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| Section | Objectives |
|---|---|
| Implement secure and scalable AI systems | - Scalability and performance optimization
|
| Operationalizing machine learning solutions | - Deployment and monitoring
|
| Plan and design AI solutions using Azure AI services | - Responsible AI design
|
| Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
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NEW QUESTION # 123
Drag and Drop Question
You have an Azure Machine Learning workspace named WS1 and a GitHub account named account1 that hosts a private repository named repo1.
You need to clone repo1 to make it available directly from WS1. The configuration must maximize the performance of the repo1 clone.
Which four actions should you perform in sequence?
Answer:
Explanation:
Explanation:
Step 1: Create a compute instance
Git integration for Azure Machine Learning
Clone Git repositories into your workspace file system
Azure Machine Learning provides a shared file system for all users in the workspace. To clone a Git repository into this file share, we recommend that you create a compute instance (Step 1) & open a terminal (Step 2). Once the terminal is opened, you have access to a full Git client and can clone and work with Git via the Git CLI experience.
We recommend that you clone the repository into your user directory so that others will not make collisions directly on your working branch.
Step 2: Open a terminal window
Step 3: Generate a Secure Shell (SSH) key pair
Authenticate your Git Account with SSH
Generate a new SSH key
1. Open the terminal window in the Azure Machine Learning Notebook Tab.
2. Paste the text below, substituting in your email address.
ssh-keygen -t rsa -b 4096 -C your_email@example.com
This creates a new ssh key, using the provided email as a label.
3. When you're prompted to "Enter a file in which to save the key" press Enter. This accepts the default file location.
4. Verify that the default location is '/home/azureuser/.ssh' and press enter. Otherwise specify the location '/home/azureuser/.ssh'.
5. At the prompt, type a secure passphrase. We recommend you add a passphrase to your SSH key for added security Step 4: Add a public key to account1.
Add the public key to Git Account
1. In your terminal window, copy the contents of your public key file. If you renamed the key, replace id_rsa.pub with the public key file name.
cat ~/.ssh/id_rsa.pub
2. Select and copy the SSH key output to your clipboard.
3. Next, follow the steps to add the SSH key to your preferred account type:
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-train-model-git-integration
NEW QUESTION # 124
Case Study 1 - Fabrikam Inc.
Background
Fabrikam Inc. is a mid-sized healthcare analytics company that provides population health dashboards and predictive insights to regional hospital systems across the United States.
Fabrikam Inc. customers rely on near real time analytics to monitor patient flow, staffing needs, and readmission risks. They use multiple traditional forecasting machine learning models for predictions.
Fabrikam Inc. has an established Microsoft Azure footprint. The company uses Jupyter Notebooks that run on a local server as the primary development environment. The data science team is experiencing scalability, asset management and code management issues with the current development platform. Fabrikam Inc. plans to migrate to a cloud-based development environment to mitigate the issues.
Additionally, the company plans to implement a Retrieval-Augmented Generation (RAG)-based chat application for client support. Leadership requires the application to be developed and deployed with a low operational risk.
Current Environment
Fabrikam Inc. operates a single Azure subscription that has the following components:
* Azure Data Lake Storage Gen2 that contains de-identified clinical and operational datasets
* Azure AI Search indexing curated analytical documents and reference materials
* A small set of Python-based training scripts maintained by data scientists
* Azure OpenAI Service with deployed foundational models
* A Microsoft Foundry resource for building a RAG-based solution
Evaluation data has manually defined expected responses.
The current challenges faced by the data science team include the following:
* Model training jobs are run manually from notebooks.
* Experiment tracking is inconsistent
* Model versions are registered without standardized metadata.
* Deployment is performed manually by data scientists, with limited rollback capability.
* The team has no standardized evaluation process for generative AI outputs.
The environment currently allows public network access. Authentication relies on user accounts rather than managed identities. Compute targets are manually created and shared across experiments. This has led to resource contention during peak usage.
Business Requirements
Fabrikam Inc. has the following business requirements for the modernization initiative:
* Provide a conversational interface that answers analytics questions by using internal documents and datasets.
* Ensure that sensitive healthcare-related data is not exposed outside the Fabrikam Inc. Azure tenant.
* Enable repeatable and auditable model training and deployment processes.
* Support experimentation to compare prompt strategies and fine-tuned models.
* Align the model with the ranked preferences and optimize behavior for the long term.
* Minimize disruption to existing analytics workloads during rollout.
Technical Requirements
To support the business goals, Fabrikam Inc. identifies these technical requirements:
* Use Azure Machine Learning workspaces to centrally manage data assets, models, and environments.
* Implement experiment tracking and model versioning for all training jobs.
* Orchestrate training and evaluation by using pipelines rather than manually running notebooks.
* Deploy traditional machine learning models with support for staged rollout and rollback.
* Improve RAG-based solution output quality.
* Use the existing evaluation datasets that are based on real data with input-output pairs.
* Apply advanced fine-tuning techniques only when prompt engineering is insufficient Issues and Constraints Fabrikam Inc. must comply with internal security policies that require the company to restrict network access and avoid long-lived secrets. The data science team has limited Azure DevOps experience, so solutions must favor managed services and automation over custom infrastructure.
Cost predictability is important. Leadership prefers serverless or managed compute options where possible but is willing to approve dedicated compute for stable production workloads.
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. The solution must support experimentation and gradual rollout while ensuring governance, security, and operational stability. The data science and platform teams must collaborate to deliver this solution by using Azure Machine Learning and Microsoft Foundry capabilities.
Hotspot Question
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.
Answer:
Explanation:
NEW QUESTION # 125
Drag and Drop Question
A customer-facing web application uses a foundational model deployed through Microsoft Foundry.
A new model version must be introduced and validated without disrupting production traffic.
You need to deploy the new version by using a safe promotion strategy.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
NEW QUESTION # 126
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 on the review screen.
You work in Microsoft Foundry with a prompt flow.
You must manually evaluate prompts and compare results across prompt variants.
You need to capture the inputs, outputs, token usage, and latencies for each flow run for the evaluation.
Solution: In Microsoft Foundry, turn on Tracing for the prompt flow of the project and execute test runs to produce trace data.
Does the solution meet the goal?
Answer: A
Explanation:
Navigate to the Microsoft Foundry project in the Azure portal or Foundry portal and open the prompt flow for which you want to capture telemetry. Locate the Tracing toggle in the project or flow settings and enable it - this single action instructs the Foundry runtime to instrument every LLM call within the flow. Execute test runs of the prompt flow through the portal ' s test interface or via the CLI. Open the Traces view in Microsoft Foundry: each run appears as a trace with a tree of spans, where each span corresponds to one LLM call or tool invocation. Every span captures the exact input sent to the model, the model ' s output, token counts including prompt tokens and completion tokens, and wall-clock latency. Compare runs across prompt variants by examining individual traces side-by-side. This solution directly satisfies all four capture requirements.
Microsoft Learn Reference Topic: Enable and use tracing in Microsoft Foundry - Capturing LLM call telemetry for evaluation
NEW QUESTION # 127
You have an Azure subscription that contains a resource group named rg-ml.
You plan to create an Azure Machine Learning workspace named workspacel in rg-ml by using Azure Machine Learning Python SDK v2.
You need to ensure workspacel is configured to prevent the collection of potentially sensitive data by Microsoft diagnostics.
How should you complete the provided code? To answer, select the appropnate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 128
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