Microsoft Azure AI Engineer Associate AI-102 Practice Question
You have deployed a gpt-35-turbo model to Azure AI Foundry by using an Azure OpenAI Service resource. You must:
observe how many tokens each request consumes and the 95th-percentile end-to-end latency,
receive alerts if the 95th-percentile latency ever exceeds 1000 ms,
analyze how often the built-in content filter blocks or annotates prompts or completions. Which configuration should you use?
Enable Application Insights request telemetry and depend on its performance charts; no diagnostic setting is required because content filter events are included automatically.
Enable the "Audit" and "Trace" diagnostic log categories and create an alert rule on the Activity Log event "ActionLatency".
Create a metric alert on the "Tokens per minute" metric and export Activity Logs to a storage account for content filter analysis.
Create an alert rule on the "Total request latency" Azure Monitor metric (P95) and enable a diagnostic setting that sends the "RequestResponse" log category to a Log Analytics workspace.
Azure OpenAI emits real-time performance metrics through Azure Monitor. The "Total request latency" metric tracks end-to-end round-trip time and supports percentile aggregation, so an alert rule can be created on the P95 value exceeding 1000 ms. "Processed Prompt tokens", "Generated Completion tokens", and "Processed Inference tokens" metrics allow visibility into per-call token usage. Content-filter evaluations are included in the data captured by the "RequestResponse" category of diagnostic logs, which must be streamed to a Log Analytics workspace (or another sink) by enabling a diagnostic setting. Enabling only Activity logs or relying on Application Insights would not capture content filter events, and metrics alone cannot record the text moderation outcomes. Therefore, combining Azure Monitor metrics with a diagnostic setting that forwards the "RequestResponse" log category satisfies all three requirements.
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