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| Section | Objectives |
|---|---|
| Topic 1: Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Topic 2: Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
| Topic 3: Implement secure and scalable AI systems | - Scalability and performance optimization
|
| Topic 4: Operationalizing machine learning solutions | - ML lifecycle management
|
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NEW QUESTION # 99
You plan to filter your traces to identify issues while observing how the application is responding. The solution must not use an external knowledge base.
You need to select an evaluation metric.
Which built-in evaluator should you use?
Answer: A
Explanation:
A multi-turn chatbot application intermittently produces responses that are grammatically correct and on-topic but contradict earlier turns in the conversation, creating a confusing user experience. CoherenceEvaluator measures exactly this: whether the flow of ideas across a multi-turn conversation is logically consistent and non-contradictory without requiring an external knowledge base. RelevanceEvaluator (option A) measures whether responses are topically on-point but often requires a reference context or knowledge base.
SimilarityEvaluator (option B) requires a reference answer for comparison. QAEvaluator (option C) is a composite evaluator for question-answering tasks that requires a ground-truth context document.
CoherenceEvaluator is the only option that works purely from the conversation history itself with no external knowledge base, perfectly matching the stated constraint and the multi-turn chatbot use case.
Microsoft Learn Reference Topic: Evaluate conversational AI applications in Microsoft Foundry - CoherenceEvaluator for multi-turn chatbots
NEW QUESTION # 100
Hotspot Question
You manage a Microsoft Foundry project.
You are developing a solution to generate content based on text and images. The solution requires the ability to manage high-volume processing and avoid disruptions to the online workloads.
You need to deploy the solution.
Which deployment type and large language model (LLM) should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Using Global provisioned deployment and a GPT-4o model is an excellent, industry-standard choice for this Microsoft AI Foundry project. This approach ensures you meet your high-volume processing and text-and-image generation requirements without degrading online workloads, offering both guaranteed latency and dedicated resources.
Box 1: GPT-4o
The GPT-4o Model is highly optimized for handling both text and image input. It provides the multi-modal reasoning necessary to generate contextually accurate content based on visual and textual datasets.
Box 2: Global provisioned
Provisioned Deployment Type: Provisioned throughput allows you to allocate dedicated compute capacity (tokens per minute). This guarantees performance and is explicitly designed to handle high-volume processing without disrupting your online workloads, as you will not be sharing constrained resources.
Global vs. Regional: Global provisioned deployments decouple capacity management from specific regions, providing the highest throughput limits and automatic access to resources with predictable costs. However, if your enterprise has strict European data residency regulations, choosing a Regional Provisioned deployment in a local region ensures your traffic and data are processed entirely within the EU.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/provisioned-throughput
NEW QUESTION # 101
You create an MLflow model
You must deploy the model to Azure Machine Learning for batch inference.
You need to create the batch deployment.
Which two components should you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point
Answer: C,D
NEW QUESTION # 102
You create an Azure Machine Learning workspace.
You must create a custom role named DataScientist that meets the following requirements:
Role members must not be able to delete the workspace.
Role members must not be able to create, update, or delete compute resource in the workspace.
Role members must not be able to add new users to the workspace.
You need to create a JSON file for the DataScientist role in the Azure Machine Learning workspace.
The custom role must enforce the restrictions specified by the IT Operations team.
Which JSON code segment should you use?
Answer: D
Explanation:
The following custom role can do everything in the workspace except for the following actions:
It can ' t create or update a compute resource.
It can ' t delete a compute resource.
It can ' t add, delete, or alter role assignments.
It can ' t delete the workspace.
To create a custom role, first construct a role definition JSON file that specifies the permission and scope for the role. The following example defines a custom role named " Data Scientist Custom " scoped at a specific workspace level:
data_scientist_custom_role.json :
{
" Name " : " Data Scientist Custom " ,
" IsCustom " : true,
" Description " : " Can run experiment but can ' t create or delete compute. " ,
" Actions " : [ " * " ],
" NotActions " : [
" Microsoft.MachineLearningServices/workspaces/*/delete " ,
" Microsoft.MachineLearningServices/workspaces/write " ,
" Microsoft.MachineLearningServices/workspaces/computes/*/write " ,
" Microsoft.MachineLearningServices/workspaces/computes/*/delete " ,
" Microsoft.Authorization/*/write "
],
" AssignableScopes " : [
" /subscriptions/ < subscription_id > /resourceGroups/ < resource_group_name > /providers/Microsoft.
MachineLearningServices/workspaces/ < workspace_name > "
]
}
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-assign-roles
NEW QUESTION # 103
You create a workspace by using Azure Machine Learning Studio.
You must run a Python SDK v2 notebook in the workspace by using Azure Machine Learning Studio.
You need to reset the state of the notebook.
Which three actions should you use? Each correct answer presents a complete solution.
Answer: A,B,D
Explanation:
Azure Machine Learning Studio explicitly distinguishes actions that merely interrupt execution from actions that reset notebook state and clear variables . Microsoft documents three relevant operations that reset notebook state: changing the kernel, switching compute, and resetting compute .
C). Reset the compute restarts the notebook execution environment. When another cell is subsequently executed, the compute environment starts again and the previous in-memory notebook state is no longer retained.
D). Change the current kernel causes the notebook to use a different kernel. Because variables, imported objects, and other runtime state belong to the active kernel process, changing the kernel resets that execution state.
E). Change the compute switches the notebook to another compute resource. Microsoft states that switching compute automatically connects the notebook to the selected compute and resets the notebook state and variables.
By contrast, B. Stop the current kernel only stops a currently running cell; executing another cell automatically restarts the kernel while the notebook-state behavior documented for this action does not constitute a reset. Likewise, A. navigating to another workspace section stops running cells but does not reset variables or notebook state.
Study Guide Reference: Implement machine learning model lifecycle and operations - Azure Machine Learning notebooks, compute instances, kernel management, execution environments, and interactive development.
NEW QUESTION # 104
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