NEW QUESTION 52
You are fine-tuning a base language model to analyze customer feedback.
You label examples of support tickets. You must improve classification accuracy by configuring and fine- tuning the base model in Microsoft Foundry.
You need to configure and run fine-tuning.
What should you do first?

NEW QUESTION 53
You need to isolate training workloads while remaining cost-aware to address Fabrikam Inc.’s issues, constraints, and technical requirements.
What should you implement?

NEW QUESTION 54
A team provisions an Azure Machine Learning environment by triggering pull requests.
Deployments must be automated, auditable, and require approval before running.
You need to select a deployment automation tool.
Which tool should you use?

NEW QUESTION 55
A team develops multiple AI applications in Microsoft Foundry that rely on shared prompt templates.
The team requires a centralized way to track, version, and reuse prompt content across projects.
You need to recommend a solution to track and reuse prompt content.
Which approach should you recommend?

NEW QUESTION 56
You need to run large-scale inference jobs on millions of records periodically. Jobs are not latency-sensitive but must be cost-efficient and scalable. Which deployment option is MOST appropriate?

NEW QUESTION 57
Hotspot Question
You use Azure Machine Learning to implement hyperparameter tuning for an Azure ML Python SDK v2-based model training.
Training runs must terminate when the primary metric is lowered by 25 percent or more compared to the best performing run.
You need to configure an early termination policy to terminate training jobs.
Which values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

NEW QUESTION 58
You plan to filter your traces to identify issues while observing how the application is responding. The solution must not use an external knowledge base.
You need to select an evaluation metric.
Which built-in evaluator should you use?

NEW QUESTION 59
Drag and Drop Question
A team iterates prompts used by a generative AI agent. The team must support internal review before releasing changes.
The team must:
– Track prompt changes with a clear history for audit and rollback.
– Compare prompt variants in parallel without affecting the prompt used in the production environment.
You need to select the appropriate source control approach for each requirement.
What should you use for each requirement? To answer, move the appropriate source controls to the correct requirements. You may use each source control 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.

NEW QUESTION 60
Hotspot Question
A team retrains a machine learning model on a weekly basis by using updated training data.
The team must be able to rerun any previous experiment by using the exact data that was available at the time it was originally run. The solution must preserve historical versions of the data without duplicating training scripts.
You need to manage the data so that experiments can be reproduced reliably.
Which workspace actions should you perform? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

NEW QUESTION 61
You are training machine learning models in Azure Machine Learning. You use Hyperdrive to tune the hyperparameters.
In previous model training and tuning runs, many models showed similar performance.
You need to select an early termination policy that meets the following requirements:
– accounts for the performance of all previous runs when evaluating the current run
– avoids comparing the current run with only the best performing run to date Which two early termination policies should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

NEW QUESTION 62
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.

NEW QUESTION 63
Drag and Drop Question
A company plans to deploy a foundation model in Microsoft Foundry.
The mode must support the following workloads:
– A customer support workload used across multiple regions
– A marketing workload that must remain within a specific region due to data residency requirements You need to select the deployment type.
Which deployment type should you use for each workload? To answer, move the appropriate deployment types to the correct requirements. You may use each deployment type 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.

NEW QUESTION 64
Your ML pipeline contains independent feature engineering steps that currently execute sequentially, increasing overall runtime. You want to optimize execution without modifying logic.
What is the BEST solution?

NEW QUESTION 65
A data science team completes multiple training runs within an experiment by using MLflow.
The team wants to store a selected model in Azure Machine Learning so that it can be versioned and deployed later.
The model must be versioned centrally for reuse across environments.
You need to version the trained model.
Which two actions should you perform? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.

NEW QUESTION 66
Multiple teams need access to approved models with version tracking, lineage, and governance controls. Models must be discoverable and reusable across projects. What Azure ML feature should you use?

NEW QUESTION 67
Drag and Drop Question
You use Azure Machine Learning to deploy a model as a real-time web service.
You need to create an entry script for the service that ensures that the model is loaded when the service starts and is used to score new data as it is received.
Which functions should you include in the script? To answer, drag the appropriate functions to the correct actions. Each function may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

NEW QUESTION 68
Hotspot Question
A team is provisioning a new Azure Machine Learning workspace for a production project.
The workspace must support secure secret storage and operational monitoring. The team requires the workspace to be created with the correct dependent resources to meet security and monitoring requirements.
You need to configure the required dependencies when the team creates the workspace.
Which resources should you associate with the workspace? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

NEW QUESTION 69
A pipeline step fails intermittently due to transient compute issues. You need to improve reliability without modifying core logic or increasing cost significantly. What is the BEST approach?