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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
| Topic 2: Implement machine learning model lifecycle and operations | 25–30% | - Register, version, and package models
|
| Topic 3: Implement generative AI quality assurance and observability | 10–15% | - Evaluate and test generative AI applications
|
| Topic 4: Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| Topic 5: Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
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NEW QUESTION # 37
A company plans to deploy a foundation model in Microsoft Foundry.
The mode must support the following workloads:
A customer support workload used across multiple regions
A marketing workload that must remain within a specific region due to data residency requirements You need to select the deployment type.
Which deployment type should you use for each workload? 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:
For a customer support workload used across multiple regions, Global Standard deployment is the right choice: it routes each request to the nearest available Azure region automatically, reducing latency globally and providing the highest throughput and availability. For a marketing workload that must remain within a specific region due to data residency requirements, a Data Zone Standard or single-region deployment ensures all compute and data processing occurs within a defined geographic boundary, satisfying GDPR and local data sovereignty rules. Microsoft Foundry ' s deployment types are designed around exactly this trade-off:
Global routing for performance-critical multi-region workloads, and Data Zone or Regional isolation for data- residency-constrained workloads. Choosing the wrong deployment type can result in either compliance violations or unnecessary latency.
Microsoft Learn Reference Topic: Model deployment options in Microsoft Foundry - Global, Data Zone, and Regional deployment types
NEW QUESTION # 38
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 refine a GPT-5 model so that its performance and behavior align with the technical and business requirements of Fabrikam Inc.
Which two Foundry strategies should you apply? Each correct answer presents a complete solution. Choose two.
NOTE: Each correct selection is worth one point.
Answer: B,D
Explanation:
Scenario, Technical Requirements:
Improve RAG-based solution output quality.
Apply advanced fine-tuning techniques only when prompt engineering is insufficient Business Requirements:
Provide a conversational interface that answers analytics questions by using internal documents and datasets.
The two Foundry strategies that would be most useful in this scenario are Evaluations and Supervised fine-tuning.
Evaluations: This strategy is critical for improving RAG-based solution output quality. It provides systemic measurement to test how changes to your data, prompts, or retrieval chunks impact the accuracy, relevance, and groundedness of the model's answers before moving to more complex methods.
Supervised fine-tuning: This strategy directly satisfies the requirement to apply advanced fine- tuning techniques only when prompt engineering is insufficient. It allows you to deeply customize the model's behavior and conversational tone to fit your specific analytics interface requirements.
Incorrect:
Guardrails: While valuable for safety and compliance, guardrails focus on enforcing hard operational boundaries (e.g., blocking toxic content or preventing data leaks). They do not optimize RAG output quality or provide advanced behavioral updates when prompt engineering fails.
Synthetic data generation: While helpful for bootstrapping training datasets when real-world data is scarce, it is an data-preparation step rather than a core model refinement strategy designed to iteratively solve prompt engineering limitations or build conversational systems.
Reference:
https://medium.com/@hugoparreao/context-engineering-what-really-improves-the-performance-of-llms-0c0e3ed45c98
NEW QUESTION # 39
You manage an Azure Machine Learning workspace. You have a folder that contains a CSV file. The folder is registered as a folder data asset.
You plan to use the folder data asset for data wrangling during interactive development.
You need to access and load the folder data asset into a Pandas data frame.
Which method should you use to achieve this goal?
Answer: C
Explanation:
Azure Machine Learning distinguishes between uri_folder, uri_file, and mltable data assets. Because the registered asset in this scenario is a folder data asset containing CSV data , the appropriate MLTable API is mltable.from_delimited_files(). Microsoft documents that a registered uri_folder containing CSV files can be retrieved through MLClient, its storage path supplied as a folder path, and then passed to mltable.
from_delimited_files(). The resulting MLTable object can subsequently be materialized into a Pandas DataFrame by calling to_pandas_dataframe().
A representative workflow is conceptually:
tbl = mltable.from_delimited_files(paths=[{ " folder " : data_asset.path}]) followed by:
df = tbl.to_pandas_dataframe()
mltable.load() is primarily appropriate when loading an existing MLTable definition , such as a registered table data asset containing an MLTable file. from_parquet_files() is intended for Parquet-formatted data, while from_delta_lake() targets Delta Lake data sources. Neither matches the CSV-based folder described here. Microsoft also identifies uri_folder as the appropriate asset type for folders containing CSV or Parquet files used with Pandas or Spark.
Study Guide Reference: Design and implement an MLOps infrastructure - Azure Machine Learning data assets, MLTable APIs, interactive data access, and Pandas-based data wrangling.
Continue with Azure ML data access
NEW QUESTION # 40
Hotspot Question
You review the following Azure CLI command and the relevant Bicep excerpt.
(Non-relevant sections are omitted.)
You need to validate what the snippet will do before it is merged. 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:
NEW QUESTION # 41
Drag and Drop Question
A real-time endpoint is deployed in Azure Machine Learning to serve predictions to a web application.
Users report intermittent failures and unexpected responses when calling the endpoint.
You need to identify the appropriate troubleshooting action for each reported issue.
Which troubleshooting action should you perform for each issue? To answer, move the appropriate troubleshooting actions to the correct issues. You may use each troubleshooting action 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 # 42
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