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| Section | Objectives |
|---|---|
| Topic 1: Implement machine learning model lifecycle and operations | - Deploy models to real-time and batch endpoints - Monitor model performance, data drift, and operational health - Retrain, update, and manage model versions in production - Train, register, and version models using Azure Machine Learning |
| Topic 2: Optimize generative AI systems and model performance | - Optimize inference performance, caching, and throughput - 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 |
| Topic 3: Implement generative AI quality assurance and observability | - Evaluate generative AI outputs for quality, safety, and grounding - Monitor latency, token usage, cost, and error rates - Implement logging, tracing, and telemetry for GenAI applications - Conduct red teaming, adversarial testing, and content filtering |
| Topic 4: Design and implement an MLOps infrastructure | - Manage environments, data stores, and model registries - 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 |
| Topic 5: Design and implement a GenAIOps infrastructure | - Configure prompt orchestration, prompt flows, and agent frameworks - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Manage API keys, rate limits, and responsible AI guardrails |
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NEW QUESTION # 148
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 # 149
Hotspot Question
You have an Azure Machine Learning workspace and a collection of image files stored in two Azure Blob Storage accounts.
You need to configure data asset properties.
Which values should you use in your configuration? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: uri_folder
The best data asset type for this scenario is a File data asset (uri_file or uri_folder).
In Azure Machine Learning, image files used for training computer vision models are best managed as file-based data assets because machine learning frameworks read individual images directly from storage paths rather than tabular rows.
Preserves Formats: Keeps images in their native formats (PNG, JPEG).Direct Access: Allows training scripts to mount or download files easily.
*-> Folder Mapping: A uri_folder references the entire directory containing your images.
Efficiency: Avoids the overhead of parsing unstructured binary data into a table.
Box 2: azureml
To point a uri_folder data asset to your Azure Blob Storage locations in Azure Machine Learning, you should use the azureml:// URI scheme.
This is the recommended, modern standard that leverages Azure ML datastores for secure tokenless access.
Alternatively, you can use direct Azure Storage URI schemes depending on your configuration Recommended Scheme: Azure ML Datastore This scheme abstracts the storage credentials by referencing an Azure ML Datastore.
Format: azureml://datastores/<datastore_name>/paths/<path_to_folder>/
Example: azureml://datastores/myblobdatastore/paths/images/training_set/ Incorrect:
[not wasbs]
Azure Blob Storage (wasbs)
Used when connecting via the legacy Windows Azure Storage Blob driver.
Format:
wasbs://<container_name>@<storage_account_name>.blob.core.windows.net/<path_to_folder>/
[Not abfss]
Azure Data Lake Storage Gen2 (abfss)
Used if your Blob Storage accounts have the hierarchical namespace enabled.
Format:
abfss://<container_name>@<storage_account_name>.dfs.core.windows.net/<path_to_folder>/ Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-data-assets
NEW QUESTION # 150
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. NOTE: Each correct selection is worth one point. Choose two .
Answer: B,D
Explanation:
A loan approval model must be assessed for both transparency and demographic fairness before registration.
Option D (feature importance) addresses prediction transparency - using SHAP or Azure ML ' s Responsible AI dashboard, the team can see which features drive individual predictions, revealing whether proxy variables for protected attributes are influencing decisions inappropriately. Option E (error rates across demographic cohorts) directly measures whether the model makes more mistakes for specific groups - a core fairness metric required under Responsible AI principles. Option A (global error rates) masks disparities since you need cohort-level analysis. Option B (endpoint latency) is a performance engineering concern. Option C (inference schema compatibility) is a technical integration check. The Azure ML Responsible AI dashboard combines both error analysis and feature importance in a unified interface designed for exactly this type of pre-deployment fairness assessment.
Microsoft Learn Reference Topic: Responsible AI dashboard in Azure Machine Learning - Error analysis and model interpretability
NEW QUESTION # 151
A company's platform engineers manage the resource settings and governance of Microsoft Foundry.
Developers must be able to create and update project assets but must not be able to change resource-level configurations.
You need to enforce least privilege access for the engineers and developers.
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: B,D
Explanation:
[A]
Engineer Permissions (Hub Scope)
Engineers require the ability to manage infrastructure, networking, and global security settings.
Role: Azure AI Administrator or Contributor.
Assignment Scope: Assign at the Foundry Hub/Resource level.
Capabilities: They can manage virtual networks, customer-managed keys, and shared connections (e.g., Azure OpenAI) that all projects inherit.
[C]
Developer Permissions (Project Scope)
Developers should be restricted from changing the underlying resource configurations but need full access to their specific AI workloads.
Role: Azure AI Developer or Project Contributor.
Assignment Scope: Assign strictly at the Foundry Project level.
Capabilities: This allows them to create and update project assets (agents, flows, evaluations) and deploy models without permission to modify Hub-level infrastructure or security settings Reference:
https://learn.microsoft.com/en-us/azure/foundry/concepts/rbac-foundry
NEW QUESTION # 152
A team develops and manages a conversational assistant by using Microsoft Foundry.
The team requires generative AI to automatically evaluate every pull request of an agentic application and fail the build if safety thresholds are exceeded.
You need to automate evaluations as part of CI.
What should you configure?
Answer: A
Explanation:
A GitHub Actions workflow can absolutely be configured to automatically evaluate pull requests using Azure AI Foundry and fail the CI build if safety thresholds are exceeded.By integrating the Azure AI Evaluation SDK into your automated testing suite, you can scan agentic applications for safety risks (like jailbreaks, hate speech, or content harms) during the continuous integration (CI) lifecycle.
Reference:
https://medium.com/@cataldi.ricardo/compliance-and-governance-app-with-pyrit-39526bd2e4f8
NEW QUESTION # 153
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