MLS-C01 Practice Question: Machine Learning Implementation and Operations
Exhibit
2019-10-12 15:30:01 - ERROR - Model prediction failed: Input shape mismatch. Expected (None, 10), got (None, 8).
Refer to the exhibit. A SageMaker endpoint logs this error. What is the most likely cause?
⚠ Common exam trap
It's easy for candidates to confuse a feature count mismatch with a data type error (Option C), because both involve input validation, but the error message specifically points to a shape or dimension mismatch rather than a type conversion failure.
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
✓
The input data has fewer features than the model expects
The error log indicates a mismatch between the number of features in the input data and the number of features the model was trained on. SageMaker's inference endpoint validates the input shape against the model's expected feature dimensions; when the input has fewer features, the model cannot perform the matrix operations required for prediction, resulting in this error. Option D correctly identifies this feature count mismatch as the root cause.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model is corrupted
Why it's wrong here
Corruption would cause different errors.
- ✗
There is a network connectivity issue
Why it's wrong here
Network issues would cause timeout, not shape mismatch.
- ✗
The input data type is incorrect
Why it's wrong here
The error mentions shape, not data type.
- ✓
The input data has fewer features than the model expects
Why this is correct
The error explicitly states shape mismatch: expected 10 features, got 8.
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