A team provisions an Azure Machine Learning environment by triggering pull requests.Deployments must be automated, auditable, and require approval before running.You need to select a deployment automation tool.Which tool should you use?
Answer(s): B
GitHub Actions is the best tool for this scenario.Pull Request Integration: GitHub Actions is a native continuous integration and continuous delivery (CI/CD) platform built directly into GitHub. It can natively listen for repository events—such as triggering a workflow automatically when a pull request is opened, updated, or merged.Required Approvals: It provides built-in governance through Environments. You can configure specific deployment environments (e.g., Production) to enforce required reviewers, ensuring a deployment cannot run until an authorized user manually approves it.Auditable Logs: Every workflow execution, event trigger, and manual approval is tracked chronologically within GitHub. This provides a highly transparent, centralized audit trail for compliance requirements.
https://learn.microsoft.com/en-us/azure/developer/github/github-actions
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(s): D
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.
https://medium.com/@cataldi.ricardo/compliance-and-governance-app-with-pyrit-39526bd2e4f8
A team runs training and inference jobs in Azure Machine Learning.The team experiences inconsistent runtime dependencies that cause variation in results.You need to ensure that all jobs use the same execution dependencies.Which asset should you define?
To guarantee consistent runtime dependencies in Azure Machine Learning, you must use Azure ML Environments configured with custom Docker images or pinned Conda dependencies.An Environment asset encapsulates the exact Python packages, environment variables, and software settings for your training and inference workloads.
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets
You need to recommend an experiment-tracking strategy that ensures consistent experiment results.What should you recommend?
Scenario: The current challenges faced by the data science team include the following: Experiment tracking is inconsistentTo support the business goals, Fabrikam Inc. identifies these technical requirements: Implement experiment tracking and model versioning for all training jobs.---In Azure-based AI operations, integrating MLflow with Azure Machine Learning (Azure ML) provides a unified interface to track experiments, version models, and manage the lifecycle of both traditional ML and Generative AI workloads. Direct Implementation Strategy To ensure consistent experiment results and comparison of prompt strategies versus fine-tuned models, use the following architectural approach:Centralized Tracking: Configure the MLflow tracking URI to point to your Azure ML Workspace. This allows all logs (from local notebooks, remote training jobs, or Prompt Flow) to aggregate in a single "Experiments" dashboard.Prompt Strategy Comparison: Use Azure ML Prompt Flow to develop and test prompt variants. Prompt Flow automatically logs metrics (like groundedness and relevance) which can be viewed alongside fine-tuned model metrics in the Azure ML Studio.Model Versioning: Use the MLflow Model Registry hosted within Azure ML. Each successful training or fine-tuning run should be registered as a new version of a named model, providing a clear lineage from data to deployment.
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-cli-runs
HOTSPOT (Drag and Drop is not supported)You need to deploy the RAG-based chat application that meets Fabrikam Inc.’s business and technical requirements.Which configuration should you use for each requirement? To answer, select the appropriate options in the answer area.Note: Each correct selection is worth one point.Hot Area:
Answer(s): A
Scenario: Business Requirements: Provide a conversational interface that answers analytics questions by using internal documents and datasets.Box 1: Managed Online Endpoint Serve real-time user queriesTo serve real-time user queries in your Azure Machine Learning solution, a Managed Online Endpoint is the best deployment type.Turnkey Infrastructure: Azure manages OS updates, patching, and scaling out-of-the-box.Low Latency: Built specifically for synchronous, high-throughput, real-time inference.Native ML Features: Direct integration with Azure ML data drift, monitoring, and security.Blue/Green Deployment: Safely rollout updates by splitting traffic between models.Incorrect: Azure Container Apps (ACA) Jobs: Built for batch processing, not real-time APIs. They spin up, run a task, and shut down. This introduces massive cold-start latency.Azure Kubernetes Service (AKS) with Custom Containers: Offers maximum control but brings high operational overhead. Choose this only if you require complex custom network topologies or strict multi-cloud portability.Box 2: Azure AI Search index Retrieve internal documentsAzure AI Search is the best choice for retrieving internal documents to power an analytics-focused conversational interface (RAG).While Azure Cosmos DB and Azure SQL Database have specialized retrieval capabilities, Azure AI Search functions as a dedicated enterprise search engine. It outperforms the other options by combining keyword, vector, and semantic ranking out of the box.
https://docs.azure.cn/en-us/machine-learning/concept-endpoints-online
You need to recommend a solution to address Fabrikam Inc.’s limited rollback capability.Which deployment approach should you recommend?
Answer(s): C
Scenario: Current Environment: Deployment is performed manually by data scientists, with limited rollback capability.The correct deployment type to address this problem is Managed online endpoints with traffic splitting.Native traffic control: Managed online endpoints naturally support native blue-green deployments under a single HTTP endpoint.Granular traffic splitting: You can deploy a new model version (e.g., green) with 0% live traffic, safely test it, and then incrementally shift traffic from the old version (e.g., blue).Instant rollbacks: If the new model shows bugs or degrades performance, you can immediately change the traffic percentage configuration back to 100% for the old deployment. This eliminates the risks of limited rollback capabilities.No infrastructure overhead: Unlike setting up manual routing, Azure handles the underlying infrastructure and routing mechanisms in a turnkey fashion.Incorrect: [Not A] VM-hosted REST APIs: This forces you to build, maintain, and configure your own custom load balancers and deployment scripts to handle routing and rollbacks, which increases operational risk and complexity.[Not B] Azure Kubernetes Service with blue-green switching: While it supports blue-green deployments, it requires you to manage complex Kubernetes infrastructure, service meshes, or ingress controllers manually to handle the traffic switching.[Not D] Batch endpoints: These are designed for long-running, asynchronous processing of large data batches rather than real-time requests where instant live-traffic rollback is required.
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-safely-rollout-online-endpoints
You need to make model training repeatable and auditable to address Fabrikam Inc.'s current environment challenges and technical requirements.What should you use?
Azure Machine Learning (AML) training would be best. While the other options can execute code, they lack the native machine learning operations (MLOps) capabilities required for enterprise auditing and repeatability.Azure ML pipelines are purpose-built for machine learning workflows. They natively solve your tracking and auditing requirements.Built-in Lineage: Automatically tracks data inputs, code versions, environments, and output models.Native Component Reuse: Steps are modular, containerized, and easily shared across teams.Data Caching: Skips completed steps if inputs have not changed, saving time and compute costs.Scenario, Technical Requirements: Implement experiment tracking and model versioning for all training jobs. Orchestrate training and evaluation by using pipelines rather than manually running notebooks.Problem Statement Fabrikam Inc. must design and implement an Azure-based AI operations solution that enables reliable training, evaluation, deployment, and iteration of generative AI models.
https://medium.com/data-for-ai/mlops-with-a-feature-store-8dabc845584a
HOTSPOT (Drag and Drop is not supported)A machine learning model is deployed to production in Azure Machine Learning and is actively serving predictions for a business application. The model was trained by using a historical dataset that represented expected input patterns at the time of deployment.The team working on the model must ensure the following:-Changes in input data distribution are detected.-Appropriate actions are triggered when predefined thresholds are exceeded.You need to configure monitoring to meet the requirements.Which configuration should you use for each requirement? To answer, select the appropriate options in the answer area.Note: Each correct selection is worth one point.Hot Area:
Box 1: Enable data drift monitoring.In Azure Machine Learning, enabling data drift monitoring allows you to detect shifts in the statistical distribution of your model's input data, which is a primary cause of model performance degradation. To implement this, you typically set up a Data Drift Monitor (now often referred to under the broader Model Monitoring v2 framework) to compare your production data against a baseline.Box 2: Configure alerts To trigger actions when predefined thresholds are exceeded for a deployed Azure Machine Learning model, you should configure alerts. This is the standard operational procedure for monitoring model performance (such as data drift or accuracy degradation) in production environments.
https://abhishek-reddy.medium.com/detecting-and-managing-data-distribution-shifts-in-the-mlops-lifecycle-for-machine-learning-models-1ea33ce84c3c https://docs.azure.cn/en-us/machine-learning/concept-model-monitoring
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