I remember seeing something like this in exam reports, and A is pretty much always recommended. Select Columns lets you exclude the label without messing up the pipeline setup. Maybe D works if you're doing a one-off, but it's not practical for real inference scenarios. Agree?
Q: 4
You use the designer to create a training pipeline for a classification model. The pipeline uses a
dataset that includes the features and labels required for model training.
You create a real-time inference pipeline from the training pipeline. You observe that the schema for
the generated web service input is based on the dataset and includes the label column that the
model predicts. Client applications that use the service must not be required to submit this value.
You need to modify the inference pipeline to meet the requirement.
What should you do?
Options
Discussion
A , real exam/training always pushes Select Columns for removing label column cleanly from inference schema. So A.
C/D? Official guide and lab exercises both cover when to use clusters vs AKS for batch.
B imo, seen a similar scenario pop up on practice sets. Cluster fits for repeated scheduled batch jobs like nightly runs.
D imo. If you swap the dataset for Enter Data Manually without the label column, you technically remove it from the schema, which matches the requirement, right? But I might be missing some pipeline constraints here-open to other views.
Its A, that Select Columns step removes the label from the input. D is tempting but switching to Enter Data Manually isn't practical for real pipeline usage. Saw similar on practice, A did the trick.
Practice tests and the official docs both talk about Select Columns for this. That's A.
D
Its B. Pretty sure official docs and practice tests mention AML compute clusters for scheduled batch jobs like this.
Be respectful. No spam.
Question 4 of 35