Operationalizing Machine Learning and Generative AI Solutions (beta) AI-300 Exam Questions

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Question #1 (Topic: demo questions)

You manage an Azure Machine learning workspace. You develop a machine learning model. You must deploy the model to use a low-priority VM with a pricing discount. You need to deploy the model. Which compute target should you use?

Azure Container Instances (ACI)

Azure Machine Learning compute clusters

Local deployment

Azure Kubernetes Service (AKS)

Correct Answer: B

Explanation:

B. Azure Machine Learning compute clusters is the correct answer because Azure Machine Learning Compute Clusters support low-priority (Spot) virtual machines, which provide significant cost savings compared to standard VMs. These clusters are designed for machine learning workloads and can automatically scale based on demand. In contrast, Azure Container Instances (ACI) and local deployments do not support low-priority VMs, while Azure Kubernetes Service (AKS) is primarily used for production inference deployments and is not the typical compute target for leveraging low-priority VMs in Azure Machine Learning. Therefore, if the goal is to deploy a machine learning model using discounted low-priority compute resources, Azure Machine Learning Compute Clusters are the best choice.

Question #2 (Topic: demo questions)

A team trains an MLflow model that scores customer churn risk. The model will be consumed by different downstream systems. One system requests predictions synchronously during customer interactions. Another system submits files containing millions of records for scheduled scoring. You need to deploy the model by using managed inference options that match each usage pattern. Which option should you use for each usage pattern? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

Correct Answer: A

Explanation not available for this question.

Question #3 (Topic: demo questions)

You need to recommend an experiment-tracking strategy that ensures consistent experiment results. What should you recommend?

Azure Machine Learning job output logs

MLflow experiment tracking

Application Insights logs

Azure Monitor alerts

Correct Answer: B

Explanation:

MLflow provides a structured and consistent way to track machine learning experiments, including parameters, metrics, artifacts, and model versions. By using MLflow experiment tracking within Azure Machine Learning, you ensure that every run is logged in a standardized format, making results reproducible and comparable across different experiments. It also helps maintain consistency by storing all experiment metadata centrally, so you can easily reproduce a model’s performance and verify results over time. Unlike general logging or monitoring tools, MLflow is specifically designed for experiment lifecycle management, which makes it the best choice for ensuring consistent experiment tracking in machine learning workflows.

Question #4 (Topic: demo questions)

You need to isolate training workloads while remaining cost-aware to address Fabrikam Inc.’s issues, constraints, and technical requirements. What should you implement?

Training jobs that run on a single shared compute cluster

Fixed-size compute cluster

Dedicated compute clusters per experiment

Managed compute targets with autoscaling

Correct Answer: D

Explanation:

Managed compute targets with autoscaling are the best option because they allow training workloads to scale up or down based on demand while optimizing cost. Instead of keeping resources running at full capacity all the time, autoscaling ensures that compute resources are only provisioned when needed and automatically reduced during idle periods. This helps isolate training workloads effectively—since each job can still run in its own managed environment—while also controlling operational costs. Compared to fixed-size or dedicated clusters, this approach provides better flexibility and cost efficiency, making it ideal for environments like Fabrikam Inc. where both isolation and budget awareness are required.

Question #5 (Topic: demo questions)

You need to standardize how Fabrikam Inc. manages machine learning assets. Which action should you perform first?

Register assets in the Azure Machine Learning registry

Create a shared Azure Machine Learning workspace.

Deploy a managed online endpoint.

Create a new Microsoft Foundry project.

Correct Answer: B

Explanation:

A shared Azure Machine Learning workspace is the foundational step for standardizing how machine learning assets are managed across an organization. The workspace acts as a central environment where datasets, models, environments, pipelines, and compute resources are created, organized, and governed. By establishing a shared workspace first, Fabrikam Inc. ensures that all teams are working within a consistent structure, which enables proper collaboration, access control, and lifecycle management of ML assets.
Once the workspace is in place, other capabilities—such as registering assets in an Azure Machine Learning registry or deploying managed online endpoints—can be built on top of it. Without a standardized workspace, asset management would remain fragmented and inconsistent, making governance and reuse difficult.

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