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| Section | Objectives |
|---|---|
| Topic 1: Implement machine learning model lifecycle and operations | - Monitor model performance, data drift, and operational health - Retrain, update, and manage model versions in production - Train, register, and version models using Azure Machine Learning - Deploy models to real-time and batch endpoints |
| Topic 2: Design and implement an MLOps infrastructure | - Set up Azure Machine Learning workspace and compute targets - Manage environments, data stores, and model registries - Implement security, governance, and compliance for MLOps - Configure source control, CI/CD pipelines, and automation for ML workflows |
| Topic 3: Design and implement a GenAIOps infrastructure | - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Manage API keys, rate limits, and responsible AI guardrails - Configure prompt orchestration, prompt flows, and agent frameworks |
| Topic 4: 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 |
| Topic 5: Implement generative AI quality assurance and observability | - Implement logging, tracing, and telemetry for GenAI applications - Monitor latency, token usage, cost, and error rates - Conduct red teaming, adversarial testing, and content filtering - Evaluate generative AI outputs for quality, safety, and grounding |
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NEW QUESTION # 30
You train and register an Azure Machine Learning model
You plan to deploy the model to an online endpoint
You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.
Solution:
Create a managed online endpoint and set the value of its auth.mode parameter to aml.token. Deploy the model to the online endpoint.
Does the solution meet the goal?
Answer: A
NEW QUESTION # 31
You are authoring a notebook in Azure Machine Learning studio.
You must install packages from the notebook into the currently running kernel. The installation must be limited to the currently running kernel only.
You need to install the packages.
Which magic function should you use?
Answer: A
Explanation:
To target and install Python packages into the currently running kernel only, you should use the %pip line magic function.
Kernel Isolation: Unlike using the shell command !pip install, which installs the package into the underlying compute instance's base environment, the %pip magic explicitly targets the specific Python executable assigned to your active notebook kernel.
No Restart Needed: It automatically syncs the newly installed packages with the current session so you can import them immediately without restarting the kernel.
Example Usage
Simply type and run the following python line in a notebook cell:
%pip install <package_name>
Reference:
https://learn.microsoft.com/en-us/answers/questions/1921271/i-have-a-new-compute-instance-that-has-python3-8-a
NEW QUESTION # 32
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: A
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 # 33
-
A customer-facing web application uses a foundational model deployed through Microsoft Foundry.
A new model version must be introduced and validated without disrupting production traffic.
You need to deploy the new version by using a safe promotion strategy.
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:
Correct sequence:
* Deploy a new model version.
* Validate the behavior of the new version.
* Update the model settings on the production route.
The safe approach is to deploy the new model side by side with the existing production deployment rather than modifying the currently serving model immediately. Microsoft Foundry documentation explains that a newly available model version can be tested through a separate deployment, allowing teams to evaluate behavioral differences without affecting existing production requests.
After deployment, validate the behavior of the new version against representative production workloads.
Validation should cover response quality, latency, errors, structured-output compatibility, tool-calling behavior, safety characteristics, and application-specific acceptance criteria. Microsoft ' s model-migration guidance explicitly places validation before production rollout and recommends proving that the new configuration meets defined quality requirements before broad exposure.
Once validation succeeds, update the model settings on the production route so that the application ' s existing production entry point directs requests to the validated deployment. This preserves the stable application-facing endpoint while changing the backend model in a controlled manner.
Shift 100% of traffic to the new version is not the appropriate intermediate safe-promotion action because immediately transferring all production traffic removes the staged validation safeguard. Creating another AI gateway route version is also unnecessary for the required three-step promotion sequence.
Study Guide Reference: Implement machine learning model lifecycle and operations - model versioning, side-by-side deployments, pre-production validation, controlled promotion, production routing, and rollback- safe model lifecycle management.
NEW QUESTION # 34
You create a binary classification model. You use the Fairlearn package to assess model fairness.
You must eliminate the need to retrain the model.
You need to implement the Fairlearn package.
Which algorithm should you use?
Answer: B
Explanation:
The best algorithm to implement within the Fairlearn package for a binary classification model without retraining is the ThresholdOptimizer.
Why ThresholdOptimizer?
This algorithm is a post-processing technique specifically designed to mitigate unfairness after a model has already been trained. By setting the prefit parameter to True, it treats your existing model as a "black box," adjusting its decision thresholds for different groups to satisfy fairness constraints (like demographic parity or equalized odds) without changing the underlying model weights.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml
NEW QUESTION # 35
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