AI-102 Plan and manage an Azure AI solution Practice Question
You are designing an Azure AI solution that uses an Azure AI services multi-service account. The solution must call the service from an Azure Kubernetes Service (AKS) cluster without embedding account keys in application code. You need to configure authentication and authorization so that the workload identity used by the pods can be granted access. (Choose two.)
⚠ Common exam trap
The trap here is thinking that storing the key in Key Vault satisfies a no-keys-in-code requirement, when the application still has to retrieve and use that key.
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
✓
Assign the Cognitive Services User role to the managed identity.
Keyless access from AKS pods requires two things: the cluster must federate the pod identity with Microsoft Entra ID, and that identity must be granted a role on the Azure AI services resource. Workload identity federation provides the token acquisition mechanism, while the Cognitive Services User role provides the authorization. Together they let the application call the service without storing or handling account keys.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Store the account key in an Azure Key Vault secret and reference it from the pod.
Why it's wrong here
Even if the key is stored in Key Vault, the pod still needs to retrieve and use the key, which means the application code handles a secret value. This reintroduces the key-management burden the scenario is trying to eliminate, and it does not use the workload identity for authorization. It is a valid pattern in other contexts but fails the explicit requirement to avoid embedding keys in application code.
- ✓
Assign the Cognitive Services User role to the managed identity.
Why this is correct
Granting the Cognitive Services User role to the managed identity gives the workload the data-plane permissions needed to call the Azure AI services endpoints. This is the recommended way to authorize API calls without keys, because role assignments are evaluated by Azure RBAC and can be scoped to the specific Azure AI services resource. It directly supports the requirement to avoid embedding account keys in application code.
- ✗
Create a service principal with a client secret and mount the secret into the pod.
Why it's wrong here
A service principal with a client secret still requires the application to handle a secret value, which is the pattern the scenario forbids. Workload identity federation removes the need for a client secret by using a federated credential, so this option is a step backward. It also does not specifically authorize the workload identity against the Azure AI services resource.
- ✓
Configure the AKS cluster to use workload identity federation with Microsoft Entra ID.
Why this is correct
Workload identity federation lets the AKS pods obtain Microsoft Entra ID tokens through a federated credential, so the workload can authenticate without a client secret. This is the foundation for granting the managed identity access to the Azure AI services resource. Without this federation, the pods cannot present an identity that Azure RBAC can evaluate, so it is essential to the keyless authentication design.
- ✗
Enable local authentication on the Azure AI services account.
Why it's wrong here
Enabling local authentication allows the account to be accessed with shared keys, which is exactly the key-based model the scenario wants to avoid. It does not help the workload identity obtain a token, and it increases the attack surface by keeping key-based access available. This option works against the goal of keyless, identity-based access from the AKS pods.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
This AI-102 practice question is part of Courseiva's free Microsoft 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 AI-102 exam.