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Amazon MLS-C01 Exam - Topic 3 Question 122 Discussion

Actual exam question for Amazon's MLS-C01 exam
Question #: 122
Topic #: 3
[All MLS-C01 Questions]

[Data Engineering]

A Machine Learning Specialist is preparing data for training on Amazon SageMaker The Specialist is transformed into a numpy .array, which appears to be negatively affecting the speed of the training

What should the Specialist do to optimize the data for training on SageMaker'?

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Suggested Answer: C

by Gilberto at Oct 17, 2025, 12:13 PM

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Doretha
14 days ago
I agree, B seems solid. Compression helps speed.
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Gerry
19 days ago
I think option B is the best choice. Parquet is efficient.
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Eladia
24 days ago
D sounds interesting, but I doubt it helps with data format issues.
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Cristy
29 days ago
Totally agree with B, compression is key!
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Junita
1 month ago
Wait, isn't using numpy .array common? Why's it slowing things down?
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Tamala
1 month ago
Parquet is the way to go, my friends. Compress that data and watch the training speed soar!
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Suzi
1 month ago
Recordio protobuf, huh? Sounds like a magical incantation to summon a data-crunching wizard!
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Thurman
2 months ago
A could work, but I think transforming the data into the right format is more important than using batch transform.
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Christa
2 months ago
D sounds interesting, but I'm not sure if that's the right approach here. Optimizing the data itself might be a better solution.
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Katina
2 months ago
I'd go with B. Parquet is a great format for large datasets and can really speed up training.
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Mona
2 months ago
I'm pretty confident that option C is the way to go here. Transforming the dataset into the Recordio protobuf format is a common technique for optimizing data for SageMaker training, so that's the approach I'd take.
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Ashley
2 months ago
I think C might be better for speed.
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Audra
3 months ago
Option C seems like the way to go. Recordio protobuf is designed for efficient data handling in SageMaker.
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Lashawna
3 months ago
B is the way to go, Parquet is super efficient!
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Shasta
3 months ago
Option D seems a bit off-topic. Hyperparameter optimization is more about tuning the model, not optimizing the data itself. I think I'd go with either option B or C to address the data format issue.
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Lelia
4 months ago
Hmm, I'm not sure about this one. The question mentions that the numpy array is negatively affecting the speed of the training, so I'd probably try option A and use the SageMaker batch transform feature to transform the data into a DataFrame.
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Rodney
4 months ago
I'm a bit confused by this question. Does the Specialist need to compress the data or just transform it into a different format? I'm not sure if option B is the right approach here.
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Staci
4 months ago
I think I'd go with option C. Transforming the dataset into the Recordio protobuf format seems like the best way to optimize the data for training on SageMaker.
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Desiree
3 months ago
I think option B is better. Parquet format is efficient for large datasets.
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Rima
3 months ago
I’m leaning towards A. DataFrames are easy to work with in SageMaker.
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Ashton
4 months ago
I recall that hyperparameter optimization is more about tuning the model rather than optimizing data. So, I don't think option D is the answer.
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France
4 months ago
I feel like we went over this in our study group. Using SageMaker's batch transform feature seems like it could be a good option, but I'm not confident.
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Vivan
5 months ago
I'm not entirely sure, but I think transforming the dataset into Recordio protobuf format might be the right choice. It sounds familiar from the practice questions.
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Celeste
5 months ago
I remember we discussed the importance of data formats in our last class. I think using Apache Parquet could really help with speed.
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