You plan to run a script as an experiment using a Script Run Configuration. The script uses modules from the scipy library as well as several Python packages that are not typically installed in a default conda environment.You plan to run the experiment on your local workstation for small datasets and scale out the experiment by running it on more powerful remote compute clusters for larger datasets.You need to ensure that the experiment runs successfully on local and remote compute with the least administrative effort.What should you do?
Answer(s): C
If you have an existing Conda environment on your local computer, then you can use the service to create an environment object. By using this strategy, you can reuse your local interactive environment on remote runs.
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-environments
You write a Python script that processes data in a comma-separated values (CSV) file.You plan to run this script as an Azure Machine Learning experiment.The script loads the data and determines the number of rows it contains using the following code:You need to record the row count as a metric named row_count that can be returned using the get_metrics method of the Run object after the experiment run completes.Which code should you use?
Answer(s): B
Log a numerical or string value to the run with the given name using log(name, value, description=''). Logging a metric to a run causes that metric to be stored in the run record in the experiment. You can log the same metric multiple times within a run, the result being considered a vector of that metric.Example: run.log("accuracy", 0.95)Incorrect Answers:E: Using log_row(name, description=None, **kwargs) creates a metric with multiple columns as described in kwargs. Each named parameter generates a column with the value specified. log_row can be called once to log an arbitrary tuple, or multiple times in a loop to generate a complete table.Example: run.log_row("Y over X", x=1, y=0.4)
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run
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 are creating a new experiment in Azure Machine Learning Studio.One class has a much smaller number of observations than the other classes in the training set.You need to select an appropriate data sampling strategy to compensate for the class imbalance.Solution: You use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.Does the solution meet the goal?
Answer(s): A
SMOTE is used to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
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 are creating a new experiment in Azure Machine Learning Studio.One class has a much smaller number of observations than the other classes in the training set.You need to select an appropriate data sampling strategy to compensate for the class imbalance.Solution: You use the Stratified split for the sampling mode.Does the solution meet the goal?
Instead use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.Note: SMOTE is used to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.
You are creating a machine learning model.You need to identify outliers in the data.Which two visualizations can you use? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.
Answer(s): B,E
The box-plot algorithm can be used to display outliers.One other way to quickly identify Outliers visually is to create scatter plots.
https://blogs.msdn.microsoft.com/azuredev/2017/05/27/data-cleansing-tools-in-azure-machine-learning/
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