing on the model's performance metrics?

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