6.1 Azure OpenAI Service — Models and Deployment

Quick Answer: Azure OpenAI (now surfaced through Microsoft Foundry Models) gives you GPT-4o/GPT-4.1 (multimodal text+vision), o-series reasoning models, GPT-image-1 and DALL-E 3 (images), Whisper (speech-to-text), and text-embedding-3 (vectors). Nothing works until you create a deployment that binds one model version to a TPM quota. Pick a deployment type — Standard, Global Standard, Data Zone, Provisioned-Managed (PTU), or Batch — based on data residency and traffic predictability.

The AI-102 Context

Generative AI (Domain 2) is 15-20% of AI-102 (Designing and Implementing a Microsoft Azure AI Solution), among the most heavily revised areas in 2026. The exam is 40-60 questions in 100 minutes, passing at 700 / 1000, costing about USD $165. Note the live AI-102 retires June 30, 2026 and is replaced by a refreshed version — every model name below is current as of June 2026.

Model Catalog You Must Recognize

On the Exam: o-series reasoning models ignore temperature/top_p and use max_completion_tokens (reasoning tokens are billed but hidden). A question that sets temperature=0 on o1 is a distractor — the parameter is silently ignored.

Creating the Resource

Azure OpenAI must be its own Cognitive Services account. It is not bundled into the multi-service Azure AI Services resource that covers Vision, Language, and Speech.

az cognitiveservices account create \
  --name my-openai-service \
  --resource-group rg-ai-prod \
  --kind OpenAI \
  --sku S0 \
  --location eastus2 \
  --yes

The key flag is --kind OpenAI. A frequent trap: a scenario provisions a multi-service resource then asks why a GPT-4o call returns 404 — the fix is a dedicated OpenAI resource.

Deploying a Model

A deployment is a named alias mapping one model + version to a capacity quota. You call the deployment name, never the raw model name.

az cognitiveservices account deployment create \
  --name my-openai-service \
  --resource-group rg-ai-prod \
  --deployment-name gpt4o-chat \
  --model-name gpt-4o \
  --model-version "2024-11-20" \
  --model-format OpenAI \
  --sku-name GlobalStandard \
  --sku-capacity 50   # 50K tokens-per-minute quota

Deployment Types (2026)

Provisioned Throughput Units (PTU): you reserve a fixed block of capacity for guaranteed throughput and predictable latency. PTUs lower per-token cost at high volume but require a commitment. Choose PTU when traffic is predictable and high-volume; choose Standard/Global for spiky or experimental loads.

Azure OpenAI vs. OpenAI Direct

On the Exam: "Why choose Azure OpenAI over OpenAI?" → enterprise security and compliance: managed identity, private endpoints, content filtering, and regional/data-residency control. Prefer managed identity over API keys for any production scenario the exam describes.

Tokens, Quota, and Cost

Tokens are the billing and limiting unit. In English, 1 token ≈ 0.75 words ≈ 4 characters, so 1,000 words ≈ 1,333 tokens. Total request tokens = input (prompt) + output (completion), each priced separately per 1,000 tokens.

Worked example: A summarizer sends a 6,000-token document plus a 200-token instruction and expects a 600-token summary. Per call ≈ 6,800 tokens. A 30K-TPM Standard deployment supports ≈ 30,000 / 6,800 ≈ 4 calls/minute before throttling (HTTP 429). To scale, raise --sku-capacity (quota) or move to Global Standard / PTU. Common trap: developers blame the model for 429s when the real fix is increasing TPM quota or using Retry-After backoff.