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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design and implement a GenAIOps infrastructure | 20–25% | - Implement infrastructure for generative AI workloads
|
| Topic 2: Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
| Topic 3: Implement machine learning model lifecycle and operations | 25–30% | - Register, version, and package models
|
| Topic 4: Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
| Topic 5: Implement generative AI quality assurance and observability | 10–15% | - Evaluate and test generative AI applications
|
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NEW QUESTION # 47
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: B
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 # 48
A team uses a hosted Git repository to store training code and pipeline definitions of a machine learning experiment.
The team must ensure that access to the repository is granted without requiring each developer to store personal access tokens on their machines.
Repository access must be secure and centrally managed to reduce credential spread.
You need to enable secure access between an Azure Machine Learning workspace and the repository.
Answer: D
Explanation:
A managed identity is the appropriate credential model when an Azure-hosted workload requires centrally controlled, non-user authentication to another service that supports Microsoft Entra identities. Managed identities eliminate developer-managed credentials because Azure manages the identity lifecycle and obtains short-lived Microsoft Entra tokens at runtime. Microsoft explicitly identifies managed identities as a mechanism for Azure-hosted applications to authenticate without storing credentials, and Azure DevOps supports managed identities for secure automation and repository-related access.
This satisfies the key MLOps security requirement: authentication is associated with the workload rather than individual developers. Administrators can grant the identity only the required repository permissions, enforcing least privilege while centralizing authorization. Microsoft also recommends Microsoft Entra-based authentication over higher-risk PAT-based approaches for Azure Repos.
Option A spreads a reusable private credential among developers and creates unnecessary exposure. Option B still introduces a PAT that must be stored, protected, rotated, and revoked. Option C depends on individual interactive authentication and is unsuitable for reliable automated MLOps workflows. Microsoft documents managed identities and workload identities as approaches that eliminate persistent secrets and reduce credential-management overhead.
Study Guide Reference: Design and implement an MLOps infrastructure - source-control integration, workload authentication, managed identities, least-privilege access, and credential management.
NEW QUESTION # 49
Drag and Drop Question
You build and manage a model by using Azure Machine Learning workspace.
Before you deploy the model, you must create a Responsible AI dashboard in Azure Machine Learning studio. The dashboard must provide observation of the following:
- metrics that show real-world impact on an outcome of interest due to
taking a treatment policy
- examples with minimal changes to a particular data point such that
the model's prediction changes
You need to implement the components for the Responsible AI dashboard.
Which components should you implement? To answer, move the appropriate components to the correct observations. You may use each component 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:
Explanation:
Box 1: Causal analysis
To observe metrics that show the real-world impact on an outcome of interest due to taking a treatment policy, you must implement the Causal analysis component.
Real-World Intervention: Causal analysis specifically uses historical data to evaluate how changing a particular feature (the treatment) directly impacts a real-world outcome.
Policy Making: It generates policy insights by showing which treatment policy is most effective for a specific cohort to optimize your outcome of interest.
Box 2: Counterfactual analysis
You must implement Counterfactual analysis.
The Counterfactual what-if component of the Azure Machine Learning Responsible AI dashboard is designed specifically to "interrogate" a model. It calculates the exact feature tweaks necessary to reverse or modify a specific forecast:
Minimizing Changes: It generates a set of examples demonstrating the smallest possible adjustments to a data point that cause the model's prediction to flip.
Actionable Scenarios: It surfaces the closest data points with opposing or alternative predictions, allowing you to easily test "what-if" scenarios.Why Other Components Do Not Match Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai-dashboard
NEW QUESTION # 50
You have a deployment of an Azure OpenAI Service base model.
You plan to fine-tune the model.
You need to prepare a file that contains training data.
Which file format should you use?
Answer: C
Explanation:
To fine-tune a model in the Azure OpenAI Service, your training data must be in JSONL (JSON Lines) format.
File Requirements
Format: Each line of the file must be a single, valid JSON object.
Encoding: The file must be encoded in UTF-8.
Special Requirement: For some older tools or specific Azure OpenAI use cases, the file may need to include a byte-order mark (BOM).
Size Limits: Individual files typically have a maximum size (e.g., 100 MB per file in some contexts), but you can often upload multiple files.
Reference:
https://dev.to/icebeam7/fine-tuning-a-model-with-azure-open-ai-studio-39p7
NEW QUESTION # 51
A team accidentally deploys an outdated model version due to incorrect tagging. You need to enforce strict deployment governance and version control. What should you implement?
Answer: A
Explanation:
Version pinning ensures that only specific, approved model versions are deployed, while approval workflows enforce governance. This prevents accidental deployment of outdated or unverified models. Simple naming conventions or tagging are error-prone and do not provide sufficient control.
NEW QUESTION # 52
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