IT업계에 종사하고 계신 분은Microsoft AI-300 시험을 패스하여 자격증을 취득하려고 검색하다 저희 블로그를 보게 되시고 저희 사이트까지 방문하게 될것입니다. 방문하는 순간 Microsoft AI-300시험에 대한 두려움이 사라질것입니다. 완벽한 구매후 서비스까지 겸비하고 있어 자격증을 취득하는데서의 믿음직스러운 동반자로 되어드릴게요.
| Section | Objectives |
|---|---|
| Topic 1: Optimize generative AI systems and model performance | - Optimize inference performance, caching, and throughput - Implement cost management and scaling strategies for GenAI workloads - Tune prompts, system messages, and grounding strategies - Fine-tune and distill models for specific use cases |
| Topic 2: Implement generative AI quality assurance and observability | - Monitor latency, token usage, cost, and error rates - Conduct red teaming, adversarial testing, and content filtering - Implement logging, tracing, and telemetry for GenAI applications - Evaluate generative AI outputs for quality, safety, and grounding |
| Topic 3: Implement machine learning model lifecycle and operations | - Train, register, and version models using Azure Machine Learning - Retrain, update, and manage model versions in production - Monitor model performance, data drift, and operational health - Deploy models to real-time and batch endpoints |
| Topic 4: 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 - Configure prompt orchestration, prompt flows, and agent frameworks - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search |
| Topic 5: Design and implement an MLOps infrastructure | - Implement security, governance, and compliance for MLOps - Manage environments, data stores, and model registries - Set up Azure Machine Learning workspace and compute targets - Configure source control, CI/CD pipelines, and automation for ML workflows |
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질문 # 169
A real-time endpoint experiences sporadic latency spikes. Investigation reveals instances scale down to zero during inactivity. You need to reduce latency without significantly increasing cost.
What should you configure?
정답：A
질문 # 170
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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.
정답：
설명：
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.
질문 # 171
You create an Azure Machine Learning workspace
You are developing a Python SDK v2 notebook to perform custom model training in the workspace. The notebook code imports all required packages.
You need to complete the Python SDK v2 code to include a training script. environment, and compute information.
How should you complete ten code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point
정답：
설명：
Explanation:
질문 # 172
You need to recommend an experiment-tracking strategy that ensures consistent experiment results.
What should you recommend?
정답：C
설명：
MLflow is the industry-standard open-source platform for experiment tracking, and Azure Machine Learning has first-class native integration with it. When you use MLflow within an Azure ML job, parameters, metrics, and artifacts are automatically logged to the run history of the AML workspace, making every run reproducible and comparable. Option A (AML job output logs) only captures console output and lacks structured parameter and metric logging. Option C (Application Insights logs) is designed for application- level telemetry, not ML experiment metadata. Option D (Azure Monitor alerts) is a reactive notification tool, not a tracking system. MLflow ' s autologging capability means that for common frameworks such as scikit- learn, XGBoost, and PyTorch, parameters and metrics are captured without a single line of custom code, directly answering Fabrikam ' s requirement for consistent experiment tracking.
Microsoft Learn Reference Topic: Track ML experiments with MLflow - Azure Machine Learning MLflow integration
질문 # 173
You retrain an existing model.
You need to register the new version of a model while keeping the current version of the model in the registry.
What should you do?
정답：B
설명：
Model version: A version of a registered model. When a new model is added to the Model Registry, it is added as Version 1. Each model registered to the same model name increments the version number.
Reference:
https://docs.microsoft.com/en-us/azure/databricks/applications/mlflow/model-registry
질문 # 174
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