AI-102 Implement generative AI solutions Practice Question
Network Topology
Refer to the exhibit. An administrator runs this Azure CLI command to deploy a GPT-4 model in Azure AI Foundry. The command fails with an error that the deployment name already exists. What should the administrator do to resolve the issue?
⚠ Common exam trap
Watch out — candidates often think the error is about model availability or SKU constraints, but the error explicitly states 'deployment name already exists,' which is a naming conflict, not a capacity or version issue.
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
✓
Use a different deployment name or delete the existing deployment.
The error message indicates that a deployment with the same name already exists in the Azure AI Foundry workspace. In Azure AI Foundry, deployment names must be unique within a workspace. The correct resolution is to either choose a different deployment name or delete the existing deployment before re-running the command. This aligns with the Azure CLI behavior where resource names (including AI model deployments) must be unique per scope.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a different deployment name or delete the existing deployment.
Why this is correct
Deployment names must be unique within an Azure AI Foundry resource. Since the CLI command failed because that name is already taken, the administrator must either supply a new unique name or remove the existing deployment before retrying.
- ✗
Specify a different resource group.
Why it's wrong here
The deployment name collision is scoped to the Azure AI Foundry account or resource, not the resource group, so relocating the resource group leaves the conflicting name intact. Changing resource groups suits isolating billing or network boundaries, not resolving a duplicate deployment identifier.
- ✗
Remove the --sku-name parameter.
Why it's wrong here
The --sku-name parameter sets the pricing tier (for example Standard or Provisioned), which is unrelated to the deployment name uniqueness check. Removing it either triggers a missing-parameter error or defaults the tier; SKU selection matters when choosing capacity or throughput, not naming conflicts.
- ✗
Use a different model version.
Why it's wrong here
Model version selects which GPT-4 weights are served, not the deployment's identifier. The name-exists error arises from the deployment name itself, so altering the version still targets the same occupied name. Version choice is relevant when pinning a specific model release for compatibility.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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