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NEW QUESTION 24
You are developing a data science workspace that uses an Azure Machine Learning service.
You need to select a compute target to deploy the workspace.
What should you use?
- A. Azure Data Lake Analytics
- B. Azure Databricks
- C. Apache Spark for HDInsight
- D. Azure Container Service
Answer: D
Explanation:
Azure Container Instances can be used as compute target for testing or development. Use for low-scale CPU- based workloads that require less than 48 GB of RAM.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where
NEW QUESTION 25
You create a multi-class image classification deep learning model.
The model must be retrained monthly with the new image data fetched from a public web portal. You create an Azure Machine Learning pipeline to fetch new data, standardize the size of images, and retrain the model.
You need to use the Azure Machine Learning SDK to configure the schedule for the pipeline.
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.
Answer:
Explanation:
Explanation:
Step 1: Publish the pipeline.
To schedule a pipeline, you'll need a reference to your workspace, the identifier of your published pipeline, and the name of the experiment in which you wish to create the schedule.
Step 2: Retrieve the pipeline ID.
Needed for the schedule.
Step 3: Create a ScheduleRecurrence..
To run a pipeline on a recurring basis, you'll create a schedule. A Schedule associates a pipeline, an experiment, and a trigger.
First create a schedule. Example: Create a Schedule that begins a run every 15 minutes:
recurrence = ScheduleRecurrence(frequency="Minute", interval=15)
Step 4: Define an Azure Machine Learning pipeline schedule..
Example, continued:
recurring_schedule = Schedule.create(ws, name="MyRecurringSchedule",
description="Based on time",
pipeline_id=pipeline_id,
experiment_name=experiment_name,
recurrence=recurrence)
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-schedule-pipelines
NEW QUESTION 26
You register a file dataset named csv_folder that references a folder. The folder includes multiple comma- separated values (CSV) files in an Azure storage blob container.
You plan to use the following code to run a script that loads data from the file dataset. You create and instantiate the following variables:
You have the following code:
You need to pass the dataset to ensure that the script can read the files it references.
Which code segment should you insert to replace the code comment?
- A. script_params={'--training_files': file_dataset},
- B. inputs=[file_dataset.as_named_input('training_files').as_mount()],
- C. inputs=[file_dataset.as_named_input('training_files').to_pandas_dataframe()],
- D. inputs=[file_dataset.as_named_input('training_files')],
Answer: B
Explanation:
Example:
from azureml.train.estimator import Estimator
script_params = {
# to mount files referenced by mnist dataset
'--data-folder': mnist_file_dataset.as_named_input('mnist_opendataset').as_mount(),
'--regularization': 0.5
}
est = Estimator(source_directory=script_folder,
script_params=script_params,
compute_target=compute_target,
environment_definition=env,
entry_script='train.py')
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-train-models-with-aml
NEW QUESTION 27
You need to define a process for penalty event detection.
Which three 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.
Answer:
Explanation:
NEW QUESTION 28
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