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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% | - Evaluate and test generative AI applications
|
| Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
| 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
|
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NEW QUESTION # 42
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals.
You have an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.
You plan to add a new Jupyter kernel that will be accessible from the same terminal session.
You need to perform the task that must be completed before you can add the new kernel.
Solution: Delete the Python 3.6 - AzureML kernel.
Does the solution meet the goal?
Answer: B
Explanation:
No. Removing the existing Python 3.6 - AzureML kernel is not a prerequisite for adding a new kernel. Azure Machine Learning supports multiple installed Jupyter kernels on the same compute instance, and notebooks can select among those kernels without requiring one existing kernel to be removed first. Microsoft states that the notebook environment automatically discovers all Jupyter kernels installed on the connected compute instance.
The supported new-kernel workflow begins by creating a separate environment, for example with conda create --name newenv, and activating it. The required pip and ipykernel packages are then installed in the environment before registering it with Jupyter by using python -m ipykernel install --user --name newenv.
Deleting an existing AzureML kernel provides no technical benefit to that workflow. Microsoft also explicitly cautions users not to delete Conda environments or Jupyter kernels they did not create , because doing so can interfere with built-in Jupyter/JupyterLab functionality on the compute instance.
Therefore, the existing Python 3.6 kernel should remain untouched; the new kernel should be installed alongside it in its own environment.
Study Guide Reference: Design and implement an MLOps infrastructure - Jupyter kernel management, Conda environments, Azure Machine Learning compute instances, and development environment isolation.
NEW QUESTION # 43
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?
Answer: D
Explanation:
The question ' s constraint is explicit: no retraining. This immediately eliminates ExponentiatedGradient (option A), which is a reduction-based in-processing algorithm that requires retraining as part of the fairness- aware optimization process. GridSearch (option C) systematically trains multiple models across a grid of fairness constraints, again requiring training. CorrelationRemover (option B) is a pre-processing technique that transforms training features before training, also requiring a new training run. ThresholdOptimizer (option D) is a post-processing algorithm: it takes an already-trained model and optimizes its classification thresholds independently for different sensitive-attribute groups to achieve a fairness constraint such as equalized odds or demographic parity, without touching the model weights. This is the only Fairlearn approach that operates purely at inference time on an existing model.
Microsoft Learn Reference Topic: Mitigate unfairness in machine learning models with Fairlearn - Post- processing with ThresholdOptimizer
NEW QUESTION # 44
A team plans to deploy a large foundation model in Microsoft Foundry as part of a new enterprise AI capability.
Different business units across the team ' s organization will access the model from various internal applications.
You need to deploy a foundation model by minimizing latency.
Which deployment type should you use?
Answer: D
Explanation:
Data Zone Standard deployment routes requests to multiple datacenters within a defined geographic zone such as the US data zone, providing lower latency by using zone-level load balancing while maintaining data within a broad compliance boundary. This makes it suitable for enterprise internal applications where multiple business units need low-latency access without strict single-region data residency requirements. Developer deployment (option A) is a low-quota, unguaranteed tier designed for prototype testing, not production workloads serving multiple business units. Data Zone Batch (option B) is designed for high-volume asynchronous batch processing, not interactive low-latency real-time requests. Global Batch (option D) is optimized for scheduled bulk inference across all global regions but adds latency and is inappropriate for synchronous interactive use cases.
Microsoft Learn Reference Topic: Foundation model deployment types in Microsoft Foundry - Data Zone Standard for low-latency internal use
NEW QUESTION # 45
You use an Azure Machine Learning workspace.
You must monitor cost at the endpoint and deployment level.
You have a trained model that must be deployed as an online endpoint. Users must authenticate by using Microsoft Entra ID.
What should you do?
Answer: B
NEW QUESTION # 46
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals.
You work in Microsoft Foundry with a prompt flow.
You must manually evaluate prompts and compare results across prompt variants.
You need to capture the inputs, outputs, token usage, and latencies for each flow run for the evaluation.
Solution: Configure Azure Monitor to collect logs from the workspace. Use the logs to perform prompt evaluation.
Does the solution meet the goal?
Answer: B
Explanation:
Azure Monitor is valuable for production observability , but it is not the appropriate mechanism for the stated development-time requirement: manually inspecting individual prompt-flow executions and comparing prompt variants with their inputs, outputs, token consumption, and latency.
Prompt flow provides purpose-built run outputs and tracing . Microsoft documents that after a flow execution, the Outputs experience exposes detailed flow inputs and outputs. When tracing is enabled, the Trace view provides execution duration and token information and lets the developer expand individual steps to inspect their inputs and execution details.
Prompt flow also directly supports variants and batch/evaluation runs. Multiple variants can be executed and compared, while the run details expose per-test-case inputs, outputs, token counts, duration, and evaluation results. Microsoft explicitly states that multiple runs can be selected to compare their metrics and outputs.
Azure Monitor/Application Insights can collect operational metrics such as token consumption and flow or node latency for deployed applications, but that telemetry is primarily intended for monitoring and troubleshooting rather than the interactive prompt-variant evaluation workflow described here.
Therefore, the proposed solution does not meet the goal .
Study Guide Reference: Implement generative AI quality assurance and observability - prompt evaluation, tracing, debugging, token consumption, latency, and prompt-variant comparison.
NEW QUESTION # 47
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