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Microsoft AI-300 Exam Syllabus Topics:

Section	Weight	Objectives
Optimize generative AI systems and model performance	15–20%	- Improve efficiency and cost-effectiveness 1. Optimize inference and deployment 2. Manage resource utilization - Optimize model selection and configuration 1. Tune prompts and generation settings 2. Choose appropriate models and parameters
Implement generative AI quality assurance and observability	10–15%	- Monitor generative AI systems 1. Track usage, performance, and errors 2. Implement logging and alerting - Evaluate and test generative AI applications 1. Define evaluation metrics and criteria 2. Test for safety, accuracy, and relevance
Implement machine learning model lifecycle and operations	25–30%	- Deploy models to production 1. Configure deployment options and scaling 2. Deploy to real-time and batch endpoints - Orchestrate model training and experimentation 1. Track experiments and metrics 2. Create and manage pipelines - Register, version, and package models 1. Create reusable model packages 2. Manage model registry - Monitor and maintain models in production 1. Monitor data and model drift 2. Implement retraining and update workflows
Design and implement an MLOps infrastructure	15–20%	- Implement infrastructure as code for Machine Learning 1. Use Bicep or Azure CLI to deploy resources 2. Automate infrastructure provisioning - Create and manage Machine Learning workspace resources and assets 1. Manage compute targets, datastores, and environments 2. Configure workspace settings and security
Design and implement a GenAIOps infrastructure	20–25%	- Implement infrastructure for generative AI workloads 1. Integrate with Azure services and tools 2. Design scalable and secure architecture - Set up Microsoft Foundry environment 1. Manage compute and deployment resources 2. Configure projects, connections, and security

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Quiz 2026 AI-300: Operationalizing Machine Learning and Generative AI Solutions Authoritative Exam Dumps Provider

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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions (Q22-Q27):

NEW QUESTION # 22
You create an Azure Machine Learning workspace and install the MLflow library.
You need to tog different types of data by using the MLflow library.
Which method should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 23
A team develops and manages a conversational assistant by using Microsoft Foundry.
The team requires generative AI to automatically evaluate every pull request of an agentic application and fail the build if safety thresholds are exceeded.
You need to automate evaluations as part of CI.
What should you configure?

A. Retrieval chunking strategy
B. Content filter configured in warning mode
C. Blocklist applied to the model endpoint
D. GitHub Actions workflow that executes the runs

Answer: D

Explanation:
The correct solution is a GitHub Actions workflow that executes Microsoft Foundry evaluations as part of the CI process . Microsoft provides an AI agent evaluation GitHub Action specifically for incorporating Foundry agent evaluations into CI/CD workflows. The action can invoke the agent against an evaluation dataset, execute configured evaluators-including safety evaluators-and publish evaluation results before a change reaches production.
For pull-request gating, the workflow can be configured to run whenever relevant application files change.
Evaluation commands can also enforce explicit thresholds and return a non-zero exit code when those thresholds are not satisfied. Microsoft documents evaluation gating through options such as --fail-on pass- rate= < threshold > or --fail-on any-failure; a non-zero result causes the CI job to fail, preventing unsafe changes from being promoted.
A blocklist or content filter provides runtime content protection but does not automate pull-request evaluation.
A retrieval chunking strategy affects RAG retrieval quality, not CI safety gates.
Therefore, the required mechanism is GitHub Actions integrated with Foundry evaluation runs and safety thresholds .
Study Guide Reference: Implement generative AI quality assurance and observability - automated evaluations, CI/CD quality gates, safety evaluators, GitHub Actions, and pre-production validation.

NEW QUESTION # 24
You manage an Azure Machine Learning workspace.
You must set up an event-driven process to trigger a retraining pipeline.
You need to configure an Azure service that will trigger a retraining pipeline in response to data drift in Azure Machine Learning datasets. Which Azure service should you use?

A. Event Hubs
B. Azure Functions
C. Logic Apps
D. Event Grid

Answer: D

NEW QUESTION # 25
A data science team plans to evaluate multiple hyperparameter values automatically while training a model in Azure Machine Learning.
The tuning process must run multiple training trials without manually modifying the training script for each run.
You need to automate hyperparameter tuning for the training job.
What should you do?

A. Create a tuning job that runs multiple trials with different parameter values.
B. Adjust hyperparameters after model deployment.
C. Duplicate the training script for each parameter combination.
D. Manually change hyperparameter values between training runs.

Answer: A

NEW QUESTION # 26
Hotspot Question
You manage a Microsoft Foundry project.
You are evaluating two RAG solutions.
When generating answers, the solutions display the following results:
- The first solution displays low completeness and low utilization.
- The second solution displays low completeness and high utilization.
You need to address the issues found during evaluation.
Which action should you perform first for each issue? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 27
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