MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker to train a model using a built-in algorithm. The training job fails with an error indicating that the algorithm expects the data to be in recordIO-protobuf format, but the input is CSV. What is the most efficient way to resolve this?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a SageMaker processing job to convert the CSV data to recordIO-protobuf format.
Using a SageMaker processing job to convert CSV data to recordIO-protobuf format is an efficient, scalable, and fully managed solution within the SageMaker ecosystem. Option A is incorrect because changing the inference data to recordIO-protobuf does not address the training data format issue. Option B is incorrect because using a boto3 script to convert locally is less efficient and not scalable compared to a managed processing job. Option D is incorrect because switching algorithms may not be desirable and does not solve the underlying data format requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change the inference data to recordIO-protobuf format.
Why it's wrong here
Inference data format is separate; training data needs conversion.
- ✗
Use a boto3 script to convert the CSV files locally and upload.
Why it's wrong here
Local conversion is less scalable and efficient than a processing job.
- ✓
Use a SageMaker processing job to convert the CSV data to recordIO-protobuf format.
Why this is correct
Processing jobs can efficiently transform data into the required format.
- ✗
Switch to a different algorithm that accepts CSV format.
Why it's wrong here
This may not be feasible if the algorithm is required.
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