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Amazon MLA-C01 Exam - Topic 3 Question 4 Discussion

Actual exam question for Amazon's MLA-C01 exam
Question #: 4
Topic #: 3
[All MLA-C01 Questions]

A company uses Amazon Athena to query a dataset in Amazon S3. The dataset has a target variable that the company wants to predict.

The company needs to use the dataset in a solution to determine if a model can predict the target variable.

Which solution will provide this information with the LEAST development effort?

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Suggested Answer: A

by Lavonne at Jun 16, 2025, 12:33 PM

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Alona
4 months ago
Not sure about D, tuning models can be tricky without enough data.
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Alberto
4 months ago
I agree, A seems like the best choice for quick results.
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Edda
5 months ago
Wait, can Macie even create models? Sounds odd.
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Salina
5 months ago
I think B would take way too much time.
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Catherin
5 months ago
A is definitely the easiest option here!
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Felicia
5 months ago
I practiced a question similar to this, and I think selecting a model from Amazon Bedrock could be more complex than just using SageMaker Autopilot.
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Harrison
5 months ago
I feel like Amazon Macie is more for data security and not really for model training, so that option might not be right.
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Tony
6 months ago
I'm not entirely sure, but I think using custom scripts would take a lot of time compared to the other choices.
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Skye
6 months ago
I remember studying about Amazon SageMaker Autopilot and how it automates model creation, which seems like a good option here.
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Gayla
6 months ago
I think option A is the way to go. Using Amazon SageMaker Autopilot sounds like the easiest path to get the information the company needs with the least development effort. The other options seem a bit more complex for this particular task.
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Santos
6 months ago
Option D sounds interesting, but I'm not too familiar with Amazon Bedrock. I'd need to do some research to understand if that's the best approach here. For now, I'm leaning towards option A - it seems like the most straightforward solution.
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Louvenia
6 months ago
I'm a bit confused by the options here. Is Amazon Macie really a good choice for this kind of task? I'm not sure it's the right tool for model building and performance evaluation. I might lean more towards option B and implement my own scripts.
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Fannie
7 months ago
Hmm, this seems like a straightforward question. I think I'll go with option A - using Amazon SageMaker Autopilot to create a new model and report the performance. That seems like the least development effort.
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Aileen
9 months ago
Ah, the classic 'which solution requires the least effort' question. I'm torn between A and B - on one hand, I love a good DIY project, but on the other hand, I'm feeling a little lazy today.
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Aleisha
8 months ago
B) Implement custom scripts to perform data pre-processing, multiple linear regression, and performance evaluation. Run the scripts on Amazon EC2 instances.
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Lino
9 months ago
A) Create a new model by using Amazon SageMaker Autopilot. Report the model's achieved performance.
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Kati
9 months ago
Hmm, Option A seems like the easiest, but I wonder if we'd be sacrificing some control and customization. Maybe we could get the best of both worlds with Option B?
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Adell
9 months ago
Option D sounds like it could be a good fit, but I'm not familiar with Amazon Bedrock. Might need to do some research to see if it's the right tool for the job.
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Raul
8 months ago
Let's look into it and see if it's the best solution for predicting the target variable.
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Hollis
9 months ago
I agree, but we should definitely research Amazon Bedrock first to make sure it's the right choice.
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Alonso
9 months ago
I think option D could work well if we tune the model with the data.
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Fredric
10 months ago
But option A uses Amazon SageMaker Autopilot which automates the model creation process, saving time and effort.
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Janella
10 months ago
I disagree, I believe option D would require less effort and provide accurate results.
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Fredric
10 months ago
I think option A is the best choice for least development effort.
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Vicki
10 months ago
Option C sounds intriguing, but I'm not sure how much control we'd have over the model selection and tuning process. Might be a bit of a black box.
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Vallie
9 months ago
I) Option C sounds intriguing, but I'm not sure how much control we'd have over the model selection and tuning process. Might be a bit of a black box.
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Tandra
9 months ago
C) Configure Amazon Macie to analyze the dataset and to create a model. Report the model's achieved performance.
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Millie
10 months ago
B) Implement custom scripts to perform data pre-processing, multiple linear regression, and performance evaluation. Run the scripts on Amazon EC2 instances.
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Oneida
10 months ago
A) Create a new model by using Amazon SageMaker Autopilot. Report the model's achieved performance.
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Von
10 months ago
I'm more of a DIY kind of person, so Option B appeals to me more. Implementing the scripts myself will help me understand the process better.
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Linette
10 months ago
Option A seems like the easiest way to get a model and performance evaluation with minimal effort. SageMaker Autopilot takes care of all the heavy lifting.
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Giovanna
9 months ago
Let's go with Option A then. It's the most efficient solution for our needs.
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Leana
9 months ago
I agree, using Amazon SageMaker Autopilot would definitely save us time and effort in creating and evaluating the model.
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Fannie
10 months ago
Option A seems like the easiest way to get a model and performance evaluation with minimal effort. SageMaker Autopilot takes care of all the heavy lifting.
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