hardMultiple ChoiceObjective-mapped
PMLE Practice Question: A data science team has trained a TensorFlow…
A data science team has trained a TensorFlow model on-premises using a large dataset. When they try to deploy the model to Vertex AI for online predictions, the deployed model fails to start with a ‘MemoryError’. The model artifact is 2 GB, and the machine type is n1-standard-4 (15 GB RAM). What is the most likely cause?
⚠ Common exam trap
Google Cloud often tests the misconception that model file size must be less than total machine RAM to avoid OOM errors, but the trap here is that TensorFlow's memory footprint during loading and serving is significantly larger than the artifact size due to framework overhead and graph construction.
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 is too large for the machine's memory, causing an out-of-memory (OOM) error during loading.
The model artifact is 2 GB, and loading it into memory on an n1-standard-4 machine (15 GB RAM) can still cause a MemoryError. TensorFlow models often require additional memory for graph construction, intermediate tensors, and framework overhead, which can easily exceed the available RAM, especially when the model is loaded entirely into memory before serving.
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 stored in a regional bucket and the Vertex AI endpoint is in a different region.
Why it's wrong here
Cross-region access is allowed, though not optimal; it would not cause MemoryError.
- ✗
The machine type does not support TensorFlow models larger than 1 GB.
Why it's wrong here
No such limitation exists; TensorFlow can load larger models given sufficient memory.
- ✓
The model is too large for the machine's memory, causing an out-of-memory (OOM) error during loading.
Why this is correct
The 2 GB model may require more than 15 GB RAM during loading due to overhead and intermediate structures.
- ✗
The model file is corrupted or missing dependencies, causing a crash.
Why it's wrong here
Corruption typically causes import errors, not MemoryError.
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