A team is deploying machine learning models to a production inference endpoint in Azure Machine Learning.
The team requires a safe way to validate a new model version without disrupting existing users.
You need to recommend a deployment strategy for controlled testing of a new model version.
What should you configure?

• A.traffic splitting between deployments
• B.the model asset version in the registry
• C.deployment to a separate staging endpoint
• D.an evaluation script in Azure Machine Learning

Comment: *

Name: *

Email: *

Verification: *

Hotspot Question
You have an Azure Machine Learning workspace named Workspace1.
You plan to train an image classification model by using Automated ML in Workspace1.
You need to complete the provided Azure Machine Learning Python SDK v2 code to bring labeled image data as input for model training.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:
Box 1: azure.ai.ml.constants
azure.ai.ml.constants is the official SDK v2 submodule where the AssetType enum resides.
Box 2: MLTABLE
AssetType.MLTABLE: Automated ML for Computer Vision tasks (such as image classification and object detection) specifically requires your input data and corresponding label annotations to be provided via an MLTable asset type. This structure points to a folder containing your dataset configurations and your .jsonl bounding box or classification files.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models

Comment: *

Name: *

Email: *

Verification: *

A team develops and manages a conversational assistant by using Microsoft Foundry.
The team must be able to validate that the assistant does not produce hateful responses before the application is exposed to any users.
You need to evaluate the model output for hateful responses as part of a repeatable validation process.
Which evaluator should you configure first?

• A.Protected material
• B.Groundedness
• C.Indirect attacks
• D.Content safety

Comment: *

Name: *

Email: *

Verification: *

A real-time endpoint experiences sporadic latency spikes. Investigation reveals instances scale down to zero during inactivity. You need to reduce latency without significantly increasing cost.
What should you configure?

• A.Increase timeout settings
• B.Set minimum instance count
• C.Switch to batch endpoint
• D.Reduce logging

Comment: *

Name: *

Email: *

Verification: *

You use Azure Machine Learning to train models across multiple experiments by using the same workspace.
You must record training runs in a centralized location to compare results from different jobs.
During training, performance values must be captured so they appear in the experiment run history.
You need to configure experiment tracking.
What should you configure for each requirement? To answer, select the appropriate options in the answer area
. NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:
Azure Machine Learning ' s experiment tracking is built around two complementary concepts. First, experiments are named containers that group related runs. By calling mlflow.set_experiment with an experiment name at the start of your training code, all subsequent runs are grouped under that experiment name in the AML workspace, creating the centralized record required. Second, metrics are scalar values such as accuracy, loss, or AUC that represent model performance. Calling mlflow.log_metric with a metric name and value during training persists these values to the run ' s record in the experiment history. These values appear on the Azure ML Studio run detail page and can be compared across runs using the experiment comparison view. Without set_experiment, runs fall into a default experiment. Without log_metric, the run history has no performance data to display or compare.
Microsoft Learn Reference Topic: Track machine learning experiments with MLflow in Azure Machine Learning

Comment: *

Name: *

Email: *

Verification: *