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| Section | Objectives |
|---|---|
| Topic 1: Deploy and consume models | - Monitor deployed models and endpoints - Deploy models to endpoints |
| Topic 2: Train machine learning models | - Tune hyperparameters and evaluate models - Train models using Azure Machine Learning |
| Topic 3: Design and prepare a machine learning solution | - Select appropriate Azure services for machine learning workloads - Plan and configure Azure Machine Learning workspace - Manage compute and data assets |
| Topic 4: Explore and analyze data | - Ingest and prepare data for modeling - Perform exploratory data analysis |
| Topic 5: Optimize and manage models | - Improve model performance - Track experiments and manage model lifecycle |
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NEW QUESTION # 361
You use an Azure Machine Learning workspace. Azure Data Factor/ pipeline, and a dataset monitor that runs en a schedule to detect data drift.
You need to Implement an automated workflow to trigger when the dataset monitor detects data drift and launch the Azure Data Factory pipeline to update the dataset. The solution must minimize the effort to configure the workflow.
How should you configure the workflow? To answer select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 362
You register a model that you plan to use in a batch inference pipeline.
The batch inference pipeline must use a ParallelRunStep step to process files in a file dataset. The script has the ParallelRunStep step runs must process six input files each time the inferencing function is called.
You need to configure the pipeline.
Which configuration setting should you specify in the ParallelRunConfig object for the PrallelRunStep step?
Answer: B
Explanation:
Explanation
node_count is the number of nodes in the compute target used for running the ParallelRunStep.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/azureml.contrib.pipeline.steps.parall
NEW QUESTION # 363 
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 364
You are tuning a hyperparameter for an algorithm. The following table shows a data set with different hyperparameter, training error, and validation errors.
Answer:
Explanation:
NEW QUESTION # 365
You manage an Azure Machine Learning workspace.
You build a custom model you must log with MLflow. 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 for the model must be packaged with the model.
You need to create a custom model flavor to enable logging with MLflow.
What should you use?
Answer: B
Explanation:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-mlflow- models?view=azureml-api-2&tabs=loader
NEW QUESTION # 366
......
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