Itcertkr는 Microsoft인증관련덤프를 제공하는 최고의 업체입니다, 덤프들은 Itcertkr의 베터랑의 전문가들이 오랜 풍부한 경험과 AI-300지식으로 만들어낸 최고의 제품입니다. 그리고 우리는 온라인무료 서비스도 제공되어 제일 빠른 시간에 소통 상담이 가능합니다.
| Section | Objectives |
|---|---|
| Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Implement secure and scalable AI systems | - Security and governance
|
| Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
| Operationalizing machine learning solutions | - ML lifecycle management
|
Itcertkr에서 출시한 Microsoft인증AI-300 덤프는 시험문제점유율이 가장 높은 시험대비자료입니다. 실제Microsoft인증AI-300시험문제유형과 같은 형식으로 제작된Microsoft인증AI-300 시험공부자료로서Itcertkr덤프의 실용가치를 자랑하고 있습니다.덤프를 공부하여 시험불합격하시면 덤프비용은 환불처리해드립니다.
질문 # 152
You have an Azure Machine Learning workspace that includes an AmICompute cluster and a batch endpoint.
You clone a repository that contains an MLflow model to your local computer. You need to ensure that you can deploy the model to the batch endpoint.
Solution: Create a datastore in the workspace.
Does the solution meet the goal?
정답：A
질문 # 153
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.
정답：
설명：
질문 # 154
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?
정답：D
설명：
The question ' s constraint is explicit: no retraining. This immediately eliminates ExponentiatedGradient (option A), which is a reduction-based in-processing algorithm that requires retraining as part of the fairness- aware optimization process. GridSearch (option C) systematically trains multiple models across a grid of fairness constraints, again requiring training. CorrelationRemover (option B) is a pre-processing technique that transforms training features before training, also requiring a new training run. ThresholdOptimizer (option D) is a post-processing algorithm: it takes an already-trained model and optimizes its classification thresholds independently for different sensitive-attribute groups to achieve a fairness constraint such as equalized odds or demographic parity, without touching the model weights. This is the only Fairlearn approach that operates purely at inference time on an existing model.
Microsoft Learn Reference Topic: Mitigate unfairness in machine learning models with Fairlearn - Post- processing with ThresholdOptimizer
질문 # 155
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 have an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.
You plan to add a new Jupyter kernel that will be accessible from the same terminal session.
You need to perform the task that must be completed before you can add the new kernel.
Solution: Delete the Python 3.8 - AzureML kernel.
Does the solution meet the goal?
정답：A
설명：
No. Deleting an existing Azure Machine Learning kernel is neither required nor appropriate before adding another Jupyter kernel. Azure Machine Learning compute instances are explicitly designed to support multiple kernels simultaneously . Microsoft states that notebooks automatically discover the Jupyter kernels installed on the connected compute instance, allowing users to switch between them from the kernel selector.
To add a kernel, the supported process is to create a new Conda environment, activate it, install pip and ipykernel, and then register that environment by using python -m ipykernel install --user. Existing kernels do not need to be removed.
More importantly, Microsoft warns against deleting Conda environments or Jupyter kernels that you did not create , because doing so can damage Jupyter or JupyterLab functionality on the Azure Machine Learning compute instance. This makes removal of a built-in AzureML kernel particularly inappropriate as a prerequisite for creating another kernel.
The correct approach is therefore to leave the existing kernel intact and create a separate environment/kernel for the new workload.
Study Guide Reference: Design and implement an MLOps infrastructure - Azure Machine Learning notebook environments, kernel management, compute instances, and dependency isolation.
질문 # 156
A team trains an MLflow model that scores customer churn risk. The model will be consumed by different downstream systems.
One system requests predictions synchronously during customer interactions.
Another system submits files containing millions of records for scheduled scoring.
You need to deploy the model by using managed inference options that match each usage pattern.
Which option should you use for each usage pattern? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
A system requesting predictions synchronously during customer interactions needs sub-second responses, while a system submitting files with millions of records can tolerate minutes of processing time. For real-time synchronous serving, a Managed Online Endpoint provisions a persistent always-on container behind an HTTPS REST endpoint that returns predictions within milliseconds. For large-batch asynchronous scoring, a Batch Endpoint accepts a data asset input, distributes scoring across a compute cluster, and writes results back to storage. Online endpoints support auto-scaling based on request volume and traffic splitting. Batch endpoints are invoked on-demand or on a schedule, automatically provisioning and de-provisioning compute, keeping costs low for intermittent large jobs. Each deployment type is purpose-built for its usage pattern and should not be swapped.
Microsoft Learn Reference Topic: Deploy and score models with managed online endpoints and batch endpoints - Azure Machine Learning
질문 # 157
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연구결과에 의하면Microsoft인증 AI-300시험은 너무 어려워 시험패스율이 낮다고 합니다. Itcertkr의 Microsoft인증 AI-300덤프와 만나면Microsoft인증 AI-300시험에 두려움을 느끼지 않으셔도 됩니다. Itcertkr의 Microsoft인증 AI-300덤프는 엘리트한 IT전문가들이 실제시험을 연구하여 정리해둔 퍼펙트한 시험대비 공부자료입니다. 저희 덤프만 공부하시면 시간도 절약하고 가격도 친근하며 시험준비로 인한 여러방면의 스트레스를 적게 받아Microsoft인증 AI-300시험패스가 한결 쉬워집니다.
AI-300자격증참고서: https://www.itcertkr.com/AI-300_exam.html