Quick Answer
The answer is Azure Kubernetes Service (AKS) for low-latency real-time inference with custom generative AI models on Azure Machine Learning. AKS is the correct compute target because it provides horizontal pod autoscaling to handle traffic spikes, supports GPU acceleration for model compute, and can be paired with a low-latency ingress controller like NGINX or Azure Application Gateway to route requests directly to containerized models, enabling sub-100ms response times via gRPC or HTTP scoring protocols. On the Microsoft Azure AI Engineer Associate AI-102 exam, this question tests your understanding of compute targets for real-time endpoints versus batch endpoints; a common trap is choosing Azure Container Instances for its simplicity, but AKS is required for production-grade, scalable low-latency inference. Remember the mnemonic “AKS for Real-Time, ACI for Quick-Time” to distinguish that AKS handles persistent, low-latency workloads while ACI is better for dev/test scenarios.
This AI-102 practice question tests your understanding of implement generative ai solutions. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
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?
Local deployment
Why wrong: Local deployment is not scalable for production.
Azure Batch
Why wrong: Azure Batch is for batch processing, not real-time.
Azure Functions
Why wrong: Azure Functions may have cold start latency.
Azure Kubernetes Service (AKS)
AKS is designed for real-time inference with low latency.
Answer choices
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.
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.
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often 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.
Detailed technical explanation
Under the hood, AKS deploys the model as a containerized service behind a Kubernetes Service of type LoadBalancer, which distributes requests across multiple pod replicas. For ultra-low latency, you can enable NVIDIA GPU device plugins and use TensorRT or ONNX Runtime with CUDA to accelerate inference. A real-world scenario is a chatbot or fraud detection system requiring p99 latency under 50ms, where AKS can be paired with Azure Front Door for global load balancing and TLS termination.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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: 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.
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 11, 2026
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