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| Section | Objectives |
|---|---|
| Topic 1: Implement generative AI quality assurance and observability | - Evaluate generative AI outputs for quality, safety, and grounding - Conduct red teaming, adversarial testing, and content filtering - Implement logging, tracing, and telemetry for GenAI applications - Monitor latency, token usage, cost, and error rates |
| Topic 2: Implement machine learning model lifecycle and operations | - Retrain, update, and manage model versions in production - Deploy models to real-time and batch endpoints - Train, register, and version models using Azure Machine Learning - Monitor model performance, data drift, and operational health |
| Topic 3: Optimize generative AI systems and model performance | - Implement cost management and scaling strategies for GenAI workloads - Tune prompts, system messages, and grounding strategies - Fine-tune and distill models for specific use cases - Optimize inference performance, caching, and throughput |
| Topic 4: Design and implement an MLOps infrastructure | - Implement security, governance, and compliance for MLOps - Configure source control, CI/CD pipelines, and automation for ML workflows - Set up Azure Machine Learning workspace and compute targets - Manage environments, data stores, and model registries |
| Topic 5: Design and implement a GenAIOps infrastructure | - Manage API keys, rate limits, and responsible AI guardrails - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Configure prompt orchestration, prompt flows, and agent frameworks |
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NEW QUESTION # 98
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. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
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: B
Explanation:
Correct:
* Create an environment.
Incorrect:
* Delete the Python 3.6 - AzureML kernel.
* Delete the Python 3.8 - AzureML kernel.
Note:
Before you can add a new Jupyter kernel on an Azure Machine Learning compute instance terminal, you must create a Conda environment.
Required Workflow
To officially provision and expose the new kernel to your Azure Machine Learning studio Notebooks, you need to execute the following full process from your terminal session:
Create the environment: Provision a new isolated environment (e.g., using conda create -n newenv python=3.10).
Activate the environment: Run conda activate newenv.
Install dependencies: Add the required ipykernel package using conda install ipykernel or pip install ipykernel.
Register the kernel: Bind the new environment configuration to the global Jupyter directory by running:
python -m ipykernel install --user --name newenv --display-name "My New Kernel" Reference:
https://docs.azure.cn/en-us/machine-learning/how-to-access-terminal
NEW QUESTION # 99
An organization is deploying generative AI solutions by using Microsoft Foundry to support multiple production workloads.
The organization has the following workload requirements:
* One workload must be real-time, latency-sensitive, and have predictable global usage patterns that demand consistent performance.
* One workload must have variable performance and be optimized for cost-efficient operation.
You need to select a global deployment type for each workload.
Which type of deployment should you use for each workload requirement? To answer, move the appropriate deployment types to the correct requirements. You may use each deployment type 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:
Real-time, predictable: Global Provisioned
Flexible, cost efficient: Global Standard
For the real-time, latency-sensitive workload with predictable global demand , use Global Provisioned .
Microsoft Foundry defines Global Provisioned deployments as providing reserved model processing capacity and predictable throughput through provisioned throughput units (PTUs). Because capacity is reserved, this deployment type provides lower and more consistent latency than Global Standard and is specifically recommended for sustained, predictable, high-throughput production workloads.
For the workload requiring flexibility and cost-efficient operation , use Global Standard . Global Standard operates on a pay-per-token model and dynamically routes requests across Azure ' s global infrastructure.
Microsoft recommends it as the starting deployment type for most workloads because it offers the lowest price, broad regional availability, and high default quota without requiring the customer to reserve PTU capacity. This makes it appropriate when demand varies and paying for permanently reserved throughput would be inefficient.
The architectural distinction is therefore straightforward: Global Provisioned trades reserved-capacity cost for predictable throughput and reduced latency variance, while Global Standard provides elastic, consumption-based operation with greater latency variability under sustained high load. Microsoft's deployment overview likewise identifies provisioned throughput for predictable low-latency performance and standard deployment for general hosted workloads.
Study Guide Reference: Design and implement a GenAIOps infrastructure - Microsoft Foundry deployment types, Global Standard, Global Provisioned, PTUs, latency predictability, throughput, and cost optimization.
NEW QUESTION # 100
A team plans to deploy a large foundation model in Microsoft Foundry as part of a new enterprise AI capability.
Different business units across the team's organization will access the model from various internal applications.
You need to deploy a foundation model by minimizing latency.
Which deployment type should you use?
Answer: C
Explanation:
In this scenario, Data Zone Standard is the most appropriate deployment type for minimizing latency.
While other options cater to high-volume or testing needs, Data Zone Standard is specifically designed for real-time application traffic with a balance of performance and regional availability.
Why Data Zone Standard is the Correct Choice
Real-Time Processing: Unlike the "Batch" options, Data Zone Standard is built for synchronous, real-time requests from internal applications, ensuring the low latency required for interactive user experiences.
Dynamic Routing: It dynamically routes traffic to the most available data centers within a specific Microsoft-defined data zone (e.g., US or EU), which helps maintain responsiveness even if one region experiences high load.
Higher Quotas: It offers higher default throughput (TPM/RPM) than standard regional deployments, allowing multiple business units to access the model simultaneously without hitting restrictive limits that could cause queuing and latency spikes.
Incorrect:
[Not A]
Developer: This deployment type is typically used for initial testing, prototyping, and experimentation rather than high-performance production workloads accessed by many different business units.
[Not B, not D]
Global Batch & Data Zone Batch: These are asynchronous deployment types. They are designed for processing large datasets (like document summaries or mass sentiment analysis) with a 24- hour turnaround time. While they are 50% cheaper, they are not suitable for real-time applications where immediate response is needed.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/deployment-types
NEW QUESTION # 101
A data science team trains a classification model that predicts loan approval outcomes.
Before registering the model, the team must ensure the following:
- Predictions must not disproportionately impact protected groups.
- Prediction errors can be evaluated across different data segments.
You need to assess whether the model meets Responsible AI expectations.
Which two approaches should you use? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.
Answer: A,E
Explanation:
[D]
To evaluate a trained loan classification model for Responsible AI expectations--ensuring no disproportionate impact on protected groups, evaluating error across segments, and verifying prediction transparency--you can employ SHAP (SHapley Additive exPlanations) values to assess feature importance.
This approach allows you to identify which variables (e.g., credit history, debt levels) drive the model's predictions, fostering trust and fairness.
Feature Importance for Transparency: Use SHAP (model-agnostic) or LIME (local approximations) to explain why the model approved or denied a loan. These techniques identify how each feature contributes to individual predictions.
[E]
To ensure a trained loan approval classification model meets responsible AI expectations-- specifically, that it does not disproportionately impact protected groups and that errors can be evaluated across segments--you should analyze error rates across defined demographic cohorts using Fairness-Aware Machine Learning metrics.
Reference:
https://urfpublishers.com/journal/artificial-intelligence/article/view/explainable-aiml-testing- ensuring-transparency-accountability-and-compliance
https://timvero.com/blog/ethics-in-automated-lending-can-ai-make-fair-credit-decisions
NEW QUESTION # 102
A data science team completes multiple training runs within an experiment by using MLflow.
The team wants to store a selected model in Azure Machine Learning so that it can be versioned and deployed later.
The model must be versioned centrally for reuse across environments.
You need to version the trained model.
Which two actions should you perform? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.
Answer: A,D
Explanation:
To set up versioning for a trained model in an Azure Machine Learning (Azure ML) workspace using MLflow, you must capture the model artifacts during the training run and then register the model into the centralized registry.
[A]
1. Capture Model Artifacts
During each training run, use the MLflow SDK to log the model. This ensures that all necessary files (the model binary, environment dependencies, and the MLmodel metadata) are stored as run outputs in the workspace.
Manual Logging: Use a flavor-specific method like mlflow.sklearn.log_model(model,
"model_path") within an active run.
Automatic Logging: Call mlflow.autolog() before starting your training. This automatically captures metrics, parameters, and the model artifacts for supported frameworks.
Artifact Location: Once logged, artifacts are typically found in the outputs/ folder of the specific run, accessible via the Azure Machine Learning Studio.
[B]
2. Register the Model
After identifying the best-performing run, you register it to the Model Registry. This creates a named, versioned entity that can be accessed across different environments for deployment.
To set up versioning for an MLflow model in Azure Machine Learning (Azure ML) that is accessible across different environments, you should use a centralized Azure ML Registry. While a standard Azure ML Workspace acts as an MLflow server for individual experiments, an Azure ML Registry is the specifically designed feature for sharing models, environments, and components across multiple workspaces and environments within an Azure tenant.
3. Centralized Reuse
By registering the model in the workspace's registry, you establish a single source of truth. You can then load this specific version in any environment (e.g., staging or production) using its registry URI: models:/<model_name>/<version_or_alias>.
Incorrect:
[Not D]
Must use an Azure ML Registry (Central).
Reference:
https://mlflow.org/docs/latest/ml/model-registry/
NEW QUESTION # 103
......
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