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Updated: Oct 27, 2026
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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 Price: | $165 USD |
| Exam Duration: | 100-120 |
| Available Languages: | English, Italian, French, Arabic (Saudi Arabia), Chinese (Traditional), Korean, Japanese, Chinese (Simplified), Portuguese (Brazil), Indonesian (Indonesia), Russian, Spanish, German |
| Related Certifications: | Machine Learning Operations (MLOps) Engineer Associate |
| Real Exam Qty: | 40-60 |
| Exam Format: | Build list, Multiple choice, Case study, Drag and drop, Multiple response |
| Certificate Validity Period: | 1 year (renewable) |
| Passing Score: | 700/1000 |
| Sample Questions: | DOWNLOAD DEMO |
| 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 |
|---|---|
| Implement machine learning model lifecycle and operations | - Monitor model performance, data drift, and operational health - Train, register, and version models using Azure Machine Learning - Retrain, update, and manage model versions in production - Deploy models to real-time and batch endpoints |
| Design and implement an MLOps infrastructure | - Set up Azure Machine Learning workspace and compute targets - Implement security, governance, and compliance for MLOps - Manage environments, data stores, and model registries - Configure source control, CI/CD pipelines, and automation for ML workflows |
| Optimize generative AI systems and model performance | - Optimize inference performance, caching, and throughput - Implement cost management and scaling strategies for GenAI workloads - Tune prompts, system messages, and grounding strategies - Fine-tune and distill models for specific use cases |
| Implement generative AI quality assurance and observability | - Monitor latency, token usage, cost, and error rates - Implement logging, tracing, and telemetry for GenAI applications - Evaluate generative AI outputs for quality, safety, and grounding - Conduct red teaming, adversarial testing, and content filtering |
| Design and implement a GenAIOps infrastructure | - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Configure prompt orchestration, prompt flows, and agent frameworks - Manage API keys, rate limits, and responsible AI guardrails - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search |
Microsoft Operationalizing Machine Learning and Generative AI Solutions Exam: Questions Worth Asking
The Microsoft Operationalizing Machine Learning and Generative AI Solutions exam contains 40-60 to complete within 100-120. Timed simulation in the PrepAwayTest Desktop Test Engine teaches that rhythm before exam day does.
Three versions of the same study materials: a printable, expert-prepared PDF with free demo download and instant access; a Desktop Test Engine for Windows with customizable simulation and timed modes that works offline; and an Online Test Engine for any browser on Windows, Mac, Android, and iOS with test history and performance review. All 189 practice questions are identical across versions. Every purchase includes 365 days of free updates, a 50% renewal discount afterward, and unlimited computer installations.
The official Microsoft Operationalizing Machine Learning and Generative AI Solutions outline defines 5 domains. The leading three are Implement machine learning model lifecycle and operations, Optimize generative AI systems and model performance, and Design and implement a GenAIOps infrastructure. The full list is in the topics section above, and PrepAwayTest's 189 practice questions cover each one.
The Microsoft Operationalizing Machine Learning and Generative AI Solutions exam requires 700/1000 to pass, and registration costs $165 USD. A failed attempt means paying the same fee again — a readiness check with the PrepAwayTest engines before booking is the cheaper option.
The AI-300 exam is a Microsoft certification exam validating the Microsoft Operationalizing Machine Learning and Generative AI Solutions syllabus shown above. It belongs to these credential paths: Machine Learning Operations (MLOps) Engineer Associate. Candidates in 2026 keep up with it using PrepAwayTest's 189 practice questions, available as a printable PDF, a Windows Desktop Test Engine, and an Online Test Engine — three versions of the same materials.
PrepAwayTest issues a full refund if you take the Microsoft Operationalizing Machine Learning and Generative AI Solutions exam within 60 days of purchase and do not pass. The policy does not apply if the exam is taken within 3 days of purchase, if the exam was never actually taken, or to free materials or expired orders, and the candidate name must match the payer name. Submit a scan of your enrollment slip and the official Score Report PDF within 2 days of the exam; claims are processed within 7 days. Alternatively, exchange for two free exam products of equal value and keep your original update service. Orders arrive by email within 1 minute — contact support if nothing arrives within 2 hours.
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. Since requirements change over time, confirm the latest on the official Microsoft exam page before scheduling.
Microsoft recommends these training resources for the Microsoft Operationalizing Machine Learning and Generative AI Solutions exam:
Follow the training with steady self-testing — the 189 practice questions from PrepAwayTest help you apply theory and fill any gaps the training leaves.
Online (proctored via Pearson VUE) or at a Pearson VUE testing center Register for the Microsoft Operationalizing Machine Learning and Generative AI Solutions exam through these official channels:
After booking, PrepAwayTest delivers your practice questions within 1 minute — preparation starts the same day.
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
You are reviewing a dataset that will be used for an advanced fine-tuning job in Microsoft Foundry.
The fine-tuning job uses preference comparison data.
You review the following dataset excerpt.
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Preference comparison data with chosen versus rejected response pairs is the input format for Direct Preference Optimization (DPO) or RLHF-style fine-tuning - an advanced fine-tuning technique available in Microsoft Foundry. A valid DPO dataset record must have three fields: a prompt as the input, a chosen field containing the preferred response, and a rejected field containing the less preferred response. The file must be in JSONL format with UTF-8 encoding, where each line represents one complete preference pair. When evaluating statements about this dataset, mark True if the dataset contains all three required fields and chosen responses represent higher-quality outputs than rejected ones. Mark False if the format is incompatible with DPO requirements, if the required rejected field is missing, or if the chosen and rejected responses appear to be of equivalent quality with no clear preference signal.
Microsoft Learn Reference Topic: Advanced fine-tuning with preference data in Microsoft Foundry - DPO dataset format
A team deploys a classification model to production and monitors performance and data changes.
The team wants to ensure that significant drops in prediction accuracy automatically trigger the following:
Stakeholders must be notified of the drops.
Retraining must be initiated when thresholds are exceeded
You need to configure monitoring to meet the requirements.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Correct Answer:
Explanation:
Microsoft ' s guidance on production model monitoring prescribes a four-stage sequence. First, configure an Azure ML model monitor on the deployed endpoint, specifying which metrics to track and on what schedule.
Second, define the threshold value below which model performance is considered unacceptable - this becomes the trigger condition. Third, create an Azure Monitor alert rule that evaluates the monitored metric against the threshold and fires when it is breached; the alert rule is associated with an action group that sends notifications to stakeholders via email, SMS, or Teams webhook. Fourth, the action group includes a webhook action pointing to an Azure Machine Learning pipeline ' s published REST endpoint, which starts the retraining job automatically. This sequence cleanly separates detection, notification, and remediation, matching Microsoft ' s recommended MLOps automation pattern.
Microsoft Learn Reference Topic: Automate model retraining based on monitoring alerts - Azure Machine Learning MLOps
You manage an Azure Machine Learning workspace. You use Azure Machine Learning Python SDK v2 to configure a trigger to schedule a pipeline job. You need to create a time-based schedule with recurrence pattern.
Which two properties must you use to successfully configure the trigger? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
- A. schedule
- B. frequency
- C. time_zone
- D. interval
- E. start.time
Correct Answer: B,D 🗳️
You create a new Azure Machine Learning workspace with a compute cluster.
You need to create the compute cluster asynchronously by using the Azure Machine Learning Python SDK v2.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point
Correct Answer:
Explanation:
You have an Azure Machine Learning workspace named Workspace^ Workspace1 has a registered MLflow model named model1 with PyFunc flavor. You plan to deploy model1 to an online endpoint named endpoint1 without egress connectivity by using Azure Machine Learning Python SDK v2. You have the following code:
You need to add a parameter to the ManagedOnlineDeployment object to ensure the model deploys successfully.
Solution: Add the code_path parameter.
Does the solution meet the goal?
- A. Yes
- B. No
Correct Answer: B 🗳️
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