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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions (Q46-Q51):
NEW QUESTION # 46
Hotspot Question
You are monitoring a fine-tuned large language model deployed in Microsoft Foundry.
You evaluate the model before and after fine-tuning by using the same evaluation dataset.
You review the following evaluation results:
You need to determine whether the fine-tuned model shows improved performance without introducing regression. For each of the following statements, select Yes if the statement is true.
Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 47
You create a binary classification model. You use the Fairlearn package to assess model fairness.
You must eliminate the need to retrain the model.
You need to implement the Fairlearn package.
Which algorithm should you use?
- A. fairlearn.preprocessing.CorrelationRemover
- B. fairlearn.postprocessing.ThresholdOptimizer
- C. fairiearn.reductions.ExponentiatedGradient
- D. fairlearn.reductions.GridSearch
Answer: B
Explanation:
The best algorithm to implement within the Fairlearn package for a binary classification model without retraining is the ThresholdOptimizer.
Why ThresholdOptimizer?
This algorithm is a post-processing technique specifically designed to mitigate unfairness after a model has already been trained. By setting the prefit parameter to True, it treats your existing model as a "black box," adjusting its decision thresholds for different groups to satisfy fairness constraints (like demographic parity or equalized odds) without changing the underlying model weights.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml
NEW QUESTION # 48
Drag and Drop Question
An organization uses Microsoft Foundry to develop generative AI projects that access shared Azure resources such as storage accounts and vector databases.
The organization s security policy requires eliminating secret key-based authentication and enforcing least-privilege access.
You must configure identity and access so that:
Services authenticate without stored credentials.
Permissions are scoped appropriately across projects and shared resources.
You need to configure the appropriate identity or access mechanism for each requirement.
What should you configure in Microsoft Foundry to meet each requirement? To answer, move the appropriate configuration mechanisms to the correct requirements. You may use each configuration mechanism 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 # 49
A team is experimenting with traditional models for a classification workflow in Azure Machine Learning.
The team requires a consistent way to manage assets that are created during experimentation.
You need to ensure that artifacts can be reused and governed across projects.
Which asset should you register?
- A. Pipeline
- B. Model
- C. Environment
- D. Component
Answer: B
Explanation:
In an Azure Machine Learning classification workflow, you should register Models.
Registration creates a versioned asset in your workspace or a centralized registry, which is essential for ensuring that artifacts are reusable, governed, and trackable across different projects and environments.
Key Assets for Reuse and Governance
To maintain a consistent and governed workflow, you should focus on registering these specific assets:
Models: The primary artifact. Registering a model allows you to track its lineage (which experiment created it), version it, and deploy it consistently across environments.
Components: These are self-contained pieces of code that perform specific steps in a pipeline (e.g., data cleaning, training). Registering them allows different teams to reuse the same
"traditional" classification logic without rewriting code.
Environments: Encapsulates the software dependencies (Python packages, Docker images) required for your model to run. Registering these ensures reproducibility across different compute targets.
Data Assets: Registering your training and testing datasets as versioned assets ensures that you can always audit exactly what data was used to train a specific model version.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-azure-machine-learning-v2
NEW QUESTION # 50
You deploy a new model version to a managed online endpoint. You must test it with 10% traffic and automatically roll back if latency or error rate increases beyond threshold. What should you configure?
- A. Manual testing workflow
- B. Traffic splitting with monitoring alerts
- C. Batch endpoint validation
- D. Separate endpoint for testing
Answer: B
Explanation:
Traffic splitting enables controlled rollout of a new model version by directing a percentage of requests to it. Combined with monitoring alerts, it supports automated rollback when performance degrades. Separate endpoints lack built-in traffic management and do not provide seamless or automated rollback capabilities.
NEW QUESTION # 51
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