The answer is that the custom container reads from a fixed /model directory, but Vertex AI mounts the model artifact at /tmp/model. This is the most likely cause because when using a custom container model path on Vertex AI, the platform sets the environment variable AIP_STORAGE_URI and mounts the saved model under /tmp/model, not at the hardcoded path the container expects. On the Google Professional Machine Learning Engineer exam, this question tests your understanding of how Vertex AI handles model artifact mounting differently for custom containers versus its default serving containers—a common trap is assuming the model path remains the same. The key technical concept is that custom containers must either read from the AIP_STORAGE_URI location or copy the model to their expected directory during startup. Remember the memory tip: "AIP points to /tmp, not /model"—if your container looks in the wrong directory, predictions will fail with a 'model not found' error.
PMLE Serving and scaling models Practice Question
This PMLE practice question tests your understanding of serving and scaling models. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A team deploys a TensorFlow model using a custom container to Vertex AI Endpoint. The container expects the saved model at the /model directory, but predictions fail with a 'model not found' error. The team used the default Vertex AI serving container in the past. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The container reads from a fixed directory /model, but Vertex AI mounts the model at /tmp/model.
Option D is correct because Vertex AI mounts the model artifact at a path specified by the environment variable AIP_STORAGE_URI, typically under /tmp/model. The custom container must read from this location or copy the model. Option A is wrong because the model format is not the issue. Option B is wrong because Vertex AI does not require the model to be in a Cloud Storage bucket mounted at /gcs in this context. Option C is wrong because the container can be GPU-enabled; the error is about file not found.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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 container does not have a GPU accelerator configured.
Why it's wrong here
GPU is not related to model file location.
✗
The model artifact must be downloaded from Cloud Storage and placed in /gcs.
Why it's wrong here
Vertex AI mounts the model; custom containers should use AIP_STORAGE_URI.
✓
The container reads from a fixed directory /model, but Vertex AI mounts the model at /tmp/model.
Why this is correct
Custom containers must adapt to the Vertex AI model mount point.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
✗
The model was saved in a different format (e.g., SavedModel vs. HDF5).
Why it's wrong here
The error is about file not found, not format.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related PMLE NAT questions on configuration and troubleshooting.
Serving and scaling models — This question tests Serving and scaling models — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: The container reads from a fixed directory /model, but Vertex AI mounts the model at /tmp/model. — Option D is correct because Vertex AI mounts the model artifact at a path specified by the environment variable AIP_STORAGE_URI, typically under /tmp/model. The custom container must read from this location or copy the model. Option A is wrong because the model format is not the issue. Option B is wrong because Vertex AI does not require the model to be in a Cloud Storage bucket mounted at /gcs in this context. Option C is wrong because the container can be GPU-enabled; the error is about file not found.
What should I do if I get this PMLE question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related PMLE NAT questions on configuration and troubleshooting.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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