MLS-C01 Practice Question: Machine Learning Implementation and Operations

Exhibit

2024-03-15 10:23:45,234 - root - ERROR - Failed to load model: 'NoneType' object has no attribute 'shape'
Traceback (most recent call last):
  File "/opt/ml/code/inference.py", line 45, in model_fn
    model = load_model(model_dir)
  File "/opt/ml/code/inference.py", line 30, in load_model
    input_shape = model.input_shape
AttributeError: 'NoneType' object has no attribute 'shape'

Refer to the exhibit. A data scientist is deploying a PyTorch model on a SageMaker endpoint. When the endpoint is invoked, the above error appears in CloudWatch logs. What is the MOST likely cause?

⚠ Common exam trap

The MLS-C01 exam often tests the distinction between model-loading errors (missing artifact) and inference-time errors (shape mismatch, memory), so candidates mistakenly attribute a file-not-found error to a shape or memory issue instead of recognizing it as a deployment configuration problem.

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 model artifact was not properly saved or is missing from the S3 location.

The error shown in CloudWatch logs is a `FileNotFoundError` or `No such file or directory` when SageMaker attempts to load the model artifact. This indicates that the model file (e.g., `model.pth` or `model.pt`) is missing from the specified S3 bucket path or was not properly packaged during training. SageMaker endpoints require the model artifact to be present and correctly referenced in the `model_data_url` parameter; otherwise, the container fails to load the model and throws this error.

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 endpoint instance type does not support the required CUDA version.
Why it's wrong here
CUDA incompatibility would cause a different error, like 'CUDA error'.
✗
The endpoint instance does not have enough memory to load the model.
Why it's wrong here
Insufficient memory would cause an OOM error, not a NoneType error.
✗
The input tensor shape does not match the model's expected input shape.
Why it's wrong here
Shape mismatch would cause an error during inference, not during model loading.
✓
The model artifact was not properly saved or is missing from the S3 location.
Why this is correct
If the model file is missing or corrupted, load_model returns None.

Quick reference

AWS S3 Storage Class Comparison

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

Go deeper

MLS-C01 Practice Question: Machine Learning Implementation and Operations

Exhibit

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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