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| Section | Objectives |
|---|---|
| Topic 1: Operationalizing machine learning solutions | - ML lifecycle management
|
| Topic 2: Implement secure and scalable AI systems | - Security and governance
|
| Topic 3: Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
| Topic 4: Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
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96. Frage
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: Create prompt variants and compare their outputs in the Evaluation experience.
Does the solution meet the goal?
Antwort: B
Begründung:
The Evaluation experience in Microsoft Foundry ' s prompt flow editor shows aggregate quality scores across prompt variants - which variant produces more coherent answers, which scores higher on groundedness.
However, it does not expose per-run raw telemetry: individual token counts per call, per-request latency in milliseconds, or the exact input-output pairs for each execution. The Evaluation experience is designed for comparative quality scoring, not for detailed operational telemetry. To capture inputs, outputs, token usage, and latencies at the granular run level, Tracing must be enabled in Microsoft Foundry. Tracing records each LLM call as a structured span with timing, token consumption, and the complete input-output payload - a fundamentally different view than evaluation scores that directly satisfies all four capture requirements.
Microsoft Learn Reference Topic: Trace and debug prompt flows in Microsoft Foundry - Tracing vs.
Evaluation
97. Frage
Drag and Drop Question
You develop a flow for a Microsoft Foundry project.
You plan to use outputs generated by running the flow to determine the following information:
- the number of tokens used by each large language model (LLM) node of
the flow
- the accuracy of the model used by the flow
You need to examine the output that provides the required information.
Which output type should you examine? To answer, move the appropriate output types to the correct evaluations. You may use each output 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.
Antwort:
Begründung:
Explanation:
Box 1: Traces
The appropriate output flow type to determine the number of tokens used by each Large Language Model (LLM) node is Traces.
While Metrics provide a macro-level, aggregated overview of total token usage across an entire application or resource, Traces capture detailed, node-by-node execution details.
When a prompt flow runs in Microsoft Foundry, Traces track:
The precise execution path of individual items
Inputs and outputs for each specific LLM node
Detailed telemetry records-such as input_tokens, output_tokens, and total_tokens-bound to that exact step Box 2: Metrics The most appropriate output flow type to determine the accuracy of the model is Metrics.
Metrics are quantitative measurements (such as accuracy, F1-score, precision, recall, or mean squared error) specifically calculated by evaluating model predictions against ground truth data.
Reference:
https://huggingface.co/docs/evaluate/a_quick_tour
98. Frage
Hotspot Question
You manage an Azure Machine Learning workspace by using the Python SDK v2.
You must create an automated machine learning job to generate a classification model by using data files stored in Parquet format. You must configure an autoscaling compute target and a data asset for the job.
You need to configure the resources for the job.
Which resource configuration should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
Box 1: Azure Databricks
Autoscaling: Out of the provided choices, Azure Databricks is the only compute target that natively supports the automated scaling up and down of worker nodes required to efficiently match the computation demands of your specific job.
Compatibility: Azure HDInsight and Azure Data Lake Analytics are legacy analytics platforms that do not offer the same direct, optimized, and auto-scaling compute integration within Azure ML SDK v2 pipelines for AutoML training.
Box 2: uri_folder
Directory access: Since your data consists of multiple data files stored in the Parquet format, pointing your asset type to a uri_folder allows the training job to automatically read and ingest all individual Parquet partition files stored inside that directory.
Reference:
https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.ipynb
99. Frage
A company is standardizing generative AI development across multiple teams.
Each team requires an isolated workspace. Governance and shared connections must be centrally managed.
You need to implement a Microsoft Foundry environment structure that supports centralized governance and team isolation .
Which type of configuration should you use for each requirement? To answer, move the appropriate configurations to the correct requirements. You may use each configuration 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.
Antwort:
Begründung:
Explanation:
An Azure AI Hub is the top-level governance container in Microsoft Foundry: it holds shared connections to Azure OpenAI, Azure AI Search, Azure Storage, and other services; it defines network isolation policies; it manages billing and quota at the organizational level. Multiple teams share these resources without each team needing to configure their own connections or negotiate quota independently. An Azure AI Project sits inside the Hub and provides team-level isolation: each project has its own experiments, deployments, prompt flows, evaluations, and fine-tuning jobs, all governed by the Hub ' s shared infrastructure. Different teams get their own project with independent access controls via RBAC, while the platform team manages the shared Hub.
This pattern eliminates redundant resource configurations across teams while maintaining clear team-level boundaries - the correct structure for centralized governance with team isolation.
Microsoft Learn Reference Topic: Microsoft Azure AI Foundry hub and project architecture - Centralized governance and team isolation
100. 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. NOTE: Each correct selection is worth one point. Choose three .
Antwort: A,D,E
Begründung:
Microsoft ' s foundation model selection documentation emphasizes right-sizing: choose models based on task alignment, data governance, and justified operational cost rather than defaulting to the largest or smallest available. A model optimized for conversational reasoning (option A) is correctly matched to an interactive assistant workload. A model supporting multiple input types (option D) is correctly matched to workloads combining text and image analysis. A model offering enterprise governance controls (option E) is correctly matched to workloads processing regulated business data. The largest available model (option B) is not recommended simply for operational simplicity - it adds unnecessary cost without justification. The smallest available model (option C) may fail quality thresholds for complex workloads. The correct selections are the models that align with actual task requirements, governance needs, and cost justification.
Microsoft Learn Reference Topic: Select foundation models in Microsoft Foundry - Task alignment, governance, and cost optimization
101. Frage
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