DP-100 Exam Dumps - Designing and Implementing a Data Science Solution on Azure

Scenario: Testing

You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio.

Box 1: Assign to folds

Use Assign to folds option when you want to divide the dataset into subsets of the data. This option is also useful when you want to create a custom number of folds for cross-validation, or to split rows into several groups.

Not Head: Use Head mode to get only the first n rows. This option is useful if you want to test a pipeline on a small number of rows, and don't need the data to be balanced or sampled in any way.

Not Sampling: The Sampling option supports simple random sampling or stratified random sampling. This is useful if you want to create a smaller representative sample dataset for testing.

Box 2: Partition evenly

Specify the partitioner method: Indicate how you want data to be apportioned to each partition, using these options:

Partition evenly: Use this option to place an equal number of rows in each partition. To specify the number of output partitions, type a whole number in the Specify number of folds to split evenly into text box.

Box 1: Mutual Information.

The mutual information score is particularly useful in feature selection because it maximizes the mutual information between the joint distribution and target variables in datasets with many dimensions.

Box 2: MedianValue

MedianValue is the feature column, , it is the predictor of the dataset.

Scenario: The MedianValue and AvgRoomsinHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.

You can print out detailed Docker engine log messages from the service object. You can view the log for ACI, AKS, and Local deployments. The following example demonstrates how to print the logs.

# if you already have the service object handy

print(service.get_logs())

# if you only know the name of the service (note there might be multiple services with the same name but different version number)

print(ws.webservices['mysvc'].get_logs())

You need to implement an early stopping criterion on models that provides savings without terminating promising jobs.

Truncation selection cancels a given percentage of lowest performing runs at each evaluation interval. Runs are compared based on their performance on the primary metric and the lowest X% are terminated.

Example:

from azureml.train.hyperdrive import TruncationSelectionPolicy

early_termination_policy = TruncationSelectionPolicy(evaluation_interval=1, truncation_percentage=20, delay_evaluation=5)

Box 1: from azureml.core.webservice import Webservice

The following code shows how to use the SDK to update the model, environment, and entry script for a web service to Azure Container Instances:

from azureml.core import Environment

from azureml.core.webservice import Webservice

from azureml.core.model import Model, InferenceConfig

Box 2: predictions = service.run(input_json)

Example: The following code demonstrates sending data to the service:

import json

test_sample = json.dumps({'data': [

[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],

[10, 9, 8, 7, 6, 5, 4, 3, 2, 1]

]})

test_sample = bytes(test_sample, encoding='utf8')

prediction = service.run(input_data=test_sample)

print(prediction)