Microsoft AI-300 : Operationalizing Machine Learning and Generative AI Solutions
- Exam Code: AI-300
- Exam Name: Operationalizing Machine Learning and Generative AI Solutions
- Updated: Oct 31, 2026
- Q & A: 189 Questions and Answers
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Microsoft AI-300 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| Optimize generative AI systems and model performance | 15–20% | - Improve efficiency and cost-effectiveness
|
| Implement machine learning model lifecycle and operations | 25–30% | - Orchestrate model training and experimentation
|
| Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
-
A team operates a generative AI-powered customer support assistant built on Microsoft Foundry. The application serves users globally and supports both real-time chat interactions and batch summarization jobs.
The team must ensure that the application continues to meet defined service-level objectives (SLO) as usage increases.
The team requires visibility into runtime behavior to identify performance regressions that affect the user experience and system capacity.
You need to select the performance metrics that meet the requirements.
Which performance metric should you monitor for each requirement? To answer, move the appropriate performance metrics to the correct requirements. You may use each performance metric once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Detect when the system is slow at returning output: Latency
Identify when the system cannot sustain expected request volume: Throughput Measure the duration of handling a request: Response time Latency measures delay experienced while waiting for model output and is therefore the appropriate metric for identifying degradation in perceived responsiveness. Microsoft describes latency as the time required to obtain a response from the model and exposes metrics such as Time to Response/Time to First Token and Time Between Tokens for generative AI workloads. An increase indicates that users are waiting longer for output to begin or continue.
Throughput represents the amount of workload the system can process over a unit of time. Microsoft describes system-level throughput in terms such as requests per minute and tokens per minute. Consequently, throughput is the correct metric when determining whether a deployment can sustain the required request volume as demand increases.
Response time represents the overall duration required to handle an individual request. It is appropriate for measuring end-to-end request processing and validating request-duration SLOs. Microsoft specifically includes latency, throughput, and response times among the performance metrics candidates are expected to monitor for generative AI applications.
Concurrency measures simultaneous active requests and can influence capacity and latency, but none of these requirements directly asks for simultaneous-request count.
Study Guide Reference: Implement generative AI quality assurance and observability - continuous monitoring, latency, throughput, response times, production troubleshooting, and SLO monitoring.
A team manages an Azure Machine Learning workspace where they deploy models to online endpoints.
The team needs to introduce a new version of a model to production without disrupting existing users.
The team must validate the new version before full rollout.
You need to reduce risk during deployment.
What should you do?
- A. Replace the existing endpoint.
- B. Split traffic between deployments.
- C. Deploy the model to a batch endpoint.
- D. Route all traffic to the new deployment.
Correct Answer: B 🗳️
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You use Azure Machine Learning to train models across multiple experiments by using the same workspace.
You must record training runs in a centralized location to compare results from different jobs.
During training, performance values must be captured so they appear in the experiment run history.
You need to configure experiment tracking.
What should you configure for each requirement? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Azure Machine Learning ' s experiment tracking is built around two complementary concepts. First, experiments are named containers that group related runs. By calling mlflow.set_experiment with an experiment name at the start of your training code, all subsequent runs are grouped under that experiment name in the AML workspace, creating the centralized record required. Second, metrics are scalar values such as accuracy, loss, or AUC that represent model performance. Calling mlflow.log_metric with a metric name and value during training persists these values to the run ' s record in the experiment history. These values appear on the Azure ML Studio run detail page and can be compared across runs using the experiment comparison view. Without set_experiment, runs fall into a default experiment. Without log_metric, the run history has no performance data to display or compare.
Microsoft Learn Reference Topic: Track machine learning experiments with MLflow in Azure Machine Learning
You have an Azure Machine Learning workspace that includes an AmICompute cluster and a batch endpoint.
You clone a repository that contains an MLflow model to your local computer. You need to ensure that you can deploy the model to the batch endpoint.
Solution: Create a datastore in the workspace.
Does the solution meet the goal?
- A. Yes
- B. No
Correct Answer: B 🗳️
An organization uses Microsoft Foundry to develop generative AI projects that access shared Azure resources such as storage accounts and vector databases.
The organization s security policy requires eliminating secret key-based authentication and enforcing least- privilege access.
You must configure identity and access so that:
Services authenticate without stored credentials.
Permissions are scoped appropriately across projects and shared resources.
You need to configure the appropriate identity or access mechanism for each requirement.
What should you configure in Microsoft Foundry to meet each requirement? To answer, move the appropriate configuration mechanisms to the correct requirements. You may use each configuration mechanism once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Managed Identity is Azure ' s solution for eliminating stored secrets and API keys from application configurations. When a Microsoft Foundry resource or its dependent compute has a system-assigned or user- assigned managed identity, it can authenticate to Azure services such as Storage, Key Vault, and Cognitive Services using Azure Entra ID tokens that are automatically issued and rotated by Azure. There are no keys to store, rotate, or accidentally expose in code or configuration files, directly satisfying the requirement to authenticate without stored credentials. Role-Based Access Control (RBAC) is the Azure governance mechanism for scoping permissions. By assigning specific built-in roles to the managed identity or to user and group principals at the correct scope - subscription, resource group, resource, or project level - the organization can enforce least-privilege access across different projects and shared resources.
Microsoft Learn Reference Topic: Configure managed identities and RBAC for Microsoft Azure AI Foundry resources
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