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| Section | Objectives |
|---|---|
| Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
| Operationalizing machine learning solutions | - ML lifecycle management
|
| Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Implement secure and scalable AI systems | - Scalability and performance optimization
|
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NEW QUESTION # 145
A company has multiple data science teams working on separate machine learning projects.
The company requires models to be auditable, reusable, and governed centrally across teams.
The models must allow team-level isolation for billing.
You need to establish the foundation for governed machine learning operations.
Which action should you perform first?
Answer: D
Explanation:
The ideal first step is to implement a Hub-and-Spoke Workspace Architecture combined with Azure Machine Learning Registries.
This hybrid blueprint natively decouples your organizational boundaries (billing and access) from your machine learning assets (models, environments, and pipelines).
In enterprise-scale machine learning, an Azure Machine Learning Hub Workspace acts as a central governance plane. It lets IT administrators configure security, shared compute resources, networks, and compliance guidelines centrally once. From this central hub, different teams can spin up isolated project workspaces. This provides strict data and team-level separation, which naturally supports precise cost tracking and billing isolation per team.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-hub-workspace
NEW QUESTION # 146
Hotspot Question
A company is creating an internal tool that summarizes long meeting transcripts and extracts action items.
The model must:
- Process text inputs up to 200k tokens long.
- Generate concise summaries in seconds.
- Support interactive testing before integration into the app.
You need to select, deploy, and test a model that supports summarization with low latency.
How should you complete the configuration plan? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 147
Hotspot Question
You manage a Microsoft Foundry project.
You plan to create a search index by using the Microsoft Foundry SDK.
You need to configure the Content field as a prioritized field for semantic ranking. The field is already set to be searchable.
How should you complete the item code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: SemanticConfiguration
In the Azure AI Search / Microsoft Foundry SDK, the SemanticConfiguration class defines a specific semantic configuration (such as the name and the prioritized fields) used to optimize semantic ranking.
Box 2: SemanticField
The SemanticField class is specifically used inside the SemanticPrioritizedFields object to reference the exact name of the field (in this case, "content") that you want to prioritize for titles, keywords, or content.
Reference:
https://learn.microsoft.com/en-us/azure/search/search-get-started-semantic
NEW QUESTION # 148
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 3.8 - AzureML kernel.
Does the solution meet the goal?
Answer: A
Explanation:
No. Deleting an existing Azure Machine Learning kernel is neither required nor appropriate before adding another Jupyter kernel. Azure Machine Learning compute instances are explicitly designed to support multiple kernels simultaneously . Microsoft states that notebooks automatically discover the Jupyter kernels installed on the connected compute instance, allowing users to switch between them from the kernel selector.
To add a kernel, the supported process is to create a new Conda environment, activate it, install pip and ipykernel, and then register that environment by using python -m ipykernel install --user. Existing kernels do not need to be removed.
More importantly, Microsoft warns against deleting Conda environments or Jupyter kernels that you did not create , because doing so can damage Jupyter or JupyterLab functionality on the Azure Machine Learning compute instance. This makes removal of a built-in AzureML kernel particularly inappropriate as a prerequisite for creating another kernel.
The correct approach is therefore to leave the existing kernel intact and create a separate environment/kernel for the new workload.
Study Guide Reference: Design and implement an MLOps infrastructure - Azure Machine Learning notebook environments, kernel management, compute instances, and dependency isolation.
NEW QUESTION # 149
An organization validates generative AI applications during CI/CD Microsoft Foundry.
Evaluation must run automatically and block releases when quality thresholds are NOT met. Manual evaluation is no longer acceptable.
Evaluation must use both predefined quality metrics and custom safety checks.
You need to implement an automated evaluation workflow that supports both built-in and custom metrics .
What should you do?
Answer: D
Explanation:
Define the evaluation dataset with input prompts and expected outputs covering both quality and safety scenarios. Configure built-in evaluators such as Coherence, Fluency, Groundedness, and Content Safety alongside custom Python evaluator functions for domain-specific safety checks. Create a GitHub Actions workflow that triggers on pull requests or merges to the main branch, calling the Azure AI evaluation SDK to run the evaluation job automatically. Add a quality gate by parsing the evaluation results and using a conditional step to fail the workflow when thresholds are not met. Configure GitHub branch protection rules to require the evaluation workflow to pass, preventing merging or deploying code that fails the quality gate.
Enabling application tracing (option A) and monitoring latency (option C) are runtime monitoring tools, not release gates. Reviewing results manually after deployment (option B) directly contradicts the requirement to eliminate manual evaluation.
Microsoft Learn Reference Topic: Automate AI evaluation in CI/CD with GitHub Actions - Microsoft Foundry evaluation integration
NEW QUESTION # 150
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