Quick Answer
The correct answer is to use a different deployment name or delete the existing deployment. This is because Azure OpenAI enforces unique deployment names within a single Azure AI Foundry workspace; the error “deployment name already exists” occurs when the CLI command attempts to create a model deployment with a name already assigned to another deployment in that workspace. On the AI-102 exam, this scenario tests your understanding of Azure AI Foundry resource naming constraints and the deployment lifecycle—a common trap is trying to modify the existing deployment’s model or configuration instead of recognizing the name collision. Remember that deployment names act as unique identifiers per workspace, so you must either choose a fresh name or remove the conflicting deployment before re-running the command. A quick memory tip: “Name or nuke”—if the name is taken, either pick a new one or delete the old deployment to clear the path.
This AI-102 practice question tests your understanding of implement generative ai solutions. 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. 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?
Use a different deployment name or delete the existing deployment.
Deployment names must be unique within an Azure AI Foundry resource.
Specify a different resource group.
Why wrong: The resource group is not the issue; the deployment name is.
Remove the --sku-name parameter.
Why wrong: The --sku-name parameter is required.
Use a different model version.
Why wrong: The model version is not the cause of the name conflict.
Answer choices
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.
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.
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.
Related concept
Read the scenario before looking for a memorised answer.
Specify a different resource group.
Why it's wrong here
The resource group is not the issue; the deployment name is.
Remove the --sku-name parameter.
Why it's wrong here
The --sku-name parameter is required.
Use a different model version.
Why it's wrong here
The model version is not the cause of the name conflict.
Common exam traps
The trap here is that candidates may 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.
Detailed technical explanation
Azure AI Foundry deployments are created under a workspace, and the deployment name serves as a unique identifier for the endpoint. The Azure CLI command `az ml online-deployment create` (or equivalent for AI Foundry) enforces this uniqueness at the workspace level. In real-world scenarios, if you attempt to update an existing deployment, you must use the `--update` flag or delete the old deployment first; otherwise, the command fails with a 'conflict' error (HTTP 409).
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Implement generative AI solutions — This question tests Implement generative AI solutions — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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