- A
The container does not have a GPU accelerator configured.
Why wrong: GPU is not related to model file location.
- B
The model artifact must be downloaded from Cloud Storage and placed in /gcs.
Why wrong: Vertex AI mounts the model; custom containers should use AIP_STORAGE_URI.
- C
The container reads from a fixed directory /model, but Vertex AI mounts the model at /tmp/model.
Custom containers must adapt to the Vertex AI model mount point.
- D
The model was saved in a different format (e.g., SavedModel vs. HDF5).
Why wrong: The error is about file not found, not format.
Question 311 of 1,000
hardMultiple ChoiceObjective-mapped
Debugging Model Path Issues in Custom Containers on Vertex AI
This PMLE practice question tests your understanding of pmle exam topics. 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.
Quick Answer
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.
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 container reads from a fixed directory /model, but Vertex AI mounts the model at /tmp/model.
Option C is correct. When using a custom container, Vertex AI mounts the saved model artifact at the path specified by the environment variable AIP_STORAGE_URI, typically /tmp/model. The team's container expects the model at /model, but it is actually at /tmp/model, resulting in the 'model not found' error. Option A is wrong because the error is not related to GPU configuration. Option B is wrong because Vertex AI does not download the model to /gcs; rather, it mounts the model directory. Option D is wrong because the model format (SavedModel vs HDF5) is not mentioned as an issue; the problem is the directory location.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Read the scenario before looking for a memorised answer.
- ✗
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: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this PMLE question test?
Read the scenario before looking for a memorised answer.
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 C is correct. When using a custom container, Vertex AI mounts the saved model artifact at the path specified by the environment variable AIP_STORAGE_URI, typically /tmp/model. The team's container expects the model at /model, but it is actually at /tmp/model, resulting in the 'model not found' error. Option A is wrong because the error is not related to GPU configuration. Option B is wrong because Vertex AI does not download the model to /gcs; rather, it mounts the model directory. Option D is wrong because the model format (SavedModel vs HDF5) is not mentioned as an issue; the problem is the directory location.
What should I do if I get this PMLE question wrong?
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 24, 2026
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