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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
Passing Score:700/1000
Exam Price:$165 USD
Available Languages:Chinese (Simplified), Italian, French, German, Korean, Russian, English, Japanese, Arabic (Saudi Arabia), Indonesian (Indonesia), Portuguese (Brazil), Chinese (Traditional), Spanish
Related Certifications:Machine Learning Operations (MLOps) Engineer Associate
Exam Duration:100-120
Real Exam Qty:40-60
Certificate Validity Period:1 year (renewable)
Exam Format:Drag and drop, Case study, Build list, Multiple choice, Multiple response
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:

SectionObjectives
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
Implement generative AI quality assurance and observability- Monitor latency, token usage, cost, and error rates
- Conduct red teaming, adversarial testing, and content filtering
- Evaluate generative AI outputs for quality, safety, and grounding
- Implement logging, tracing, and telemetry for GenAI applications
Design and implement a GenAIOps infrastructure- Implement RAG (Retrieval-Augmented Generation) pipelines and vector search
- Set up Microsoft Foundry and Azure AI services for generative AI workloads
- Manage API keys, rate limits, and responsible AI guardrails
- Configure prompt orchestration, prompt flows, and agent frameworks
Implement machine learning model lifecycle and operations- Train, register, and version models using Azure Machine Learning
- Deploy models to real-time and batch endpoints
- Monitor model performance, data drift, and operational health
- Retrain, update, and manage model versions in production
Design and implement an MLOps infrastructure- Implement security, governance, and compliance for MLOps
- Set up Azure Machine Learning workspace and compute targets
- Manage environments, data stores, and model registries
- Configure source control, CI/CD pipelines, and automation for ML workflows

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

Question #1

You train and register an Azure Machine Learning model
You plan to deploy the model to an online endpoint
You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.
Solution:
Create a managed online endpoint with the default authentication settings. Deploy the model to the online endpoint.
Does the solution meet the goal?

  • A. Yes
  • B. No
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #2

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.

Reveal Solution  Discussion  0

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

Question #3

You create an Azure Machine Learning workspace.
You must create a custom role named DataScientist that meets the following requirements:
Role members must not be able to delete the workspace.
Role members must not be able to create, update, or delete compute resource in the workspace.
Role members must not be able to add new users to the workspace.
You need to create a JSON file for the DataScientist role in the Azure Machine Learning workspace.
The custom role must enforce the restrictions specified by the IT Operations team.
Which JSON code segment should you use?

  • A.
  • B.
  • C.
  • D.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Explanation: Only visible for Actual4Cert members. You can sign-up / login (it's free).

Question #4

A data science team trains a classification model that predicts loan approval outcomes.
Before registering the model, the team must ensure the following:
Predictions must not disproportionately impact protected groups.
Prediction errors can be evaluated across different data segments.
You need to assess whether the model meets Responsible AI expectations.
Which two approaches should you use? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Choose two.

  • A. Evaluate feature importance for prediction transparency.
  • B. Analyze error rates across the global cohort.
  • C. Measure endpoint latency under load.
  • D. Validate inference schema compatibility.
  • E. Analyze error rates across defined demographic cohorts.
Reveal Solution  Discussion  0

Correct Answer: A,E  🗳️

Explanation: Only visible for Actual4Cert members. You can sign-up / login (it's free).

Question #5

You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.
You must register datastores in workspace1 for Azure Blob and Azure Data Lake Gen2 storage to meet the following requirements:
* Data scientists accessing the datastore must have the same level of access.
* Access must be restricted to specified containers or folders.
You need to configure a security access method used to register the Azure Blob and Azure Data lake Gen?
storage in workspace1. Which security access method should you configure? To answer, select the appropriate options in the answers area.
NOTE: Each correct selection is worth one point.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

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