hardMultiple Choice
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 bucket and endpoint placement causes latency or access-permission failures, not a MemoryError during model loading. It is tempting because region mismatch is a common Vertex AI deployment pitfall, but that scenario surfaces as storage access errors, not RAM exhaustion at container startup.
- ✗
The machine type does not support TensorFlow models larger than 1 GB.
Why it's wrong here
Vertex AI imposes no fixed 1 GB TensorFlow size ceiling; n1-standard-4 provides 15 GB RAM, so a 2 GB artifact fits. The claim is tempting because machine types do bound deployable model size, but the actual limit derives from available memory, not an arbitrary 1 GB cap.
- ✓
The model is too large for the machine's memory, causing an out-of-memory (OOM) error during loading.
Why this is correct
Vertex AI loads the entire 2 GB artifact into the container's RAM before serving. The n1-standard-4 provides 15 GB, but TensorFlow runtime overhead plus deserialisation can exhaust it, so the process is killed with MemoryError rather than a configuration fault.
- ✗
The model file is corrupted or missing dependencies, causing a crash.
Why it's wrong here
A MemoryError at startup indicates the container exhausted its allocated RAM while loading the model, not a corrupt artifact or missing dependency, which would raise import or deserialisation errors instead. Corruption is tempting because it also prevents startup, but the reported symptom is specifically memory exhaustion during model loading.
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