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| Section | Objectives |
|---|---|
| Implement secure and scalable AI systems | - Scalability and performance optimization
|
| Design and implement generative AI solutions | - Large language model integration
|
| Operationalizing machine learning solutions | - Deployment and monitoring
|
| Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
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NEW QUESTION # 112
You are fine-tuning a base language model to analyze customer feedback.
You label examples of support tickets. You must improve classification accuracy by configuring and fine-tuning the base model in Microsoft Foundry.
You need to configure and run fine-tuning.
What should you do first?
Answer: A
Explanation:
In Microsoft Foundry, when configuring and running a fine-tuning job for analyzing customer feedback (e.g., classifying support tickets), you should first enable tracing for all inference calls in the evaluation pipeline.
Tracing is a critical step in the "Evaluate" phase of the fine-tuning workflow, allowing you to capture input/output examples, identify the root cause of classification errors, monitor latency, and analyze model behavior before and after training.
Note:
To fine-tune a model and evaluate it effectively, you should follow this sequence:
1. Enable Tracing for Initial Baseline
Before you fine-tune, you should indeed enable tracing for all inference calls in your existing evaluation pipeline.
Purpose: This creates a clear "paper trail" of how the base model is currently failing.
Benefit: Tracing captures the exact inputs, outputs, and intermediate steps. By analyzing these traces, you can identify if the low accuracy is due to the model not following instructions, missing domain-specific jargon, or struggling with specific ticket categories.
Setup: You can enable Automatic Tracing (currently in preview) in Foundry to log these details to Application Insights without changing your code.
2. Configure and Run Fine-Tuning
Once you have analyzed the traces and prepared your labeled dataset, you can proceed with the fine-tuning job.
3. Continuous Evaluation
After the job completes, you must compare the fine-tuned model against your original traces Reference:
https://devblogs.microsoft.com/foundry/a-developers-guide-to-fine-tuning-gpt-4o-for-image- classification-on-azure-ai-foundry
NEW QUESTION # 113
You have an Azure Machine Learning workspace named workspaces.
You must add a datastore that connects an Azure Blob storage container to workspaces. You must be able to configure a privilege level.
You need to configure authentication.
Which authentication method should you use?
Answer: B
NEW QUESTION # 114
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.
You need to improve a GPT-5 model performance based on Fabrikam Inc.'s technical requirements. Which action should you perform first?
Answer: D
Explanation:
The recommended first action is to evaluate the model output.
Why This Is the Correct First Step
According to OpenAI's Model Optimization Framework, you must measure your baseline performance before moving to advanced optimization techniques like fine-tuning. You cannot objectively determine whether prompt engineering is "insufficient" without an established evaluation suite to quantify where the model diverges from expectations.
Scenario: To support the business goals, Fabrikam Inc. identifies these technical requirements:
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 Reference:
https://developers.openai.com/api/docs/guides/model-optimization
NEW QUESTION # 115
You use the Azure Machine learning SDK v2 tor Python and notebooks to tram a model. You use Python code to create a compute target, an environment, and a taring script. You need to prepare information to submit a training job.
Which class should you use?
Answer: B
NEW QUESTION # 116
Drag and Drop Question
A team iterates prompts used by a generative AI agent. The team must support internal review before releasing changes.
The team must:
- Track prompt changes with a clear history for audit and rollback.
- Compare prompt variants in parallel without affecting the prompt used in the production environment.
You need to select the appropriate source control approach for each requirement.
What should you use for each requirement? To answer, move the appropriate source controls to the correct requirements. You may use each source control 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:
NEW QUESTION # 117
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