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| Section | Objectives |
|---|---|
| Topic 1: 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 |
| Topic 2: Implement generative AI quality assurance and observability | - Implement logging, tracing, and telemetry for GenAI applications - Evaluate generative AI outputs for quality, safety, and grounding - Monitor latency, token usage, cost, and error rates - Conduct red teaming, adversarial testing, and content filtering |
| Topic 3: Implement machine learning model lifecycle and operations | - Deploy models to real-time and batch endpoints - Retrain, update, and manage model versions in production - Monitor model performance, data drift, and operational health - Train, register, and version models using Azure Machine Learning |
| Topic 4: 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 |
| Topic 5: Optimize generative AI systems and model performance | - Tune prompts, system messages, and grounding strategies - Implement cost management and scaling strategies for GenAI workloads - Optimize inference performance, caching, and throughput - Fine-tune and distill models for specific use cases |
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NEW QUESTION # 74
You are implementing hyperparameter tuning by using Bayesian sampling for an Azure ML Python SDK v2-based model training from a notebook. The notebook is in an Azure Machine Learning workspace. The notebook uses a training script that runs on a compute cluster with 20 nodes.
The code implements Bandit termination policy with slackjactor set to 0.2 and a sweep job with max_concurrent_trials set to 10.
You must increase effectiveness of the tuning process by improving sampling convergence.
You need to select which sampling convergence to use.
What should you select?
Answer: A
Explanation:
To improve the sampling convergence and effectiveness of your Bayesian hyperparameter sweep, you must decrease the value of max_concurrent_trials (for example, setting max_concurrent_trials = 4).
Decrease max_concurrent_trials
Bayesian sampling is a fundamentally sequential algorithm. It relies entirely on the outcomes of previously completed trials to calculate and select the next optimized set of hyperparameters.
The Problem: Setting max_concurrent_trials = 10 forces the optimization engine to kick off 10 trials at the exact same time. Because none of these 10 overlapping trials have completed or generated metrics yet, they cannot learn from one another. This high parallelism compromises the intelligence of the algorithm, turning your structured Bayesian sweep into a less effective random sampling process.
The Solution: Reducing max_concurrent_trials to a smaller number (such as 4) allows sets of trials to complete sequentially. This ensures the optimizer captures enough historical performance data to intelligently direct subsequent runs, achieving significantly better convergence.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
NEW QUESTION # 75
You are planning to register a trained model in an Azure Machine Learning workspace.
You must store additional metadata about the model in a key-value format. You must be able to add new metadata and modify or delete metadata after creation.
You need to register the model.
Which parameter should you use?
Answer: B
Explanation:
azureml.core.Model.properties:
Dictionary of key value properties for the Model. These properties cannot be changed after registration, however new key value pairs can be added.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model
NEW QUESTION # 76
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 # 77
-
You complete the fine-tuning of a generative model in Microsoft Foundry. The fine-tuned model now appears as a new model variant in your development environment.
The deployment process must ensure that proper validation and control is maintained.
You need to promote the fine-tuned model from development to production.
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:
* Create a Developer Tier deployment.
* Evaluate and validate the model.
* Create a Global Standard deployment.
The fine-tuned model should first be deployed using the Developer Tier because Microsoft specifically positions this deployment type for fine-tuned model candidate evaluation rather than production use .
Developer Tier deployments provide a cost-efficient environment for testing custom models and do not provide a production SLA.
After deployment, the model should be evaluated and validated . Microsoft Foundry supports running the model through the playground and the Evaluations service to assess the deployed candidate against expected quality, safety, and task-performance criteria. Microsoft explicitly documents that evaluations can be executed against a deployed fine-tuned model candidate and compared with other model versions.
Only after the candidate satisfies the required validation criteria should it be promoted to a Global Standard deployment for production inference. Global Standard is designed for general production workloads and provides substantially more appropriate production characteristics than Developer Tier.
Register the fine-tuned model version is the distractor here. The scenario already states that fine-tuning is complete and the resulting model variant exists in Microsoft Foundry; the required promotion workflow is therefore test deployment # validation # production deployment .
Study Guide Reference: Implement machine learning model lifecycle and operations - fine-tuned model evaluation, controlled promotion, Developer Tier deployments, production deployment, and model lifecycle governance.
NEW QUESTION # 78
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 # 79
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