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| Section | Objectives |
|---|---|
| Implement machine learning model lifecycle and operations | - Monitor model performance, data drift, and operational health - Deploy models to real-time and batch endpoints - Retrain, update, and manage model versions in production - Train, register, and version models using Azure Machine Learning |
| Optimize generative AI systems and model performance | - Fine-tune and distill models for specific use cases - Implement cost management and scaling strategies for GenAI workloads - Optimize inference performance, caching, and throughput - Tune prompts, system messages, and grounding strategies |
| Implement generative AI quality assurance and observability | - Conduct red teaming, adversarial testing, and content filtering - Implement logging, tracing, and telemetry for GenAI applications - Evaluate generative AI outputs for quality, safety, and grounding - Monitor latency, token usage, cost, and error rates |
| Design and implement an MLOps infrastructure | - Manage environments, data stores, and model registries - Configure source control, CI/CD pipelines, and automation for ML workflows - Set up Azure Machine Learning workspace and compute targets - Implement security, governance, and compliance for MLOps |
| Design and implement a GenAIOps infrastructure | - Configure prompt orchestration, prompt flows, and agent frameworks - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Manage API keys, rate limits, and responsible AI guardrails - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search |
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NEW QUESTION # 153
You manage an Azure Machine learning workspace.
You build a custom model you must log with Mlftow. The custom model includes the following:
* The model is not natively supported by Mlflow.
* The model cannot be serialized in Pickle format.
* The model source code is complex.
* The Python library tor the model must be packaged with the model.
You need to create a custom model flavor to enable logging with ML. flow.
What should you use?
Answer: D
NEW QUESTION # 154
You are authoring a notebook in Azure Machine Learning studio.
You must install packages from the notebook into the currently running kernel. The installation must be limited to the currently running kernel only.
You need to install the packages.
Which magic function should you use?
Answer: A
Explanation:
The correct choice is %pip . Azure Machine Learning compute instances can contain multiple Jupyter kernels, so package installation must target the interpreter associated with the notebook ' s currently active kernel . Microsoft explicitly recommends the %pip or %conda IPython magic commands for this purpose because these commands install packages into the environment associated with the running kernel.
For example, a notebook cell can contain:
%pip install scikit-learn
This ensures that the installed package becomes available to code executed by that notebook kernel.
By contrast, !pip invokes a shell command. Microsoft specifically warns against using !pip or !conda when the intention is to modify only the current notebook kernel, because shell-level package commands can reference environments or packages outside the active kernel.
%load has an entirely different purpose: it loads Python source code into a notebook cell and does not perform package installation.
Therefore, when package installation must be isolated to the active Azure Machine Learning notebook kernel,
%pip is the appropriate magic command .
Study Guide Reference: Design and implement an MLOps infrastructure - Azure Machine Learning development environments, notebook kernels, dependency management, and compute instances.
NEW QUESTION # 155
You manage an Azure Machine Learning workspace by using the Python SDK v2.
You must create a compute cluster in the workspace. The compute cluster must run workloads and properly handle interruptions. You start by calculating the maximum amount of compute resources required by the workloads and size the cluster to match the calculations.
The cluster definition includes the following properties and values:
* name= " mlcluster1''
* size= " STANDARD.DS3.v2 "
* min_instances=1
* maxjnstances=4
* tier= " dedicated "
The cost of the compute resources must be minimized when a workload is active Of idle. Cluster property changes must not affect the maximum amount of compute resources available to the workloads run on the cluster.
You need to modify the cluster properties to minimize the cost of compute resources.
Which properties should you modify? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 156
Drag and Drop Question
You manage an Azure Machine Learning workspace. You train a model named model1.
You must identify the features to modify for a differing model prediction result.
You need to configure the Responsible AI (RAI) dashboard for model1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
To setup the Responsible AI (RAI) dashboard in Azure Machine Learning and specifically analyze minimal feature modifications needed to change a prediction result, you must use Counterfactual Analysis.
Here are the specific sequential steps you need to take:
Step 1: Load and configure the Responsible AI Insights dashboard constructor component.
Initialize the RAI Insights Dashboard Constructor
1. Create the root pipeline component that acts as the container for your tools.
2. Call the RAI Insights dashboard constructor component.
3. Pass your registered model and your test dataset as the mandatory inputs to this component.
Step 2: Add the Counterfactuals to Responsible AI Insights dashboard
Configure Counterfactual Analysis
1. Add the specific component required to identify what features to modify for a differing prediction.
2. Call the Add Counterfactuals to RAI Insights dashboard component.
3. Link its input to the output of the constructor component initialized in Step 1 above.
4. Configure the parameter variables, including the number of counterfactual examples you want to generate per data point.
Step 3: Use the Gather Responsible AI Insights dashboard component to present the dashboard.
Assemble and Submit the Dashboard Pipeline
1. Gather the components into a cohesive Azure ML pipeline job to execute them.
-> 2. Call the Gather RAI Insights dashboard component to aggregate the constructor and the counterfactual tool outputs.
3. Submit the pipeline job to your Azure Machine Learning workspace compute cluster for processing.
Reference:
https://oneuptime.com/blog/post/2026-02-16-how-to-implement-responsible-ai-dashboards-in-azure-machine-learning/view
NEW QUESTION # 157
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:
Explanation:
When evaluating a fine-tuned model against the base model using the same evaluation dataset, the interpretation of each metric requires careful analysis. A statement that the fine-tuned model improves on the target task is True if and only if the target metric such as task-specific accuracy, F1 score, or ROUGE score shows a statistically meaningful improvement. A statement about regression on a complementary metric is True if the fine-tuned model ' s score on that metric is meaningfully lower than the base model ' s. In Microsoft Foundry ' s evaluation framework, both pre-fine-tuning and post-fine-tuning results are stored against the same experiment, enabling direct side-by-side comparison. The core principle is that improvement on the primary task is not sufficient if fine-tuning causes degradation on safety or coherence - this is called catastrophic forgetting, and the evaluation dataset is designed to detect it.
Microsoft Learn Reference Topic: Evaluate fine-tuned models in Microsoft Foundry - Compare base and fine-tuned model metrics
NEW QUESTION # 158
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