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?
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
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
Question 3
You need to recommend an experiment-tracking strategy that ensures consistent experiment results. What should you recommend?
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.
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