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| Section | Objectives |
|---|---|
| Design and implement a GenAIOps infrastructure | - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Manage API keys, rate limits, and responsible AI guardrails - Configure prompt orchestration, prompt flows, and agent frameworks |
| Implement machine learning model lifecycle and operations | - Train, register, and version models using Azure Machine Learning - Monitor model performance, data drift, and operational health - Retrain, update, and manage model versions in production - Deploy models to real-time and batch endpoints |
| Implement generative AI quality assurance and observability | - Monitor latency, token usage, cost, and error rates - Evaluate generative AI outputs for quality, safety, and grounding - Conduct red teaming, adversarial testing, and content filtering - Implement logging, tracing, and telemetry for GenAI applications |
| Design and implement an MLOps infrastructure | - Implement security, governance, and compliance for MLOps - Manage environments, data stores, and model registries - Configure source control, CI/CD pipelines, and automation for ML workflows - Set up Azure Machine Learning workspace and compute targets |
| Optimize generative AI systems and model performance | - Implement cost management and scaling strategies for GenAI workloads - Optimize inference performance, caching, and throughput - Tune prompts, system messages, and grounding strategies - Fine-tune and distill models for specific use cases |
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NEW QUESTION # 13
You plan to filter your traces to identify issues while observing how the application is responding. The solution must not use an external knowledge base.
You need to select an evaluation metric.
Which built-in evaluator should you use?
Answer: A
Explanation:
A multi-turn chatbot application intermittently produces responses that are grammatically correct and on-topic but contradict earlier turns in the conversation, creating a confusing user experience. CoherenceEvaluator measures exactly this: whether the flow of ideas across a multi-turn conversation is logically consistent and non-contradictory without requiring an external knowledge base. RelevanceEvaluator (option A) measures whether responses are topically on-point but often requires a reference context or knowledge base.
SimilarityEvaluator (option B) requires a reference answer for comparison. QAEvaluator (option C) is a composite evaluator for question-answering tasks that requires a ground-truth context document.
CoherenceEvaluator is the only option that works purely from the conversation history itself with no external knowledge base, perfectly matching the stated constraint and the multi-turn chatbot use case.
Microsoft Learn Reference Topic: Evaluate conversational AI applications in Microsoft Foundry - CoherenceEvaluator for multi-turn chatbots
NEW QUESTION # 14
A team is experimenting with traditional models for a classification workflow in Azure Machine Learning.
The team requires a consistent way to manage assets that are created during experimentation.
You need to ensure that artifacts can be reused and governed across projects.
Which asset should you register?
Answer: A
Explanation:
During experimentation, a data science team produces trained model artifacts they want to share with the MLOps team for deployment, with the governance team for compliance, and with the QA team for testing.
Registering the model in the Azure ML model registry gives each of these teams access to the versioned, immutable artifact through a single reference. Option B (Component) packages a reusable pipeline step, not the model output itself. Option C (Environment) captures the Python runtime, not the trained artifact. Option D (Pipeline) defines the orchestration workflow, not the resulting model. In Azure ML, the model registry is the governance store for trained model artifacts, recording who created it, when, from what data and code, and which metrics it achieved - making it the asset to register for reuse and governance across projects.
Microsoft Learn Reference Topic: Register and manage models in Azure Machine Learning - Model registry
NEW QUESTION # 15
You must ensure full reproducibility of experiments including dataset, code, and environment across multiple runs and workspaces. Which combination of practices is MOST appropriate?
Answer: C
Explanation:
Reproducibility requires versioning all components: code, datasets, and environments. Missing any of these elements prevents exact replication of experiments. For example, the same code with different data or dependencies can produce different results, making debugging and auditing difficult.
NEW QUESTION # 16
Hotspot Question
A machine learning model is deployed to production in Azure Machine Learning and is actively serving predictions for a business application. The model was trained by using a historical dataset that represented expected input patterns at the time of deployment.
The team working on the model must ensure the following:
- Changes in input data distribution are detected.
- Appropriate actions are triggered when predefined thresholds are
exceeded.
You need to configure monitoring to meet the requirements.
Which configuration should you use for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 17
You manage an Azure Machine Learning workspace. You design a training job that is configured with a serverless compute. The serverless compute must have a specific instance type and count You need to configure the serverless compute by using Azure Machine Learning Python SDK v2. What should you do?
Answer: D
NEW QUESTION # 18
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