AI-102 Implement generative AI solutions Practice Question
A developer wants to deploy a custom generative AI model using Azure Machine Learning. Which compute target should they choose for low-latency real-time inference?
⚠ Common exam trap
Many candidates confuse Azure Functions' serverless convenience with real-time capability, overlooking the cold-start penalty and lack of GPU support, while AKS is the only option that provides the necessary infrastructure for consistent low-latency inference.
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
✓
Azure Kubernetes Service (AKS)
Azure Kubernetes Service (AKS) is the correct compute target for low-latency real-time inference because it supports horizontal pod autoscaling, GPU acceleration, and can be configured with a low-latency ingress controller (e.g., NGINX or Azure Application Gateway) to route inference requests directly to model containers. AKS also integrates with Azure Machine Learning's real-time inference endpoint, which uses a gRPC or HTTP-based scoring protocol to achieve sub-100ms response times.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Local deployment
Why it's wrong here
Local deployment is not scalable for production.
- ✗
Azure Batch
Why it's wrong here
Azure Batch is for batch processing, not real-time.
- ✗
Azure Functions
Why it's wrong here
Azure Functions may have cold start latency.
- ✓
Azure Kubernetes Service (AKS)
Why this is correct
AKS is designed for real-time inference with low latency.
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