Microsoft AI-300 : Operationalizing Machine Learning and Generative AI Solutions

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Microsoft AI-300 Exam Syllabus Topics:

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. You are training machine learning models in Azure Machine Learning. You use Hyperdrive to tune the hyperparameters.
In previous model training and tuning runs, many models showed similar performance.
You need to select an early termination policy that meets the following requirements:
- accounts for the performance of all previous runs when evaluating the current run
- avoids comparing the current run with only the best performing run to date Which two early termination policies should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A) Median stopping
B) Bandit
C) Default
D) Truncation selection

2. A team develops and manages a conversational assistant by using Microsoft Foundry.
The team must be able to validate that the assistant does not produce hateful responses before the application is exposed to any users.
You need to evaluate the model output for hateful responses as part of a repeatable validation process.
Which evaluator should you configure first?

A) Indirect attacks
B) Content safety
C) Protected material
D) Groundedness

3. Drag and Drop Question
A team runs training jobs by using multiple Azure Machine Learning pipelines.
The team must ensure that all runs use the same Python packages and system libraries. The solution must allow dependency updates to be versioned without modifying training code.
You need to configure the workspace so that runtime dependencies are consistent and reusable.
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.

4. A real-time endpoint experiences sporadic latency spikes. Investigation reveals instances scale down to zero during inactivity. You need to reduce latency without significantly increasing cost.
What should you configure?

A) Increase timeout settings
B) Set minimum instance count
C) Reduce logging
D) Switch to batch endpoint

5. You are designing a new machine learning solution to predict customer churn by using Azure Machine Learning. You have raw data in CSV format stored in Azure Data Lake.
You need to design the solution so that it can efficiently handle large-scale model training and iterative development.
Which two actions should you perform? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.

A) Convert the data into the JSONL format and upload into Blob Storage.
B) Configure an Azure Machine Learning compute instance for model training.
C) Register the data as a tabular dataset in the Azure Machine Learning workspace.
D) Schedule training using an Azure Data Factory pipeline.
E) Configure an Azure Machine Learning compute cluster for model training.

Solutions: