Microsoft Operationalizing Machine Learning and Generative AI Solutions - AI-300 FREE EXAM DUMPS QUESTIONS & ANSWERS
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.
The default datastore of workspace1 contains a folder named sample_data.
The folder structure contains the following content:
You write Python SDK v2 code to materialize the data from the files in the sample_data folder into a Pandas data frame.
You need to complete the Python SDK v2 code to use the MLTable folder as the materialization blueprint.
How should you complete the code? To answer, select the appropriate options in the answer area. NOTE:
Each correct selection is worth one point
The default datastore of workspace1 contains a folder named sample_data.
The folder structure contains the following content:
You write Python SDK v2 code to materialize the data from the files in the sample_data folder into a Pandas data frame.
You need to complete the Python SDK v2 code to use the MLTable folder as the materialization blueprint.
How should you complete the code? To answer, select the appropriate options in the answer area. NOTE:
Each correct selection is worth one point
Correct Answer:
Explanation:
The MLTable format in Azure Machine Learning Python SDK v2 is a structured abstraction over tabular data sources. It stores a YAML file alongside data files, describing how to read and transform the data. To materialize this into a Pandas DataFrame, you use the mltable library: first call mltable.load with the path to the MLTable folder to load the definition, then call to_pandas_dataframe() on the returned object. The path parameter should point to the folder containing the MLTable YAML file on the default datastore. MLTable abstracts the storage location and transformation steps, making the code storage-agnostic - the same code works whether data lives in Blob Storage, ADLS Gen2, or a local path. The SDK v2 approach with MLTable is the recommended pattern for governed, reusable data access in Azure Machine Learning.
Microsoft Learn Reference Topic: Create and use MLTable data assets in Azure Machine Learning Python SDK v2
You have an Azure Machine Learning workspace. You are running an experiment on your local computer.
You need to use MLflow Tracking to store metrics and artifacts from your local experiment runs in the workspace.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.
You need to use MLflow Tracking to store metrics and artifacts from your local experiment runs in the workspace.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.
Correct Answer:
Explanation:
A product team is building a customer support assistant that must respond consistently across multiple channels.
Early testing shows that small wording changes in prompts cause large differences in tone and factual accuracy.
The team needs prompts that are reliable, reusable, and adaptable across multiple use cases without retraining the underlying model.
You need to design prompts that improve response quality while remaining flexible for future changes.
Which two actions should you perform? Each correct answer presents part of the solution. (Choose two.)
Early testing shows that small wording changes in prompts cause large differences in tone and factual accuracy.
The team needs prompts that are reliable, reusable, and adaptable across multiple use cases without retraining the underlying model.
You need to design prompts that improve response quality while remaining flexible for future changes.
Which two actions should you perform? Each correct answer presents part of the solution. (Choose two.)
Correct Answer: B,C
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You manage an Azure Machine Learning workspace. You use Azure Machine Learning Python SDK v2 to configure a trigger to schedule a pipeline job. You need to create a time-based schedule with recurrence pattern.
Which two properties must you use to successfully configure the trigger? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
Which two properties must you use to successfully configure the trigger? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
Correct Answer: B,C
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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?
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?
Correct Answer: C
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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?
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?
Correct Answer: A
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You manage an Azure Machine Learning workspace named workspace1.
You must register an Azure Blob storage datastore in workspace1 by using an access key. You develop Python SDK v2 code to import all modules required to register the datastore.
You need to complete the Python SDK v2 code to define the datastore.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
You must register an Azure Blob storage datastore in workspace1 by using an access key. You develop Python SDK v2 code to import all modules required to register the datastore.
You need to complete the Python SDK v2 code to define the datastore.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
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You use Azure Machine Learning to deploy a model as a real-time web service.
You need to create an entry script for the service that ensures that the model is loaded when the service starts and is used to score new data as it is received.
Which functions should you include in the script? To answer, drag the appropriate functions to the correct actions. Each function may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
You use Azure Machine Learning to deploy a model as a real-time web service.
You need to create an entry script for the service that ensures that the model is loaded when the service starts and is used to score new data as it is received.
Which functions should you include in the script? To answer, drag the appropriate functions to the correct actions. Each function may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Load the model when the service starts: init()
Use the model to score new data: run()
Azure Machine Learning scoring scripts for online endpoints use two required entry-point functions: init() and run() . Microsoft explicitly states that the scoring script specified for an online deployment must contain both functions.
The init() function is invoked when the inference container is initialized or started, typically immediately after a deployment is created or updated. It is intended for one-time initialization tasks such as locating the registered model through AZUREML_MODEL_DIR, deserializing the model, and storing it in memory.
Loading the model once during initialization avoids repeatedly loading it for every inference request, which reduces latency and processing overhead.
The run() function is called each time the endpoint receives an inference request. It accepts the incoming request data, transforms or parses the input as required, invokes the loaded model ' s prediction logic, and returns the scoring result. Microsoft describes run() as the function that performs the actual scoring or prediction for each endpoint invocation.
main(), score(), and predict() may exist inside application code or model libraries, but they are not the required Azure Machine Learning scoring-script entry points.
A company is creating an internal tool that summarizes long meeting transcripts and extracts action items.
The model must:
Process text inputs up to 200k tokens long.
Generate concise summaries in seconds.
Support interactive testing before integration into the app.
You need to select, deploy, and test a model that supports summarization with low latency.
How should you complete the configuration plan? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
The model must:
Process text inputs up to 200k tokens long.
Generate concise summaries in seconds.
Support interactive testing before integration into the app.
You need to select, deploy, and test a model that supports summarization with low latency.
How should you complete the configuration plan? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
For a tool that must process text inputs up to 200k tokens long, generate concise summaries in seconds, and support interactive testing, the configuration must address three requirements. The large token context window points to GPT-4o, which supports up to 128k tokens and is among the largest-context Azure OpenAI models available in Foundry, making it suitable for long documents such as meeting transcripts. Low latency with the need to generate responses in seconds rules out batch deployment types; Data Zone Standard provides the best latency for single-tenant enterprise use cases with zone-level routing. Interactive testing before integration points directly to Microsoft Foundry ' s built-in Chat Playground or Prompt Playground, where you can paste transcripts, adjust system prompts, and evaluate outputs interactively before writing any application integration code.
Microsoft Learn Reference Topic: Deploy and test models in Microsoft Foundry - Model selection for long- context summarization
You train and publish a machine teaming model.
You need to run a pipeline that retrains the model based on a trigger from an external system.
What should you configure?
You need to run a pipeline that retrains the model based on a trigger from an external system.
What should you configure?
Correct Answer: B
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You manage an Azure Machine Learning workspace.
You experiment with an MLflow model that trains interactively by using a notebook in the workspace. You need to log dictionary type artifacts of the experiments in Azure Machine Learning by using MLflow. Which syntax should you use?
You experiment with an MLflow model that trains interactively by using a notebook in the workspace. You need to log dictionary type artifacts of the experiments in Azure Machine Learning by using MLflow. Which syntax should you use?
Correct Answer: D
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