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| Section | Objectives |
|---|---|
| Optimize generative AI systems and model performance | - Optimize inference performance, caching, and throughput - Fine-tune and distill models for specific use cases - Tune prompts, system messages, and grounding strategies - Implement cost management and scaling strategies for GenAI workloads |
| Implement generative AI quality assurance and observability | - Conduct red teaming, adversarial testing, and content filtering - Monitor latency, token usage, cost, and error rates - Implement logging, tracing, and telemetry for GenAI applications - Evaluate generative AI outputs for quality, safety, and grounding |
| Design and implement an MLOps infrastructure | - Manage environments, data stores, and model registries - Implement security, governance, and compliance for MLOps - Set up Azure Machine Learning workspace and compute targets - Configure source control, CI/CD pipelines, and automation for ML workflows |
| Implement machine learning model lifecycle and operations | - Retrain, update, and manage model versions in production - Monitor model performance, data drift, and operational health - Deploy models to real-time and batch endpoints - Train, register, and version models using Azure Machine Learning |
| Design and implement a GenAIOps infrastructure | - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Manage API keys, rate limits, and responsible AI guardrails - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Configure prompt orchestration, prompt flows, and agent frameworks |
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NEW QUESTION # 134
A team deploys a classification model to production and monitors performance and data changes.
The team wants to ensure that significant drops in prediction accuracy automatically trigger the following:
Stakeholders must be notified of the drops.
Retraining must be initiated when thresholds are exceeded
You need to configure monitoring to meet the requirements.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Microsoft ' s guidance on production model monitoring prescribes a four-stage sequence. First, configure an Azure ML model monitor on the deployed endpoint, specifying which metrics to track and on what schedule.
Second, define the threshold value below which model performance is considered unacceptable - this becomes the trigger condition. Third, create an Azure Monitor alert rule that evaluates the monitored metric against the threshold and fires when it is breached; the alert rule is associated with an action group that sends notifications to stakeholders via email, SMS, or Teams webhook. Fourth, the action group includes a webhook action pointing to an Azure Machine Learning pipeline ' s published REST endpoint, which starts the retraining job automatically. This sequence cleanly separates detection, notification, and remediation, matching Microsoft ' s recommended MLOps automation pattern.
Microsoft Learn Reference Topic: Automate model retraining based on monitoring alerts - Azure Machine Learning MLOps
NEW QUESTION # 135
Drag and Drop Question
You have several machine learning models registered in an Azure Machine Learning workspace.
You must use the Fairlearn dashboard to assess fairness in a selected model.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Step 1: Select a metric to measured
You need to choose a baseline performance or fairness metric (such as accuracy rate, precision, or demographic parity) along which the dashboard evaluates the model's overall behavior and cross-group disparities.
Step 2: Select a model feature to evaluated
You must specify a sensitive feature (such as age, gender, or race) to split the data into different subgroups and evaluate potential disparities across them.
Step 3: Select a binary classification or regression model
The Fairlearn package is specifically designed to assess and mitigate unfairness in binary classification and regression tasks. It does not natively support clustering or multiclass models for group fairness assessments in this wizard.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml?view=azureml-api-2
NEW QUESTION # 136
A product team is building a customer support assistant that must respond consistently across multiple channels.
Early testing shows that small wording changes in prompts cause large differences in tone and factual accuracy.
The team needs prompts that are reliable, reusable, and adaptable across multiple use cases without retraining the underlying model.
You need to design prompts that improve response quality while remaining flexible for future changes.
Which two actions should you perform? Each correct answer presents part of the solution.
(Choose two.)
NOTE: Each correct selection is worth one point.
Answer: A,E
Explanation:
To achieve reliable, reusable, and adaptable prompts without retraining the model, you should implement Prompt Flow, use System Messages for tone/role definition, and enforce Output Parsing. These techniques standardize how the model behaves across all channels.
Here is the most effective approach to stabilize your customer support assistant:
[B]
1. Implement Prompt Flow
Instead of hardcoding raw prompts in your code, use Azure Machine Learning Prompt Flow.
Visual Workflows: Create executable flows that link LLMs, prompts, and Python tools.
Versioning: Easily track, iterate, and roll back prompt versions without touching application code.
Evaluation: Test prompt variants systematically against a set of baseline queries to objectively measure factual accuracy and tone before pushing to production.
[C]
2. Standardize System Messages
A well-defined system message serves as the "guardrails" for your assistant. It should dictate the persona, scope of knowledge, and safety rules.
Define the Persona: Instruct the model exactly how to behave (e.g., "You are a helpful, empathetic, and strictly technical support assistant for [Company Name].").Strict Guidelines:
Provide instructions on how to handle out-of-scope queries (e.g., "If you do not know the answer, do not guess. Apologize and route the user to [Support Email].").
Reference:
https://whitebeardstrategies.com/blog/5-best-practices-for-efficient-language-model-prompting/
NEW QUESTION # 137
You manage an Azure Machine Learning workspace.
You must define the execution environments for your jobs and encapsulate the dependencies for your code.
You need to configure the environment from a Docker build context.
How should you complete the rode segment? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 138
A team plans to deploy a large foundation model in Microsoft Foundry as part of a new enterprise AI capability.
Different business units across the team ' s organization will access the model from various internal applications.
You need to deploy a foundation model by minimizing latency.
Which deployment type should you use?
Answer: B
Explanation:
Data Zone Standard deployment routes requests to multiple datacenters within a defined geographic zone such as the US data zone, providing lower latency by using zone-level load balancing while maintaining data within a broad compliance boundary. This makes it suitable for enterprise internal applications where multiple business units need low-latency access without strict single-region data residency requirements. Developer deployment (option A) is a low-quota, unguaranteed tier designed for prototype testing, not production workloads serving multiple business units. Data Zone Batch (option B) is designed for high-volume asynchronous batch processing, not interactive low-latency real-time requests. Global Batch (option D) is optimized for scheduled bulk inference across all global regions but adds latency and is inappropriate for synchronous interactive use cases.
Microsoft Learn Reference Topic: Foundation model deployment types in Microsoft Foundry - Data Zone Standard for low-latency internal use
NEW QUESTION # 139
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