The answer is that the model is too large for the machine's memory, causing an out-of-memory (OOM) error during loading. Even though the model artifact is only 2 GB and the n1-standard-4 machine provides 15 GB of RAM, TensorFlow models require significantly more memory for graph construction, intermediate tensor operations, and framework overhead, which can easily exceed the available capacity when the model is loaded entirely into memory before serving. On the Google Professional Machine Learning Engineer exam, this question tests your understanding of Vertex AI deployment constraints and the common trap of assuming artifact size equals memory footprint—a frequent cause of memory error when deploying TensorFlow model to Vertex AI. Remember that TensorFlow’s eager execution and graph optimization can double or triple the runtime memory requirement. A useful memory tip: think of the model artifact as a compressed suitcase; unpacking it for serving always takes more closet space than the suitcase itself.
PMLE Scaling prototypes into ML models Practice Question
This PMLE practice question tests your understanding of scaling prototypes into ml 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 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?
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 model is too large for the machine's memory, causing an out-of-memory (OOM) error during loading.
Option C is correct because 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.
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 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.
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 file is corrupted or missing dependencies, causing a crash.
Why it's wrong here
Corruption typically causes import errors, not MemoryError.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
When TensorFlow loads a model, it deserializes the SavedModel protobuf, reconstructs the graph, and allocates memory for variables and operations. The n1-standard-4 has 15 GB RAM, but the OS, TensorFlow runtime, and other processes consume a portion, leaving less than 15 GB for the model. Additionally, TensorFlow may allocate memory for graph optimization and execution, which can exceed the model file size by a factor of 2–3x, leading to an OOM error even if the model file is smaller than the total RAM.
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.
TExam Day Tips
→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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Scaling prototypes into ML models — This question tests Scaling prototypes into ML models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The model is too large for the machine's memory, causing an out-of-memory (OOM) error during loading. — Option C is correct because 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.
What should I do if I get this PMLE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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.
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Question Discussion
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