The answer is the saved model directory containing the model file(s) and any custom dependencies. This is correct because Vertex AI Model Registry requires a self-contained artifact that includes both the model binary—such as a TensorFlow SavedModel or a scikit-learn model.pkl—and all runtime dependencies needed for consistent serving, ensuring the exported artifact can be deployed identically to endpoints or batch prediction jobs. On the Google Professional Machine Learning Engineer exam, this concept tests your understanding of the export workflow from Vertex AI Training to the Registry, often appearing as a trap where candidates mistakenly upload only the model file or a training checkpoint, forgetting that the Registry expects a deployable package. A common memory tip is to think of the artifact as a “deployment capsule”—it must contain everything the model needs to run, not just the trained weights.
PMLE Scaling prototypes into ML models Practice Question
This PMLE practice question tests your understanding of scaling prototypes into ml models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 machine learning engineer is exporting a trained model from Vertex AI Training to the Model Registry. Which artifact should they upload as the model artifact?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The saved model directory containing the model file(s) and any custom dependencies.
When exporting a trained model from Vertex AI Training to the Model Registry, the correct artifact is the saved model directory that contains the model file(s) (e.g., SavedModel format for TensorFlow, model.pkl for scikit-learn) along with any custom dependencies required for serving. This ensures the model can be deployed consistently to endpoints or batch predictions, as the Model Registry expects a self-contained artifact that includes both the model binary and its runtime dependencies.
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 saved model directory containing the model file(s) and any custom dependencies.
Why this is correct
This is the standard artifact expected by Vertex AI for deployment.
Related concept
Read the scenario before looking for a memorised answer.
✗
Only the model checkpoint file (.ckpt or .h5).
Why it's wrong here
Checkpoints are not complete models; missing graph and configuration.
✗
The entire training directory including training code and logs.
Why it's wrong here
Contains unnecessary files and may cause deployment issues.
✗
A zip file of the training source code.
Why it's wrong here
Source code is not a deployable model artifact.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between training artifacts (checkpoints, code) and deployable model artifacts, trapping candidates who confuse a checkpoint (used for resuming training) with a final, serving-ready model.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI Model Registry uses the saved model directory to create a model resource that can be linked to a serving container; for TensorFlow, this means the SavedModel format includes the model architecture, weights, and a signature_def_map for inference. A subtle behavior is that custom dependencies must be specified via a requirements.txt or a custom container, not embedded in the model directory itself, to avoid conflicts with the serving environment. In a real-world scenario, failing to include custom dependencies (e.g., a custom TensorFlow op) will cause the deployed model to fail at runtime with an ImportError.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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 saved model directory containing the model file(s) and any custom dependencies. — When exporting a trained model from Vertex AI Training to the Model Registry, the correct artifact is the saved model directory that contains the model file(s) (e.g., SavedModel format for TensorFlow, model.pkl for scikit-learn) along with any custom dependencies required for serving. This ensures the model can be deployed consistently to endpoints or batch predictions, as the Model Registry expects a self-contained artifact that includes both the model binary and its runtime dependencies.
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.
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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