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AI-102 Implement generative AI solutions Practice Question

Exhibit

Refer to the exhibit. {
  "deploymentName": "gpt-4",
  "model": {
    "format": "OpenAI",
    "name": "gpt-4",
    "version": "0613"
  },
  "scaleSettings": {
    "scaleType": "Standard"
  }
}

Refer to the exhibit. You are deploying a GPT-4 model using Azure OpenAI Service. The deployment uses the Standard scale type. Which statement is true about this deployment?

⚠ Common exam trap

Microsoft often tests the distinction between Standard (pay-as-you-go with rate limits) and Provisioned (reserved capacity) scale types, and candidates mistakenly associate 'Standard' with default model versions or disabled content filtering.

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

✓

The deployment uses pay-as-you-go pricing and global rate limits.

The Standard scale type in Azure OpenAI Service uses pay-as-you-go pricing, where you are billed based on the number of tokens processed. It also enforces global rate limits (e.g., tokens per minute) that apply across all deployments in the region, rather than providing dedicated capacity. Option D correctly identifies these characteristics.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
The model version is not specified and will default to the latest.
Why it's wrong here
Standard deployments require an explicit model version at creation; there is no automatic default to the latest version, and the version stays fixed until changed. It is tempting because some Azure services auto-update to the newest model, but Azure OpenAI pins the version you specify.
✗
Content filtering is disabled for this deployment.
Why it's wrong here
Standard scale type governs throughput and latency, not content filtering; filters stay enabled by default. It is tempting because scale types do affect deployment behaviour, and content filtering can be configured separately in Azure OpenAI, but that setting is independent of the Standard scale type.
✗
The deployment uses provisioned throughput with reserved capacity.
Why it's wrong here
Standard scale type bills per token with pay-as-you-go quota, not reserved capacity; provisioned throughput is the separate Provisioned scale type. It is tempting because reserved capacity guarantees consistent latency for high-volume production workloads, which is precisely when Provisioned, not Standard, is chosen.
✓
The deployment uses pay-as-you-go pricing and global rate limits.
Why this is correct
Standard deployments bill per token on a pay-as-you-go basis and draw on the shared, globally pooled quota for that model, so throughput varies with regional demand. Provisioned scale types instead reserve dedicated capacity with predictable latency and a fixed hourly charge.

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AI-102 Implement generative AI solutions Practice Question

Exhibit

Why each option matters

Option-by-option breakdown

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