Quick Answer
The most likely cause of the Vertex AI Model Registry artifact URI error is that the `--artifact-uri` flag points to a directory instead of a specific model file. This occurs because Vertex AI Model Registry requires a direct path to the exact model binary—such as `gs://bucket/model/saved_model.pb`—rather than a container directory like `gs://bucket/model/`, as the service cannot resolve which file to register for deployment from a folder. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of how Vertex AI handles artifact registration, often appearing as a trick where candidates assume a directory path is acceptable. A common trap is confusing the storage bucket structure with the registry’s need for a precise file reference. To avoid this, remember the memory tip: “Point to the file, not the folder—the registry needs a single artifact holder.”
This PMLE practice question tests your understanding of solving business challenges with ml. 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.
Refer to the exhibit. A user attempts to upload a model to Vertex AI Model Registry using the gcloud CLI. The command fails with the error shown. 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.
The region us-central1 does not support Vertex AI
Why wrong: us-central1 is a supported region.
The --container-command flag is misspelled
Why wrong: Spelling is correct; comma separating arguments is valid.
The --artifact-uri points to a directory instead of a model file
Error indicates URI must point to a single file.
The --container-ports flag expects a comma-separated list
Why wrong: Single port is valid.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
The --artifact-uri points to a directory instead of a model file
The error indicates that the `--artifact-uri` flag points to a directory (e.g., `gs://bucket/model/`) rather than a specific model file (e.g., `gs://bucket/model/saved_model.pb`). Vertex AI Model Registry requires a direct path to the model artifact file, not a container directory, because the service needs to locate and register the exact model binary for deployment.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
The region us-central1 does not support Vertex AI
Why it's wrong here
us-central1 is a supported region.
The --container-command flag is misspelled
Why it's wrong here
Spelling is correct; comma separating arguments is valid.
The --artifact-uri points to a directory instead of a model file
Why this is correct
Error indicates URI must point to a single file.
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 --container-ports flag expects a comma-separated list
Why it's wrong here
Single port is valid.
Common exam traps
Google Cloud often tests the distinction between a directory path and a file path in cloud CLI commands, exploiting the common mistake of assuming a folder URI is acceptable when the service expects a specific artifact file.
Detailed technical explanation
When uploading a model to Vertex AI Model Registry, the `--artifact-uri` must point to a specific model file (e.g., a SavedModel directory for TensorFlow, a `.pt` file for PyTorch, or a `model.pkl` for scikit-learn) because the registry stores the artifact location as a URI for serving. If a directory is provided, the service cannot resolve which file is the model, leading to a validation error. In practice, users often mistakenly pass the parent bucket folder instead of the exact model file path.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Solving business challenges with ML — This question tests Solving business challenges with ML — Read the scenario before looking for a memorised answer..
The correct answer is: The --artifact-uri points to a directory instead of a model file — The error indicates that the `--artifact-uri` flag points to a directory (e.g., `gs://bucket/model/`) rather than a specific model file (e.g., `gs://bucket/model/saved_model.pb`). Vertex AI Model Registry requires a direct path to the model artifact file, not a container directory, because the service needs to locate and register the exact model binary for deployment.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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