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| Section | Objectives |
|---|---|
| Topic 1: Train machine learning models | - Train models using Azure Machine Learning - Tune hyperparameters and evaluate models |
| Topic 2: Deploy and consume models | - Deploy models to endpoints - Monitor deployed models and endpoints |
| Topic 3: Optimize and manage models | - Improve model performance - Track experiments and manage model lifecycle |
| Topic 4: 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 |
| Topic 5: Explore and analyze data | - Ingest and prepare data for modeling - Perform exploratory data analysis |
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NEW QUESTION # 326
A coworker registers a datastore in a Machine Learning services workspace by using the following code:
You need to write code to access the datastore from a notebook.
Answer:
Explanation:
Explanation:
Box 1: DataStore
To get a specific datastore registered in the current workspace, use the get() static method on the Datastore class:
# Get a named datastore from the current workspace
datastore = Datastore.get(ws, datastore_name='your datastore name')
Box 2: ws
Box 3: demo_datastore
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-access-data
NEW QUESTION # 327
You manage an Azure Machine Learning won pace named workspace 1 by using the Python SDK v2. You create a Gene-al Purpose v2 Azure storage account named mlstorage1. The storage account includes a pulley accessible container name micOTtalnerl. 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 mtcontamer1.
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.
Answer:
Explanation:
Explanation:
NEW QUESTION # 328
You create a script that trains a convolutional neural network model over multiple epochs and logs the validation loss after each epoch. The script includes arguments for batch size and learning rate.
You identify a set of batch size and learning rate values that you want to try.
You need to use Azure Machine Learning to find the combination of batch size and learning rate that results in the model with the lowest validation loss.
What should you do?
Answer: A
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
NEW QUESTION # 329
You are preparing to use the Azure ML SDK to run an experiment and need to create compute. You run the following code:
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:
Explanation:
Box 1: No
If a training cluster already exists it will be used.
Box 2: Yes
The wait_for_completion method waits for the current provisioning operation to finish on the cluster.
Box 3: Yes
Low Priority VMs use Azure's excess capacity and are thus cheaper but risk your run being pre-empted.
Box 4: No
Need to use training_compute.delete() to deprovision and delete the AmlCompute target.
Reference:
https://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/training/train-on
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.computetarget
NEW QUESTION # 330
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 # 331
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