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| Section | Weight | Objectives |
|---|---|---|
| Implement machine learning model lifecycle and operations | 25–30% | - Monitor and maintain models in production
|
| Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
| Implement generative AI quality assurance and observability | 10–15% | - Evaluate and test generative AI applications
|
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NEW QUESTION # 176
You manage a Retrieval-Augmented Generation (RAG) system that uses Azure AI Search to retrieve documents from an indexed knowledge base.
The system must support the following retrieval requirements:
Queries that include exact policy identifiers must return matching documents even when semantic similarity is low.
Natural-language questions must prioritize semantically relevant documents even when keywords are not an exact match.
You need to configure the retrieval approach to meet the requirements.
How should you configure the retrieval behavior for each requirement? To answer, select the appropriate options in the answer area . NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Different query types require fundamentally different retrieval algorithms. For queries that include exact policy identifiers, keyword or BM25 search is the correct choice because BM25 scores documents based on term frequency and inverse document frequency - an exact match on a specific policy identifier such as POL-
2024-HR-042 scores very highly regardless of semantic context. This is the right approach when semantic similarity is low but exact term matching is critical. For natural-language questions where keywords may not be an exact match, semantic or vector search is the correct choice because vector embeddings capture meaning rather than exact tokens, finding relevant documents even when the user ' s vocabulary differs from the document ' s terminology. Azure AI Search supports both modes through its hybrid search capability, and the correct configuration maps each query type to its optimal retrieval algorithm.
Microsoft Learn Reference Topic: Configure hybrid search in Azure AI Search - BM25 keyword search vs.
semantic vector search
NEW QUESTION # 177
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 configure an optimization method to meet Fabrikam Inc.'s technical requirements.
Which strategy should you apply first? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 178
Fabrikam Inc. must improve its deployment process because traditional machine learning models are deployed manually and the organization has limited rollback capability .
You need to recommend a deployment approach that supports staged rollout and rollback while minimizing operational overhead.
Which deployment approach should you recommend?
Answer: C
Explanation:
Managed online endpoints with traffic splitting are the best fit because Azure Machine Learning supports multiple deployments behind a single online endpoint and allows production traffic to be distributed between them. This enables a blue-green or progressive rollout strategy : deploy the new model version alongside the current version, validate it independently, send a small percentage of production traffic to it, monitor behavior, and then gradually increase traffic. If problems occur, traffic can be redirected immediately to the previous deployment, providing a practical rollback mechanism.
This approach also aligns with Fabrikam's requirement to favor managed services and automation because the team has limited Azure DevOps experience. Managed online endpoints handle much of the serving, scaling, security, and monitoring infrastructure automatically and support both traffic splitting and mirrored traffic for safer validation.
Option B can also implement blue-green deployment, but AKS introduces substantially more infrastructure and operational management than Fabrikam requires. Option A would require custom deployment, routing, scaling, and rollback logic. Option D is intended for asynchronous batch inference rather than staged real-time production serving.
Study Guide Reference: Implement machine learning model lifecycle and operations - managed online endpoints, multiple deployments, traffic splitting, blue-green deployment, staged rollout, monitoring, and rollback.
NEW QUESTION # 179
You manage an Azure Machine learning workspace. You develop a machine learning model.
You must deploy the model to use a low-priority VM with a pricing discount.
You need to deploy the model.
Which compute target should you use?
Answer: C
Explanation:
The best compute target for deploying a model using low-priority VMs (or their modern successor, Spot VMs) is an Azure Machine Learning compute cluster.
Best Compute Target: AML Compute Cluster
For low-priority/Spot pricing, you should use an Azure Machine Learning compute cluster configured with the LowPriority tier.
Primary Use Case: This target is specifically recommended for batch deployments. Batch inference is ideal for low-priority VMs because these jobs are asynchronous and can tolerate the interruptions (preemptions) inherent to discounted capacity.
Pricing Advantage: Low-priority VMs offer significant discounts-often up to 80% off standard rates-by utilizing unused Azure capacity.
Automatic Handling: When a node is preempted during a batch job, Azure Machine Learning automatically attempts to replace the lost capacity and re-queues failed tasks to the cluster.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-low-priority-batch
NEW QUESTION # 180
Hotspot Question
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.
The default datastore of workspace1 contains a folder named sample_data. The folder structure contains the following content:
You write Python SDK v2 code to materialize the data from the files in the sample_data folder into a Pandas data frame.
You need to complete the Python SDK v2 code to use the MLTable folder as the materialization blueprint.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point
Answer:
Explanation:
NEW QUESTION # 181
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