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Microsoft AI-300 Exam Syllabus Topics:

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

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AI-300 German, AI-300 Antworten

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Microsoft Operationalizing Machine Learning and Generative AI Solutions AI-300 Prüfungsfragen mit Lösungen (Q58-Q63):

58. Frage
Hotspot Question
A team is building a generative AI agent by using Retrieval-Augmented Generation (RAG) in Microsoft Foundry.
The team frequently updates prompt content. The team must be able to track changes across contributors while avoiding full application redeployments.
You need to enable rapid prompt iteration with traceability. Applications consuming the agent must be able to use updated prompts without requiring redeployment.
What should you configure for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Antwort:

Begründung:

59. Frage
You manage an Azure Machine Learning workspace.
You schedule a pipeline job by using Azure Machine Learning Python SDK v2.
You need to decide whether the time-based schedule with cron expression is implemented correctly.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Antwort:

Begründung:

Explanation:

60. Frage
You run Azure Machine Learning training experiments. The training scripts directory contains 100 files that includes a file named .amlignore. The directory also contains subdirectories named ./outputs and ./logs.
There are 20 files in the training scripts directory that must be excluded from the snapshot to the compute targets. You create a file named .gitignore in the root of the directory. You add the names of the 20 files to the .gitignore file. These 20 files continue to be copied to the compute targets.
You need to exclude the 20 files.
What should you do?

A. Copy the contents of the file named .gitignore to the file named .amlignore.
B. Add the contents of the file named .amlignore to the file named .gitignore.
C. Move the file named .gitignore to the ./logs directory.
D. Move the file named .gitignore to the ./outputs directory.

Antwort: A

Begründung:
To exclude the 20 files from the training snapshot, you must add the file names to the .amlignore file located in the root of your training scripts directory.
Why the Files Are Still Copying
Azure Machine Learning training experiments use a specific order of precedence when creating a directory snapshot for compute targets:
.amlignore takes absolute priority: If an .amlignore file exists in the directory, Azure ML only respects the rules inside it. It completely ignores any .gitignore file present.
.gitignore is a fallback: Azure ML only respects .gitignore rules if an .amlignore file does not exist in the directory. Because you have both files, the .gitignore file is being completely bypassed.
Reference:
https://learn.microsoft.com/en-us/python/api/azureml-core/azureml.core.experiment.experiment

61. Frage
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2. You create a General Purpose v2 Azure storage account named mlstorage1. The storage account includes a publicly accessible container named mlcontainer1. The container stores 10 blobs with files in the CSV format.
You must develop Python SDK v2 code to create a data asset referencing all blobs in the container named mlcontainer1.
You need to complete the Python SDK v2 code.
How should you complete the code? To answer, select the appropriate options in the answer area. NOTE:
Each correct selection is worth one point.

Antwort:

Begründung:

Explanation:
Because you want to reference all blobs in a container rather than a single file, the correct asset type is AssetTypes.URI_FOLDER. This type points to a directory-level URI, allowing Azure ML to traverse all files within it. For a publicly accessible Azure Blob Storage container, the URI follows the pattern
https://storage_account.blob.core.windows.net/container_name/. You then create the Data object with the correct type, path, name, and version parameters, and register it in the workspace using ml_client.data.
create_or_update. Do not use AssetTypes.URI_FILE, which references a single file. Do not use AssetTypes.
MLTABLE unless you have an MLTable YAML descriptor. URI_FOLDER is the correct choice for referencing a collection of CSV blobs in an Azure Blob Storage container, allowing Azure ML to discover and process all files within the specified path.
Microsoft Learn Reference Topic: Create and manage data assets in Azure Machine Learning Python SDK v2
- URI_FOLDER

62. Frage
You manage an Azure Machine Learning workspace.
You must define the execution environments for your jobs and encapsulate the dependencies for your code.
You need to configure the environment from a Docker build context.
How should you complete the rode segment? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.

Antwort:

Begründung:

Explanation:

63. Frage
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