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| Section | Objectives |
|---|---|
| Optimize generative AI systems and model performance | - Implement cost management and scaling strategies for GenAI workloads - Optimize inference performance, caching, and throughput - Fine-tune and distill models for specific use cases - Tune prompts, system messages, and grounding strategies |
| 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 |
| Implement generative AI quality assurance and observability | - Implement logging, tracing, and telemetry for GenAI applications - 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 |
| Design and implement a GenAIOps infrastructure | - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Manage API keys, rate limits, and responsible AI guardrails - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Configure prompt orchestration, prompt flows, and agent frameworks |
| Implement machine learning model lifecycle and operations | - Monitor model performance, data drift, and operational health - Retrain, update, and manage model versions in production - Deploy models to real-time and batch endpoints - Train, register, and version models using Azure Machine Learning |
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NEW QUESTION # 26
A company requires that only models meeting predefined performance thresholds are registered and deployed. The solution must be fully automated within the ML workflow. What should you implement?
Answer: D
Explanation:
Conditional logic in pipelines allows automated decisions based on evaluation metrics, ensuring only models that meet performance thresholds are registered and deployed. This maintains full automation. Manual approval steps interrupt automation and are not scalable in continuous integration and deployment workflows.
NEW QUESTION # 27
A pipeline step fails intermittently due to transient compute issues. You need to improve reliability without modifying core logic or increasing cost significantly. What is the BEST approach?
Answer: C
Explanation:
Retry policies allow pipeline steps to automatically recover from transient failures, such as temporary compute or network issues. This improves reliability without modifying core logic or increasing infrastructure costs. Increasing compute resources does not address transient failure scenarios effectively.
NEW QUESTION # 28
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.
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: Configure Azure Monitor to collect logs from the workspace. Use the logs to perform prompt evaluation.
Does the solution meet the goal?
Answer: B
Explanation:
Azure Monitor is valuable for production observability , but it is not the appropriate mechanism for the stated development-time requirement: manually inspecting individual prompt-flow executions and comparing prompt variants with their inputs, outputs, token consumption, and latency.
Prompt flow provides purpose-built run outputs and tracing . Microsoft documents that after a flow execution, the Outputs experience exposes detailed flow inputs and outputs. When tracing is enabled, the Trace view provides execution duration and token information and lets the developer expand individual steps to inspect their inputs and execution details.
Prompt flow also directly supports variants and batch/evaluation runs. Multiple variants can be executed and compared, while the run details expose per-test-case inputs, outputs, token counts, duration, and evaluation results. Microsoft explicitly states that multiple runs can be selected to compare their metrics and outputs.
Azure Monitor/Application Insights can collect operational metrics such as token consumption and flow or node latency for deployed applications, but that telemetry is primarily intended for monitoring and troubleshooting rather than the interactive prompt-variant evaluation workflow described here.
Therefore, the proposed solution does not meet the goal .
Study Guide Reference: Implement generative AI quality assurance and observability - prompt evaluation, tracing, debugging, token consumption, latency, and prompt-variant comparison.
NEW QUESTION # 29
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 # 30
-
You review the following Azure CLI command and the relevant Bicep excerpt.
(Non-relevant sections are omitted.)
You need to validate what the snippet will do before it is merged. For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The command deploys the resources into an existing resource group named rg-foundry-dev: Yes The system-assigned managed identity defined in the template will automatically be inherited by all Microsoft Foundry projects: No To deploy this template to a different subscription, you must modify the Bicep file to include a subscriptionId parameter: No The first statement is Yes . The command uses az deployment group create --resource-group rg-foundry-dev, which performs an Azure Resource Manager deployment at resource-group scope . Microsoft documents that the target resource group must already exist; if it does not, it must be created before running the resource- group deployment.
The second statement is No . The Bicep declaration assigns a system-assigned managed identity to the Foundry resource itself . A Microsoft Foundry project is a child resource and can have its own managed identity . Microsoft explicitly shows project creation with " identity " : { " type " : " SystemAssigned " } and separately discusses assigning permissions to a project ' s managed identity. Therefore, the parent ' s system- assigned identity is not automatically inherited as the identity of every project.
The third statement is No . Because targetScope = ' resourceGroup ' , the same Bicep template can be deployed to a resource group in another subscription without adding a subscriptionId parameter. Azure CLI supports the global --subscription argument, or the active subscription can be changed with az account set.
NEW QUESTION # 31
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