Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
Exhibit
gcloud ai models upload \ --region=us-central1 \ --display-name=my-model \ --artifact-uri=gs://my-bucket/model \ --container-image-uri=us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch:latest
Refer to the exhibit. A data scientist runs this command to upload a custom model to Vertex AI. What is the primary purpose of the --container-image-uri flag?
⚠ Common exam trap
Candidates often confuse the --container-image-uri flag with the training container (Option B) because both involve custom containers, but Vertex AI separates training and serving containers, and this flag is exclusively for serving.
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
✓
To specify the base image for model serving
The --container-image-uri flag in the `gcloud ai models upload` command specifies the custom container image that Vertex AI will use to serve predictions. This is the base image for model serving, not for training, because Vertex AI uses this image to create the serving environment that hosts the model and handles prediction requests.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
To indicate the model artifact location
Why it's wrong here
This is done by --artifact-uri.
- ✗
To set the training container
Why it's wrong here
Training container is specified differently.
- ✓
To specify the base image for model serving
Why this is correct
Defines the serving environment for predictions.
- ✗
To define the prediction container
Why it's wrong here
Essentially same as A but less precise.
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