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Microsoft AI-300 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Operationalizing Machine Learning and Generative AI Solutions |
| Exam Number: | AI-300 |
| Exam Format: | Multiple response, Multiple choice, Build list, Drag and drop, Case study |
| Real Exam Qty: | 40-60 |
| Passing Score: | 700/1000 |
| Exam Duration: | 100-120 |
| Exam Price: | $165 USD |
| Related Certifications: | Machine Learning Operations (MLOps) Engineer Associate |
| Available Languages: | Korean, Spanish, Italian, English, Japanese, Chinese (Traditional), Arabic (Saudi Arabia), German, Russian, French, Indonesian (Indonesia), Portuguese (Brazil), Chinese (Simplified) |
| Certificate Validity Period: | 1 year (renewable) |
| Sample Questions: | Microsoft AI-300 Sample Questions |
| Exam Way: | Online (proctored via Pearson VUE) or at a Pearson VUE testing center |
| Pre Condition: | Candidates should have subject matter expertise in setting up infrastructure for MLOps and GenAIOps solutions on Azure, with experience in training, deploying, and maintaining ML models using Azure Machine Learning and generative AI applications using Microsoft Foundry. No formal prerequisite exam is required. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-300 |
Microsoft AI-300 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Design and implement a GenAIOps infrastructure | - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Manage API keys, rate limits, and responsible AI guardrails - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Configure prompt orchestration, prompt flows, and agent frameworks |
| Optimize generative AI systems and model performance | - Implement cost management and scaling strategies for GenAI workloads - Fine-tune and distill models for specific use cases - Optimize inference performance, caching, and throughput - Tune prompts, system messages, and grounding strategies |
| Design and implement an MLOps infrastructure | - Manage environments, data stores, and model registries - Set up Azure Machine Learning workspace and compute targets - Configure source control, CI/CD pipelines, and automation for ML workflows - Implement security, governance, and compliance for MLOps |
| Implement machine learning model lifecycle and operations | - Retrain, update, and manage model versions in production - Deploy models to real-time and batch endpoints - Train, register, and version models using Azure Machine Learning - Monitor model performance, data drift, and operational health |
| Implement generative AI quality assurance and observability | - Conduct red teaming, adversarial testing, and content filtering - Implement logging, tracing, and telemetry for GenAI applications - Evaluate generative AI outputs for quality, safety, and grounding - Monitor latency, token usage, cost, and error rates |
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
You create an Azure Machine Learning workspace.
You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log a table in the following format:
You need to complete the Python code to log the table.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
A team maintains Infrastructure as Code (IaC) templates to provision Azure Machine Learning resources.
Provisioning must be triggered by changes in the templates and executed without manual intervention.
You need to automate resource provisioning.
Which action should you take for each requirement? To answer, move the appropriate actions to the correct requirements. You may use each action 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:
Azure Machine Learning infrastructure can be described as Infrastructure as Code using ARM templates, Bicep, or Terraform. To make provisioning fully automated, two separate concerns must be addressed. First, the trigger: a CI/CD system like GitHub Actions or Azure DevOps Pipelines must detect changes to IaC template files in the repository, configured through a branch-push or pull-request trigger. Second, the execution: the CI/CD pipeline runs the provisioning commands such as az ml workspace create or bicep deploy non-interactively using a service principal or managed identity, eliminating the need for a human to run scripts manually. Together, these two mechanisms deliver the GitOps-style automation that modern MLOps practices require, ensuring every template change automatically results in a consistent, auditable infrastructure update.
Microsoft Learn Reference Topic: Automate Azure Machine Learning with GitHub Actions - CI/CD for ML infrastructure
A data science team trains a model that depends on features that are stored in a managed feature store.
The model is registered in Azure Machine Learning and will be deployed to a real-time endpoint.
After deployment, the model must:
* Retrieve feature values dynamically at inference time.
* Use the same feature definitions that were used during training.
* Run without manual configuration changes across environments.
You need to define feature store entities so that feature retrieval behaves as expected when the model is deployed.
Which feature store entity should you select for each requirement? To answer, move the appropriate feature store entities to the correct requirements. You may use each feature store entity 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:
Define how features are retrieved at inference: Feature retrieval specification Ensure feature consistency between training and inference: Feature retrieval specification Enable automated feature lookup in production: Feature retrieval specification The feature retrieval specification is the key model-level contract for all three requirements. Microsoft defines it as a portable specification containing the exact list of features associated with a model, including the relevant feature store, feature set, and feature-set version . The same specification participates in both training and inference, making it the connective artifact across the model lifecycle.
During training, the feature retrieval specification is used to obtain the required feature values and generate training data. Microsoft requires the specification to be packaged with the model artifact when the model depends on feature-store features. At online inference time, the scoring script loads this packaged specification, resolves the feature list, and initializes online feature lookup before calling the feature store to retrieve current values.
This also prevents training-serving inconsistencies because the deployed model carries the feature dependencies and specific feature-set versions established during training, rather than relying on manually reconstructed production configuration. Microsoft explicitly states that packaging the retrieval specification with the model minimizes changes between training and inference workflows.
A feature set specification defines feature sources and transformations, a feature set asset provides managed versioning, and materialization precomputes feature values. None of those alone defines the model ' s complete retrieval contract at inference.
Study Guide Reference: Implement machine learning model lifecycle and operations - package a feature retrieval specification with the model artifact and operationalize feature-store-backed models.
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals.
You work in Microsoft Foundry with a prompt flow.
You must manually evaluate prompts and compare results across prompt variants.
You need to capture the inputs, outputs, token usage, and latencies for each flow run for the evaluation.
Solution: Configure Azure Monitor to collect logs from the workspace. Use the logs to perform prompt evaluation.
Does the solution meet the goal?
- A. No
- B. Yes
Correct Answer: A 🗳️
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You are authoring a notebook in Azure Machine Learning studio.
You must install packages from the notebook into the currently running kernel. The installation must be limited to the currently running kernel only.
You need to install the packages.
Which magic function should you use?
- A. !conda
- B. !pip
- C. %pip
- D. %load
Correct Answer: C 🗳️
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