Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.You plan to use a Python script to run an Azure Machine Learning experiment. The script creates a reference to the experiment run context, loads data from a file, identifies the set of unique values for the label column, and completes the experiment run:The experiment must record the unique labels in the data as metrics for the run that can be reviewed later.You must add code to the script to record the unique label values as run metrics at the point indicated by the comment.Solution: Replace the comment with the following code:run.log_table('Label Values', label_vals)Does the solution meet the goal?
Answer(s): B
Instead use the run_log function to log the contents in label_vals: for label_val in label_vals: run.log('Label Values', label_val)
https://www.element61.be/en/resource/azure-machine-learning-services-complete-toolbox-ai
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.You plan to use a Python script to run an Azure Machine Learning experiment. The script creates a reference to the experiment run context, loads data from a file, identifies the set of unique values for the label column, and completes the experiment run:The experiment must record the unique labels in the data as metrics for the run that can be reviewed later.You must add code to the script to record the unique label values as run metrics at the point indicated by the comment.Solution: Replace the comment with the following code:for label_val in label_vals:run.log('Label Values', label_val)Does the solution meet the goal?
The run_log function is used to log the contents in label_vals: for label_val in label_vals: run.log('Label Values', label_val)
HOTSPOT (Drag and Drop is not supported)You publish a batch inferencing pipeline that will be used by a business application.The application developers need to know which information should be submitted to and returned by the REST interface for the published pipeline.You need to identify the information required in the REST request and returned as a response from the published pipeline.Which values should you use in the REST request and to expect in the response? To answer, select the appropriate options in the answer area.NOTE: Each correct selection is worth one point.Hot Area:
Answer(s): A
Box 1: JSON containing an OAuth bearer tokenSpecify your authentication header in the request.To run the pipeline from the REST endpoint, you need an OAuth2 Bearer-type authentication header.Box 2: JSON containing the experiment nameAdd a JSON payload object that has the experiment name.Example:rest_endpoint = published_pipeline.endpointresponse = requests.post(rest_endpoint,headers=auth_header,json={"ExperimentName": "batch_scoring","ParameterAssignments": {"process_count_per_node": 6}})run_id = response.json()["Id"]Box 3: JSON containing the run IDMake the request to trigger the run. Include code to access the Id key from the response dictionary to get the value of the run ID.
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-pipeline-batch-scoring-classification
HOTSPOT (Drag and Drop is not supported)You create an experiment in Azure Machine Learning Studio. You add a training dataset that contains 10,000 rows. The first 9,000 rows represent class 0 (90 percent).The remaining 1,000 rows represent class 1 (10 percent).The training set is imbalances between two classes. You must increase the number of training examples for class 1 to 4,000 by using 5 data rows. You add theSynthetic Minority Oversampling Technique (SMOTE) module to the experiment.You need to configure the module.Which values should you use? To answer, select the appropriate options in the dialog box in the answer area.NOTE: Each correct selection is worth one point.Hot Area:
Box 1: 300 You type 300 (%), the module triples the percentage of minority cases (3000) compared to the original dataset (1000).Box 2: 5 We should use 5 data rows.Use the Number of nearest neighbors option to determine the size of the feature space that the SMOTE algorithm uses when in building new cases. A nearest neighbor is a row of data (a case) that is very similar to some target case. The distance between any two cases is measured by combining the weighted vectors of all features.By increasing the number of nearest neighbors, you get features from more cases.By keeping the number of nearest neighbors low, you use features that are more like those in the original sample.
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
You are solving a classification task.You must evaluate your model on a limited data sample by using k-fold cross-validation. You start by configuring a k parameter as the number of splits.You need to configure the k parameter for the cross-validation.Which value should you use?
Answer(s): C
Leave One Out (LOO) cross-validationSetting K = n (the number of observations) yields n-fold and is called leave-one out cross-validation (LOO), a special case of the K-fold approach.LOO CV is sometimes useful but typically doesn't shake up the data enough. The estimates from each fold are highly correlated and hence their average can have high variance.This is why the usual choice is K=5 or 10. It provides a good compromise for the bias-variance tradeoff.
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