Chapter 4 of 5 · Microsoft AI-300
Implement Generative AI Quality Assurance and Observability
Evaluation datasets, quality and safety evaluators, automated evaluation, Foundry monitoring, tracing and token cost.
- 3topics
- 15 minfast track
- 1diagrams and exhibits
- 10–15%of the exam
Topics in this chapter
Take them in order, or jump to the one you need.
- 1Test Datasets, Data Mapping and Quality MetricsAI-300 QA: build evaluation datasets, map columns to evaluator inputs, and score groundedness, relevance, coherence and fluency…5 min
- 2Safety Evaluations and Automated Evaluation WorkflowsAI-300 QA: run risk and safety evaluators for harmful content, write custom evaluators and gate releases with thresholds in a…Diagram5 min
- 3Monitoring, Tracing, Performance and CostAI-300 observability: use Foundry monitoring, track latency, throughput and token cost, and set up OpenTelemetry tracing and…5 min
About this chapter
What this chapter covers
Implement Generative AI Quality Assurance and Observability is 10 to 15% of the Microsoft AI-300 exam. It has two skill groups: configuring evaluation and validation, and implementing observability for generative AI applications and agents.
You cannot improve what you cannot measure. This chapter covers test datasets and data mapping, built in and custom evaluators for quality and safety, evaluation in a CI/CD workflow, and production monitoring, tracing, latency and cost tracking.
Questions give a symptom (ungrounded answers, harmful output, a latency spike, a bill that doubled) and ask which evaluator, metric or telemetry setting reveals or fixes it.
How the topics fit together
Offline evaluation runs before release and gates the deployment. Telemetry from production then feeds the same questions back: is quality holding, how fast is it, and what does it cost?
Learn what each evaluator reads. Groundedness compares the response to the context; relevance compares the response to the query.
How to use these pages
Each topic page has the same shape, so you can skim for what you need.
- What it is: the idea in plain words.
- How it works: the mechanism, with tables and examples.
- What to remember for the exam: the facts to memorise and how questions are phrased.
- Common mistakes: the classic traps.
- Check yourself: three questions to test your understanding.
Ready for more practice?
The full Microsoft AI-300 question bank has 650 questions, with timed practice exams and an explanation for every answer.
This study guide is independent and is not affiliated with, endorsed by, or sponsored by Microsoft. Vendor names, certification names and exam codes are trademarks of their respective owners and are used for identification only.