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| Section | Objectives |
|---|---|
| Implement secure and scalable AI systems | - Security and governance
|
| Operationalizing machine learning solutions | - ML lifecycle management
|
| Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
| Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
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NEW QUESTION # 135
Drag and Drop Question
You have a Microsoft Foundry project with a connected Azure OpenAI Service model.
You have a set of text files stored locally on your computer.
You must set up a flow that will generate responses based on the content of your local files.
You need to implement a solution.
Which four 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:
NEW QUESTION # 136
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: A
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 # 137
A data science team registers an MLflow model in Azure Machine Learning.
The model must support low latency predictions and automatically scale based on incoming request volume.
You need to deploy the model.
Which deployment option should you use?
Answer: A
Explanation:
The best deployment option is an Azure Machine Learning Managed Online Endpoint.
This deployment model is specifically designed for low-latency, real-time predictions and supports native autoscaling based on incoming request volume.
Turnkey MLflow Support: You can deploy your registered MLflow model directly without writing a custom scoring script or defining a docker environment.
Low Latency: Built on high-performance infrastructure designed for real-time inference workloads.
Autoscaling: Automatically scales the number of virtual machine instances up or down based on metrics like CPU usage, memory, or request queue depth.
Traffic Splitting: Supports deploying multiple model versions under one endpoint to safely test new models using blue-green deployment strategies.
Reference:
https://www.interviews.chat/questions/azure-machine-learning-engineer
NEW QUESTION # 138
You train a model in Azure Machine Learning.
You plan to capture experiment details for later comparison. The training code must log parameters and metrics for each run.
You review the following training script.
You need to verify whether the training script meets the experiment tracking requirement.
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:
In Azure ML command jobs, MLflow automatically creates a run context, so explicit start_run is optional.
The training script must call mlflow.log_param to record each hyperparameter for the run. If the script calls this for each hyperparameter, the requirement for capturing parameters per run is met. The script must also call mlflow.log_metric to record numeric metrics such as accuracy or loss, optionally indexed by training step. Metrics and parameters logged inside an Azure ML command job are automatically associated with the correct experiment and run - no manual specification of experiment or run ID is required. If all three elements (run context, parameter logging, metric logging) are present in the script, all experiment tracking statements are True. If any element is absent, the corresponding statement is False and the tracking requirement is not met.
Microsoft Learn Reference Topic: Log metrics, parameters, and artifacts during Azure Machine Learning training runs with MLflow
NEW QUESTION # 139
You manage an Azure Machine Learning workspace
You build an Azure Machine Learning pipeline for image classification by using custom components. You need to define the interface, metadata, and code to execute components from a Python function. Which function should you use?
Answer: A
NEW QUESTION # 140
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