The answer is that the model was not uploaded because the artifact URI points to a directory, not a valid SavedModel. Cloud Build’s upload step specifically requires a SavedModel artifact—a directory containing a saved_model.pb file and a variables subdirectory. If the URI leads to a generic folder or an incorrectly structured directory, the upload may complete without error but fails to register a usable model resource, causing the subsequent deploy step to fail with a “model does not exist” error. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of Vertex AI’s model upload requirements and the distinction between a successful build step and a valid artifact. A common trap is assuming any directory will work, when in fact the SavedModel format is strictly enforced. Memory tip: think “pb + variables = valid SavedModel”; if either is missing, your model is missing.
PMLE Practice Question: Collaborating within and across teams to manage data and models
This PMLE practice question tests your understanding of collaborating within and across teams to manage data and 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.
Network Topology
Refer to the exhibit. A team uses this Cloud Build configuration to deploy a model to a Vertex AI endpoint. The build succeeds up to the 'upload' step, but the 'deploy-model' step fails with an error that the model 'my-model' does not exist. 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 was not uploaded because the artifact URI is a directory, not a valid SavedModel
The 'deploy-model' step fails because the model was not successfully uploaded. Cloud Build's 'upload' step expects a valid SavedModel artifact (a directory containing a saved_model.pb file and variables subdirectory). If the artifact URI points to a directory that is not a valid SavedModel, the upload may appear to succeed but does not register a usable model resource, causing the subsequent deploy step to fail with 'model does not exist'.
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 deploy step uses the display name instead of the model resource ID
Why it's wrong here
While possible, Vertex AI can resolve display names to resource IDs in deploy-model commands.
✓
The model was not uploaded because the artifact URI is a directory, not a valid SavedModel
Why this is correct
The artifact URI must point to a specific model file or subdirectory, not a generic directory.
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 Vertex AI API was not enabled for the project
Why it's wrong here
If the API were disabled, the upload would have failed earlier.
✗
The region in the deploy step does not match the model's region
Why it's wrong here
Both steps use us-central1, so region is consistent.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between a successful upload step and a valid model registration, trapping candidates who assume any directory upload creates a usable model resource.
Trap categories for this question
Command / output trap
While possible, Vertex AI can resolve display names to resource IDs in deploy-model commands.
Detailed technical explanation
How to think about this question
Vertex AI model upload requires the artifact to be a TensorFlow SavedModel, PyTorch TorchScript, or scikit-learn joblib file, depending on the framework. The Cloud Build 'upload' step uses the `gcloud ai models upload` command under the hood, which validates the artifact format; if the URI points to a directory without a valid saved_model.pb, the upload may complete without error but the model resource is not created. In real-world scenarios, this often happens when the build copies the entire training output directory instead of the specific SavedModel subdirectory.
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.
Collaborating within and across teams to manage data and models — This question tests Collaborating within and across teams to manage data and models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The model was not uploaded because the artifact URI is a directory, not a valid SavedModel — The 'deploy-model' step fails because the model was not successfully uploaded. Cloud Build's 'upload' step expects a valid SavedModel artifact (a directory containing a saved_model.pb file and variables subdirectory). If the artifact URI points to a directory that is not a valid SavedModel, the upload may appear to succeed but does not register a usable model resource, causing the subsequent deploy step to fail with 'model does not exist'.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Refer to the exhibit. A team runs this command to upload a model to Vertex AI. They want to create this model as a new version under an existing model named 'my_model'. What is missing from the command?
Why E: Option E is correct because the `--parent-model` flag is required when uploading a new model version to an existing model in Vertex AI. Without specifying the parent model name, the command would attempt to create a brand-new model rather than adding a version to the existing 'my_model'. The `gcloud ai models upload` command uses this flag to associate the new version with the specified parent model.
Last reviewed: Jun 30, 2026
Question Discussion
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This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.
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