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Microsoft AI-300 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Implement machine learning model lifecycle and operations	25–30%	- Orchestrate model training and experimentation 1. Create and manage pipelines 2. Track experiments and metrics - Deploy models to production 1. Configure deployment options and scaling 2. Deploy to real-time and batch endpoints - Monitor and maintain models in production 1. Implement retraining and update workflows 2. Monitor data and model drift - Register, version, and package models 1. Manage model registry 2. Create reusable model packages
Topic 2: Design and implement a GenAIOps infrastructure	20–25%	- Implement infrastructure for generative AI workloads 1. Design scalable and secure architecture 2. Integrate with Azure services and tools - Set up Microsoft Foundry environment 1. Manage compute and deployment resources 2. Configure projects, connections, and security
Topic 3: Optimize generative AI systems and model performance	15–20%	- Improve efficiency and cost-effectiveness 1. Manage resource utilization 2. Optimize inference and deployment - Optimize model selection and configuration 1. Tune prompts and generation settings 2. Choose appropriate models and parameters
Topic 4: Design and implement an MLOps infrastructure	15–20%	- Create and manage Machine Learning workspace resources and assets 1. Manage compute targets, datastores, and environments 2. Configure workspace settings and security - Implement infrastructure as code for Machine Learning 1. Use Bicep or Azure CLI to deploy resources 2. Automate infrastructure provisioning
Topic 5: Implement generative AI quality assurance and observability	10–15%	- Evaluate and test generative AI applications 1. Test for safety, accuracy, and relevance 2. Define evaluation metrics and criteria - Monitor generative AI systems 1. Implement logging and alerting 2. Track usage, performance, and errors

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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions (Q124-Q129):

NEW QUESTION # 124
You are designing an Azure Machine Leaning solution by using the Python SDK v2.
You must train and deploy the solution by using a compute target. The compute target must meet the following requirements:
* Enable the use of on-premises compute resources.
* Support autoscalling.
You need to configure a compute target for training and inference.
Which compute target t should you configure?
To answer select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 125
A team manages an Azure Machine Learning workspace and deploys a model to an endpoint.
A deployed online endpoint shows inconsistent response times during periods of high traffic.
You need to identify potential performance degradation.
Which three metrics should you monitor? Each correct answer presents part of the solution.
Choose three.
NOTE: Each correct selection is worth one point.

A. Connections active
B. Dataset size
C. Requests per minute
D. Request latency
E. Feature count

Answer: A,C,D

Explanation:
To locate potential performance degradation in an Azure Machine Learning online endpoint during high traffic, you should monitor these three metrics:
Requests per minute: This metric tracks the volume of incoming traffic and helps identify if spikes in load correlate with slower response times.
Connections active: This monitors the total number of concurrent TCP connections from clients, which can indicate if the endpoint is reaching its capacity limits during peak periods.
Request latency: This directly measures the time taken to respond to requests, allowing you to observe exactly when and by how much performance is degrading.
Reference:
https://oneuptime.com/blog/post/2026-02-16-how-to-deploy-a-machine-learning-model-as-a-real- time-endpoint-in-azure-machine-learning/view

NEW QUESTION # 126
You manage an Azure Machine Learning workspace.
You must log multiple metrics by using MLflow.
You need to maximize logging performance.
What are two possible ways to achieve this goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. mlflowlog_metrics
B. MLflowClient.log_batch
C. mlflow.log. metric
D. mlflow.log_param

Answer: A,B

NEW QUESTION # 127
-
A team performs interactive experimentation during development. The team also runs scalable jobs for model training.
The team must minimize costs while ensuring compute resources scale when needed. Different workloads require different compute behaviors within the same workspace.
You need to configure compute targets that support each workload.
Which compute targets should you use? To answer, move the appropriate compute targets to the correct workload types. You may use each compute target 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.

Answer:

Explanation:

Explanation:
Interactive experimentation: Azure Machine Learning compute instance
Scalable training jobs: Azure Machine Learning compute cluster
For interactive experimentation , use an Azure Machine Learning compute instance . Microsoft defines a compute instance as a fully managed, cloud-based development workstation optimized for machine learning development. It integrates directly with Jupyter, JupyterLab, and other development tools in Azure Machine Learning studio, making it the appropriate target for notebook-driven experimentation, iterative coding, debugging, and development. A compute instance is a single-node environment and can be stopped or configured with idle shutdown to control costs.
For scalable training jobs , use an Azure Machine Learning compute cluster . Compute clusters are managed training targets that can scale from a minimum to a configured maximum number of nodes based on job demand. Microsoft specifically recommends them for larger datasets, distributed training, and workloads requiring elastic compute capacity. Setting the minimum node count to 0 allows the cluster to deallocate all worker nodes when no jobs are running, which is an important cost-optimization mechanism.
Azure Batch, Databricks, AKS, and standalone Azure VMs can support specialized workloads, but they do not match the native Azure Machine Learning development-versus-elastic-training pattern as directly as compute instance and compute cluster.
Study Guide Reference: Design and implement an MLOps infrastructure - Azure Machine Learning compute targets, compute instances, compute clusters, autoscaling, distributed training, and cost optimization.

NEW QUESTION # 128
You manage an Azure Machine Learning workspace. You train a model named model1.
You must identify the features to modify for a differing model prediction result.
You need to configure the Responsible Al (RAI) dashboard for model1.
Which three 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.

Answer:

Explanation:

Explanation:

NEW QUESTION # 129
......

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