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

You have the following Azure subscriptions and Azure Machine Learning service workspaces:

You need to obtain a reference to the mi-protect workspace

Solution: Run the following Python code.

Does the solution meet the goal?

You use Azure Machine Learning to train a model.

You must use a sampling method for tuning hyperparameters. The sampling method must pick samples based on how the model performed with previous samples.

You need to select a sampling method.

Which sampling method should you use?

You create a pipeline in designer to train a model that predicts automobile prices.

Because of non-linear relationships in the data, the pipeline calculates the natural log (Ln) of the prices in the training data, trains a model to predict this natural log of price value, and then calculates the exponential of the scored label to get the predicted price.

The training pipeline is shown in the exhibit. (Click the Training pipeline tab.)

Training pipeline

You create a real-time inference pipeline from the training pipeline, as shown in the exhibit. (Click the Real-time pipeline tab.)

Real-time pipeline

You need to modify the inference pipeline to ensure that the web service returns the exponential of the scored label as the predicted automobile price and that client applications are not required to include a price value in the input values.

Which three modifications must you make to the inference pipeline? Each correct answer presents part of the solution.

NOTE: Each correct selection is worth one point.

You create a multi-class image classification deep learning model that uses a set of labeled images. You

create a script file named train.py that uses the PyTorch 1.3 framework to train the model.

You must run the script by using an estimator. The code must not require any additional Python libraries to be installed in the environment for the estimator. The time required for model training must be minimized.

You need to define the estimator that will be used to run the script.

Which estimator type should you use?

You use Azure Machine Learning to train a model based on a dataset named dataset1.

You define a dataset monitor and create a dataset named dataset2 that contains new data.

You need to compare dataset1 and dataset2 by using the Azure Machine Learning SDK for Python.

Which method of the DataDriftDetector class should you use?

You plan to explore demographic data for home ownership in various cities. The data is in a CSV file with the following format:

age,city,income,home_owner

21,Chicago,50000,0

35,Seattle,120000,1

23,Seattle,65000,0

45,Seattle,130000,1

18,Chicago,48000,0

You need to run an experiment in your Azure Machine Learning workspace to explore the data and log the results. The experiment must log the following information:

the number of observations in the dataset

a box plot of income by home_owner

a dictionary containing the city names and the average income for each city

You need to use the appropriate logging methods of the experiment’s run object to log the required information.

How should you complete the code? To answer, drag the appropriate code segments to the correct locations. Each code segment may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.

NOTE: Each correct selection is worth one point.

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 Python SEX v2 to configure the schedule for the pipeline. The schedule should be defined by using the frequency and interval properties with frequency set to month' and interval set to "1:

Which three classes should you instantiate in sequence"' To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

You manage an Azure Machine Learning workspace.

You train a model interactively with a Jupyter Notebook in the workspace During training, a dataset is created with accuiacy and loss metrics for each epoch.

You need to configure model tracking with MLflow to log the dataset created during the training.

How should you complete the code segment? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.