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| Section | Weight | Objectives |
|---|---|---|
| Implement machine learning model lifecycle and operations | 25–30% | - Deploy models to production
|
| Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| Optimize generative AI systems and model performance | 15–20% | - Improve efficiency and cost-effectiveness
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
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NEW QUESTION # 65
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.
Answer:
Explanation:
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
NEW QUESTION # 66
Hotspot Question
You manage a Microsoft Foundry project.
You plan to create a search index by using the Microsoft Foundry SDK.
You need to configure the Content field as a prioritized field for semantic ranking. The field is already set to be searchable.
How should you complete the item code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: SemanticConfiguration
In the Azure AI Search / Microsoft Foundry SDK, the SemanticConfiguration class defines a specific semantic configuration (such as the name and the prioritized fields) used to optimize semantic ranking.
Box 2: SemanticField
The SemanticField class is specifically used inside the SemanticPrioritizedFields object to reference the exact name of the field (in this case, "content") that you want to prioritize for titles, keywords, or content.
Reference:
https://learn.microsoft.com/en-us/azure/search/search-get-started-semantic
NEW QUESTION # 67
Hotspot Question
You manage an Azure OpenAI deployment of the gpt-4o base model.
You plan to fine-tune the deployed model.
You need to prepare a file that contains training data.
Which keys should you include in each line of the training data file? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
For fine-tuning conversational models like gpt-4o on Azure OpenAI, the training file must be formatted in a JSON Lines (.jsonl) file, where each line represents a single training conversation.
Each line must strictly contain a messages key wrapping an array of message objects.
Box 1: role
role: Defines the sender of the message. The accepted strings for fine-tuning are "system",
"user", or "assistant".
Box 2: content
content: Contains the actual text string or multi-modal payload of the message.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/reinforcement-fine-tuning
NEW QUESTION # 68
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?
Answer: D
Explanation:
Parallel execution allows independent pipeline steps to run simultaneously, reducing total execution time without altering logic. This is more efficient than increasing compute resources, which may raise costs without guaranteeing proportional performance improvements.
NEW QUESTION # 69
Drag and Drop Question
A real-time endpoint is deployed in Azure Machine Learning to serve predictions to a web application.
Users report intermittent failures and unexpected responses when calling the endpoint.
You need to identify the appropriate troubleshooting action for each reported issue.
Which troubleshooting action should you perform for each issue? To answer, move the appropriate troubleshooting actions to the correct issues. You may use each troubleshooting action 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:
NEW QUESTION # 70
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