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Microsoft AI-300 Exam Syllabus Topics:

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. A data science team plans to evaluate multiple hyperparameter values automatically while training a model in Azure Machine Learning.
The tuning process must run multiple training trials without manually modifying the training script for each run.
You need to automate hyperparameter tuning for the training job.
What should you do?

A) Adjust hyperparameters after model deployment.
B) Duplicate the training script for each parameter combination.
C) Manually change hyperparameter values between training runs.
D) Create a tuning job that runs multiple trials with different parameter values.

2. Hotspot Question
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.

3. You are implementing hyperparameter tuning by using Bayesian sampling for an Azure ML Python SDK v2-based model training from a notebook. The notebook is in an Azure Machine Learning workspace. The notebook uses a training script that runs on a compute cluster with 20 nodes.
The code implements Bandit termination policy with slackjactor set to 0.2 and a sweep job with max_concurrent_trials set to 10.
You must increase effectiveness of the tuning process by improving sampling convergence.
You need to select which sampling convergence to use.
What should you select?

A) Set the value of slack_factor of early_termination policy to 0.1.
B) Set the value of slack_factor of early_termination policy to 0.9.
C) Set the value of max_concurrent_trials to 4.
D) Set the value of max_concurrent_trials to 20.

4. 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.

5. A team develops and manages a conversational assistant by using Microsoft Foundry.
The team requires generative AI to automatically evaluate every pull request of an agentic application and fail the build if safety thresholds are exceeded.
You need to automate evaluations as part of CI.
What should you configure?

A) Blocklist applied to the model endpoint
B) Content filter configured in warning mode
C) GitHub Actions workflow that executes the runs
D) Retrieval chunking strategy

Solutions: