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| Section | Objectives |
|---|---|
| Topic 1: Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Topic 2: Plan and design AI solutions using Azure AI services | - Responsible AI design
|
| Topic 3: Operationalizing machine learning solutions | - ML lifecycle management
|
| Topic 4: Implement secure and scalable AI systems | - Scalability and performance optimization
|
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130. Frage
A real-time endpoint experiences sporadic latency spikes. Investigation reveals instances scale down to zero during inactivity. You need to reduce latency without significantly increasing cost.
What should you configure?
Antwort: A
131. Frage
A team is building a Retrieval-Augmented Generation (RAG) system.
The team observes that the retrieved documents are often irrelevant or incomplete.
You need to improve retrieval accuracy.
What should you adjust?
Antwort: B
Begründung:
To best improve retrieval accuracy when documents are irrelevant or incomplete, you should adjust Chunk size and overlap.
Fixes Irrelevant Retrievals: If your chunks are too large, they pack in multiple unrelated topics.
This adds noise, dilutes the semantic vector, and causes the system to pull in massive blocks of text where only a single sentence was actually relevant. Tuning the size ensures chunks stay highly specific.
Fixes Incomplete Retrievals: If your chunks are too small or lack sufficient overlap, vital context gets completely cut off at the boundaries. Adding an optimal overlap (typically 10%-20%) ensures that a sentence split across a boundary preserves its surrounding meaning, keeping the context whole.
Incorrect:
[Not B]
Temperature parameter: This is a generative LLM setting that dictates the creativity or randomness of the output text. It has absolutely zero control over the vector search database or the specific documents picked during the initial retrieval phase.
[Not C]¨
Token limits: This caps how much context you can pass into the prompt window or how much text the model can generate. While a higher limit lets you pass more documents, it does not magically fix the underlying data quality or prevent the retrieval algorithm from pulling in wrong files.
[Not D]
Embedding strategy: While choosing a stronger model or fine-tuning embeddings changes how text is numerically represented, it cannot overcome poorly structured data. Even the best embedding model cannot successfully capture semantic meaning if your data ingestion pipeline is feeding it randomly sliced, incomplete paragraphs Reference:
https://pmc.ncbi.nlm.nih.gov/articles/PMC12649634/
132. Frage
An organization is deploying several generative AI workloads by using Microsoft Foundry. Each workload must meet different requirements related to data governance, task specialization, and operational cost control.
The organization requires models that meet the following requirements:
- Model behavior aligns with the task being performed.
- Data handling aligns with internal governance policies.
- Operational complexity and cost are justified by workload needs.
You need to select the foundation model options that meet the requirements.
Which three models can you select? Each correct answer presents a complete solution. Choose three.
NOTE: Each correct selection is worth one point.
Antwort: B,C,D
Begründung:
[B] Operational complexity and cost are justified by workload needs.
In a scenario where a single generative AI workload is deployed via Microsoft Foundry and the associated high operational complexity and costs are already justified, utilizing the largest available model (e.g., GPT-4 class models) is an appropriate strategy to simplify operational management.
This approach aligns with a "production-first" or high-performance mindset where, instead of managing multiple smaller, specialized models, a single powerful model provides the necessary reasoning capabilities, accuracy, and broad applicability for complex, high-stakes tasks, reducing the overhead of engineering, fine-tuning, and maintaining several smaller models.
[C] Model behavior aligns with the task being performed
In Microsoft Azure AI Foundry, the most appropriate model to minimize usage costs while maintaining task alignment is typically a Small Language Model (SLM) from the Phi family or a specialized "mini" partner model.
Top Recommended Small Models for Cost-Efficiency
These models are designed for high efficiency and lower latency, making them ideal for specific, well-defined tasks.
[E] Data handling aligns with internal governance policies
In a Microsoft Foundry (Azure AI Foundry) setup, the Azure OpenAI Service models are the appropriate choice for processing regulated business data due to their built-in enterprise governance and security controls. Unlike standard public models, these "Direct Models" are hosted within your Azure tenant and adhere to strict organizational policies.
Enterprise Governance Controls
For workloads involving regulated data, the following governance features in Microsoft Foundry ensure compliance:
Foundry Control Plane: Centralizes management, observability, and compliance enforcement for all models and agents in one interface.
Azure Policy Integration: Allows administrators to enforce specific model usage, restrict deployments to approved regions, and apply predefined security configurations.
Microsoft Purview Integration: Extends data security posture management (DSPM) to AI workloads, enabling sensitive data discovery, classification, and data loss prevention (DLP) across user prompts and model responses.
Microsoft Entra Agent ID: Automatically assigns a unique identity to every AI agent, enabling granular, role-based access control (RBAC) and auditability of model interactions.
Azure AI Content Safety: Provides configurable filters to block harmful or regulated content and includes "protected material detection" to prevent copyright risks.
Reference:
https://azure.microsoft.com/en-us/products/ai-foundry/models
https://learn.microsoft.com/en-us/azure/foundry/responsible-ai/openai/data-privacy
133. Frage
Hotspot Question
You have an Azure Machine Learning workspace and a collection of image files stored in two Azure Blob Storage accounts.
You need to configure data asset properties.
Which values should you use in your configuration? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
Box 1: uri_folder
The best data asset type for this scenario is a File data asset (uri_file or uri_folder).
In Azure Machine Learning, image files used for training computer vision models are best managed as file-based data assets because machine learning frameworks read individual images directly from storage paths rather than tabular rows.
Preserves Formats: Keeps images in their native formats (PNG, JPEG).Direct Access: Allows training scripts to mount or download files easily.
*-> Folder Mapping: A uri_folder references the entire directory containing your images.
Efficiency: Avoids the overhead of parsing unstructured binary data into a table.
Box 2: azureml
To point a uri_folder data asset to your Azure Blob Storage locations in Azure Machine Learning, you should use the azureml:// URI scheme.
This is the recommended, modern standard that leverages Azure ML datastores for secure tokenless access.
Alternatively, you can use direct Azure Storage URI schemes depending on your configuration Recommended Scheme: Azure ML Datastore This scheme abstracts the storage credentials by referencing an Azure ML Datastore.
Format: azureml://datastores/<datastore_name>/paths/<path_to_folder>/
Example: azureml://datastores/myblobdatastore/paths/images/training_set/ Incorrect:
[not wasbs]
Azure Blob Storage (wasbs)
Used when connecting via the legacy Windows Azure Storage Blob driver.
Format:
wasbs://<container_name>@<storage_account_name>.blob.core.windows.net/<path_to_folder>/
[Not abfss]
Azure Data Lake Storage Gen2 (abfss)
Used if your Blob Storage accounts have the hierarchical namespace enabled.
Format:
abfss://<container_name>@<storage_account_name>.dfs.core.windows.net/<path_to_folder>/ Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-data-assets
134. Frage
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.
Antwort: A,B
Begründung:
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
135. Frage
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