AI-300權威認證 - AI-300熱門題庫

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Microsoft AI-300 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Design and implement a GenAIOps infrastructure	20–25%	- Set up Microsoft Foundry environment 1. Manage compute and deployment resources 2. Configure projects, connections, and security - Implement infrastructure for generative AI workloads 1. Integrate with Azure services and tools 2. Design scalable and secure architecture
Topic 2: Implement machine learning model lifecycle and operations	25–30%	- Monitor and maintain models in production 1. Implement retraining and update workflows 2. Monitor data and model drift - Deploy models to production 1. Deploy to real-time and batch endpoints 2. Configure deployment options and scaling - Register, version, and package models 1. Manage model registry 2. Create reusable model packages - Orchestrate model training and experimentation 1. Create and manage pipelines 2. Track experiments and metrics
Topic 3: Design and implement an MLOps infrastructure	15–20%	- Implement infrastructure as code for Machine Learning 1. Automate infrastructure provisioning 2. Use Bicep or Azure CLI to deploy resources - Create and manage Machine Learning workspace resources and assets 1. Configure workspace settings and security 2. Manage compute targets, datastores, and environments
Topic 4: Optimize generative AI systems and model performance	15–20%	- Optimize model selection and configuration 1. Choose appropriate models and parameters 2. Tune prompts and generation settings - Improve efficiency and cost-effectiveness 1. Manage resource utilization 2. Optimize inference and deployment
Topic 5: Implement generative AI quality assurance and observability	10–15%	- Monitor generative AI systems 1. Track usage, performance, and errors 2. Implement logging and alerting - Evaluate and test generative AI applications 1. Define evaluation metrics and criteria 2. Test for safety, accuracy, and relevance

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Microsoft AI-300熱門題庫，AI-300題庫

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最新的 Microsoft Certified AI-300 免費考試真題 (Q129-Q134):

問題 #129
Drag and Drop Question
A data science team plans to evaluate multiple algorithms and hyperparameters for a classification problem without manually authoring separate training scripts.
The team must run an automated machine learning (AutoML) job that compares models and identifies the best-performing configuration.
You need to configure and run an AutoML training job.
Which four 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.

答案：

解題說明：

問題 #130
You manage an Azure Machine learning workspace.
You build a custom model you must log with Mlftow. The custom model includes the following:
* The model is not natively supported by Mlflow.
* The model cannot be serialized in Pickle format.
* The model source code is complex.
* The Python library tor the model must be packaged with the model.
You need to create a custom model flavor to enable logging with ML. flow.
What should you use?

A. artifacts
B. custom signatures
C. model loader
D. model wrapper

答案：C

問題 #131
A team is developing a generative AI assistant. The team is experimenting with multiple prompt variants to improve the user experience.
When comparing prompt variants, the team plans to assess whether the generated responses are grammatically correct.
You need to evaluate the quality of the language from the generated responses.
Which evaluator should you use?

A. Coherence
B. Fluency
C. Grounded ness
D. Textual similarity

答案：B

問題 #132
You deploy a new model version to a managed online endpoint. You must test it with 10% traffic and automatically roll back if latency or error rate increases beyond threshold. What should you configure?

A. Manual testing workflow
B. Batch endpoint validation
C. Separate endpoint for testing
D. Traffic splitting with monitoring alerts

答案：D

解題說明：
Traffic splitting enables controlled rollout of a new model version by directing a percentage of requests to it. Combined with monitoring alerts, it supports automated rollback when performance degrades. Separate endpoints lack built-in traffic management and do not provide seamless or automated rollback capabilities.

問題 #133
A team schedules weekly retraining of a model using Azure ML pipelines. They also want retraining triggered automatically when production data significantly deviates from training data distribution, without duplicating pipeline logic. What should they implement?

A. Azure Function to retrain model manually
B. One pipeline triggered by schedule and data drift alerts
C. Two independent pipelines with shared scripts
D. Notebook-based retraining process

答案：B

解題說明：
Using a single pipeline triggered by both a schedule and data drift alerts ensures consistent retraining logic and avoids duplication. This approach minimizes operational overhead and maintenance complexity. Creating multiple pipelines can lead to inconsistencies, duplicated code, and increased effort when updating retraining logic or dependencies.

問題 #134
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