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| Section | Weight | Objectives |
|---|---|---|
| Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
| 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
|
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NEW QUESTION # 137
Drag and Drop Question
An organization operates a generative AI application in production by using Microsoft Foundry.
The application serves live user traffic and is updated by a data scientist team regularly as prompts and models evolve.
The application intermittently times out during production use, which requires ongoing visibility into runtime behavior.
The team must also validate model quality and safety before releasing new updates to avoid introducing regressions.
You need to apply the correct mechanisms for continuous runtime monitoring and for release time validation.
Which mechanisms should you use for each requirement? To answer, move the appropriate mechanisms to the correct requirements. You may use each mechanism 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 # 138
During training, pipelines occasionally fail due to schema mismatch caused by upstream data changes. You need a robust and automated solution that prevents invalid data from reaching training steps. What is the BEST approach?
Answer: B
NEW QUESTION # 139
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 .
Answer: A,C,D
Explanation:
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
NEW QUESTION # 140
A data science team plans to evaluate multiple hyperparameter values automatically while training a model in Azure Machine Learning.
The tuning process must run multiple training trials without manually modifying the training script for each run.
You need to automate hyperparameter tuning for the training job.
What should you do?
Answer: D
Explanation:
You must create a tuning job that runs multiple trials with different parameter values In Azure Machine Learning, this automated process is handled during the training phase using a sweep job (or HyperDrive in legacy configurations). You provide a single parameterized training script and define a search space. The platform then automatically spawns and manages multiple separate child runs (trials) across that space without any manual script edits.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
NEW QUESTION # 141
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?
Answer: B
Explanation:
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
NEW QUESTION # 142
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