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| Section | Objectives |
|---|---|
| Topic 1: Deploy and consume models | - Deploy models to endpoints - Monitor deployed models and endpoints |
| Topic 2: Train machine learning models | - Train models using Azure Machine Learning - Tune hyperparameters and evaluate models |
| Topic 3: Explore and analyze data | - Ingest and prepare data for modeling - Perform exploratory data analysis |
| Topic 4: Optimize and manage models | - Improve model performance - Track experiments and manage model lifecycle |
| Topic 5: Design and prepare a machine learning solution | - Select appropriate Azure services for machine learning workloads - Manage compute and data assets - Plan and configure Azure Machine Learning workspace |
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NEW QUESTION # 449
You use Azure Machine Learning studio to analyze an mltable data asset containing a decimal column named column1. You need to verify that the column1 values are normally distributed.
Which statistic should you use?
Answer: C
NEW QUESTION # 450
You are running a training experiment on remote compute in Azure Machine Learning.
The experiment is configured to use a conda environment that includes the mlflow and azureml-contrib-run packages.
You must use MLflow as the logging package for tracking metrics generated in the experiment.
You need to complete the script for the experiment.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: import mlflow
Import the mlflow and Workspace classes to access MLflow ' s tracking URI and configure your workspace.
Box 2: mlflow.start_run()
Set the MLflow experiment name with set_experiment() and start your training run with start_run().
Box 3: mlflow.log_metric( ' .. ' )
Use log_metric() to activate the MLflow logging API and begin logging your training run metrics.
Box 4: mlflow.end_run()
Close the run:
run.endRun()
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow
NEW QUESTION # 451
You tram and register a model by using the Azure Machine Learning Python SDK v2 in a local workstation.
Python 3.7 and Visual Studio Code are instated on the workstation.
When you try to deploy the model into production to a Kubernetes online endpoint you experience an error in the scoring script that causes deployment to fail.
You need to debug the service on the local workstation before deploying the service to production.
Which three actions should you perform m sequence? To answer, move the appropriate actions from the list of actions from the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation
NEW QUESTION # 452
You create an Azure Machine learning workspace. The workspace contains a folder named src. The folder contains a Python script named script 1 .py.
You use the Azure Machine Learning Python SDK v2 to create a control script. You must use the control script to run script l.py as part of a training job.
You need to complete the section of script that defines the job parameters.
How should you complete the script? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
NEW QUESTION # 453
You manage an Azure Machine Learning workspace. You create an experiment named experiment1 by using the Azure Machine Learning Python SDK v2 and MLflow.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Answer:
Explanation:
Explanation:
NEW QUESTION # 454
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