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| Section | Objectives |
|---|---|
| Topic 1: Implement generative AI quality assurance and observability | - Monitor latency, token usage, cost, and error rates - Implement logging, tracing, and telemetry for GenAI applications - Conduct red teaming, adversarial testing, and content filtering - Evaluate generative AI outputs for quality, safety, and grounding |
| Topic 2: Design and implement a GenAIOps infrastructure | - 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 RAG (Retrieval-Augmented Generation) pipelines and vector search |
| Topic 3: 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 4: 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 5: Optimize generative AI systems and model performance | - Fine-tune and distill models for specific use cases - Tune prompts, system messages, and grounding strategies - Optimize inference performance, caching, and throughput - Implement cost management and scaling strategies for GenAI workloads |
ご存知のように、すべての受験者は、知識とスキルを示すための最良の証拠となる関連するMicrosoftのAI-300認定を取得する場合、試験に合格する必要があります。 準備プロセスを簡素化する場合は、良いニュースがあります。 AI-300試験問題は、多くの国のすべてのCertJukenお客様から高く評価されており、当社はこの分野のリーダーになっています。 AI-300試験問題は、AI-300試験に合格するために非常に正確です。 AI-300実践ガイドを購入すると、高いOperationalizing Machine Learning and Generative AI Solutions合格率が得られます。
質問 # 46
A team is working in Microsoft Foundry to test and compare large language model (LLM) prompt variants in a development environment.
The team requires consistent inputs to evaluate prompt variants without relying on live user traffic.
You need to create a controlled evaluation of input data.
Which action should you perform first?
正解：B
質問 # 47
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.
正解：
解説：
Explanation:
Azure Machine Learning ' s data drift monitor solves the first requirement: it continuously compares the statistical distribution of live inference input features against the baseline training data distribution, flagging when significant drift occurs. For the second requirement of triggering appropriate actions when thresholds are exceeded, Azure Monitor alert rules are configured on the drift metrics. When the drift coefficient exceeds a defined threshold, Azure Monitor fires an alert that can invoke Logic Apps, send emails, trigger an Azure ML retraining pipeline, or post to Teams. This two-layer approach - ML-specific drift detection backed by Azure Monitor alerting - is the Microsoft-recommended pattern for production model monitoring. The data drift monitor handles detection, while Azure Monitor handles the operational response, keeping the two concerns cleanly separated.
Microsoft Learn Reference Topic: Monitor model data drift - Azure Machine Learning model monitoring and Azure Monitor integration
質問 # 48
You are preparing to build a deep learning convolutional neural network model for image classification. You create a script to train the model using CUDA devices.
You must submit an experiment that runs this script in the Azure Machine Learning workspace.
The following compute resources are available:
a Microsoft Surface device on which Microsoft Office has been installed. Corporate IT policies prevent the installation of additional software a Compute Instance named ds-workstation in the workspace with 2 CPUs and 8 GB of memory an Azure Machine Learning compute target named cpu-cluster with eight CPU-based nodes an Azure Machine Learning compute target named gpu-cluster with four CPU and GPU-based nodes You need to specify the compute resources to be used for running the code to submit the experiment, and for running the script in order to minimize model training time.
Which resources should the data scientist use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
質問 # 49
A team uses a hosted Git repository to store training code and pipeline definitions of a machine learning experiment.
The team must ensure that access to the repository is granted without requiring each developer to store personal access tokens on their machines.
Repository access must be secure and centrally managed to reduce credential spread.
You need to enable secure access between an Azure Machine Learning workspace and the repository.
What should you do?
正解：C
解説：
Configuring the workspace to use a managed identity (or a centralized service principal/workspace identity) is the correct approach to securely and centrally manage repository access without spreading personal access tokens.
No Developer Credentials: Developers do not need to generate, share, or store personal access tokens (PATs) on their local machines.
Centralized Management: Access permissions are bound to the cloud workspace resource rather than individual user accounts.
Reduced Credential Spreading: Eliminates the risk of leaked tokens when team members leave or compromise local workstations.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-identity-based-service-authentication
質問 # 50
You create an Azure Machine Learning workspace.
You must create a custom role named DataScientist that meets the following requirements:
Role members must not be able to delete the workspace.
Role members must not be able to create, update, or delete compute resource in the workspace.
Role members must not be able to add new users to the workspace.
You need to create a JSON file for the DataScientist role in the Azure Machine Learning workspace.
The custom role must enforce the restrictions specified by the IT Operations team.
Which JSON code segment should you use?
正解：C
解説：
The following custom role can do everything in the workspace except for the following actions:
It can ' t create or update a compute resource.
It can ' t delete a compute resource.
It can ' t add, delete, or alter role assignments.
It can ' t delete the workspace.
To create a custom role, first construct a role definition JSON file that specifies the permission and scope for the role. The following example defines a custom role named " Data Scientist Custom " scoped at a specific workspace level:
data_scientist_custom_role.json :
{
" Name " : " Data Scientist Custom " ,
" IsCustom " : true,
" Description " : " Can run experiment but can ' t create or delete compute. " ,
" Actions " : [ " * " ],
" NotActions " : [
" Microsoft.MachineLearningServices/workspaces/*/delete " ,
" Microsoft.MachineLearningServices/workspaces/write " ,
" Microsoft.MachineLearningServices/workspaces/computes/*/write " ,
" Microsoft.MachineLearningServices/workspaces/computes/*/delete " ,
" Microsoft.Authorization/*/write "
],
" AssignableScopes " : [
" /subscriptions/ < subscription_id > /resourceGroups/ < resource_group_name > /providers/Microsoft.
MachineLearningServices/workspaces/ < workspace_name > "
]
}
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-assign-roles
質問 # 51
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AI-300有用なテストガイド資料は、最も重要な情報を最も簡単な方法でクライアントに提示するので、AI-300有用なテストガイドを学習するための時間とエネルギーはほとんど必要ありません。クライアントは、テストの学習と準備に20〜30時間しかかかりません。仕事や学習などで忙しい人にとっては、これは良いニュースです。なぜなら、テストの準備に十分な時間がないことを心配する必要がなく、主なことをゆっくりとできるからです。 AI-300学習実践ガイドをご覧ください。ですから、AI-300試験の教材の大きな利点であり、クライアントにとって非常に便利です。
AI-300合格受験記: https://www.certjuken.com/AI-300-exam.html