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Microsoft AI-300 Exam Syllabus Topics:

Section	Objectives
Implement secure and scalable AI systems	- Scalability and performance optimization 1. Autoscaling AI workloads 2. Cost optimization strategies - Security and governance 1. Identity and access management for AI services 2. Data privacy and compliance considerations
Plan and design AI solutions using Azure AI services	- Requirements gathering and solution architecture 1. Identify business requirements for AI solutions 2. Select appropriate Azure AI services - Responsible AI design 1. Responsible AI mitigation strategies 2. Fairness, transparency, and accountability considerations
Operationalizing machine learning solutions	- Deployment and monitoring 1. Deploy models to endpoints 2. Monitor performance and drift - ML lifecycle management 1. Model training and evaluation in Azure Machine Learning 2. Model versioning and registry usage
Design and implement generative AI solutions	- Large language model integration 1. Prompt engineering and prompt flow design 2. Use Azure OpenAI Service capabilities - RAG (Retrieval Augmented Generation) solutions 1. Vector search integration 2. Knowledge grounding and retrieval design

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Microsoft Operationalizing Machine Learning and Generative AI Solutions AI-300 Prüfungsfragen mit Lösungen (Q12-Q17):

12. Frage
You have an Azure subscription named Sub1 that contains an Azure Machine Learning workspace named Workspace1. Workspace1 contains the following assets:
- a registered MLflow model named Model1
- an online endpoint named Endpoint1
Outbound network connectivity from Endpoint1 is blocked.
You need to deploy Model1 to Endpoint1.
What should you do first?

A. In Sub1, create an Azure Machine Learning registry.
B. In Sub1, create a private endpoint.
C. In Workspace1, create a linked service.
D. In Workspace1, create a package.

Antwort: D

Begründung:
To successfully deploy the registered MLflow model to an online endpoint that lacks outbound internet connectivity, you must use model packaging to build a deployment package before deploying.
By default, Azure Machine Learning deploys MLflow models using a "no-code deployment" approach, which requires an outbound internet connection during container runtime to dynamically download and install Python dependencies listed in the model's conda.yaml file.
Because outbound connectivity is blocked, this process fails. Packaging the model bundles the model binaries, dependencies, and environment ahead of time, entirely removing the requirement for runtime internet access.
Reference:
https://docs.azure.cn/en-us/machine-learning/how-to-deploy-mlflow-models-online-endpoints

13. Frage
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 data asset in the workspace.
Does the solution meet the goal?

A. Yes
B. No

Antwort: B

14. Frage
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. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
You manage an Azure Machine Learning workspace. The Python script named script.py reads an argument named training_data. The training_data argument specifies the path to the training data in a file named dataset1.csv.
You plan to run the script.py Python script as a command job that trains a machine learning model.
You need to provide the command to pass the path for the dataset as a parameter value when you submit the script as a training job.
Solution: python script.py dataset1.csv
Does the solution meet the goal?

A. Yes
B. No

Antwort: B

Begründung:
Correct:
* python script.py --training_data ${{inputs.training_data}}
The scipt is named script.py.
For the parameter use ${{inputs.training_data}}
Incorrect:
* python script.py --training_data dataset1.csv
* python script.py dataset1.csv
* python train.py --training_data training_data
Note: Read a TabularDataset, Example
In the Input object, specify the type as AssetTypes.MLTABLE, and mode as InputOutputModes.DIRECT:
* Details omitted*
job = command(
code="./src", # Local path where the code is stored
*-> command="python train.py --inputs ${{inputs.input_data}}",
inputs=my_job_inputs,
environment="<environment_name>:<version>",
compute="cpu-cluster",
)
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-read-write-data-v2

15. Frage
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. Grounded ness
C. Fluency
D. Textual similarity

Antwort: C

16. Frage
You want to ensure ML pipelines produce identical results when executed in different regions or workspaces. Which factor is MOST critical to control for reproducibility?

A. Versioned datasets and environments
B. Same dataset name
C. Same user identity
D. Same compute SKU

Antwort: A

Begründung:
Reproducibility across environments depends on consistent versioning of datasets and environments. Differences in data or dependencies can lead to inconsistent results even if compute resources are identical. Proper asset versioning ensures experiments can be reliably reproduced in any workspace or region.

17. Frage
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