Latest DP-100 Exam Questions

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 have a Python script named train.py in a local folder named scripts. The script trains a regression model
by using scikit-learn. The script includes code to load a training data file which is also located in the scripts
folder.
You must run the script as an Azure ML experiment on a compute cluster named aml-compute.
You need to configure the run to ensure that the environment includes the required packages for model
training. You have instantiated a variable named aml-compute that references the target compute cluster.
Solution: Run the following code:

Does the solution meet the goal?

You create a classification model with a dataset that contains 100 samples with Class A and 10,000 samples with Class B 
The variation of Class B is very high.
You need to resolve imbalances.
Which method should you use?

You create an Azure Machine Learning workspace and set up a development environment. You plan to train a
deep neural network (DNN) by using the Tensorflow framework and by using estimators to submit training
scripts.
You must optimize computation speed for training runs.
You need to choose the appropriate estimator to use as well as the appropriate training compute target
configuration.
Which values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

You have a dataset that contains 2,000 rows. You are building a machine learning classification model by
using Azure Learning Studio. You add a Partition and Sample module to the experiment.
You need to configure the module. You must meet the following requirements:
Divide the data into subsets
Assign the rows into folds using a round-robin method
Allow rows in the dataset to be reused
How should you configure the module? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point

You are implementing a machine learning model to predict stock prices.
The model uses a PostgreSQL database and requires GPU processing.
You need to create a virtual machine that is pre-configured with the required tools.
What should you do?

You use Azure Machine Learning Studio to build a machine learning experiment.
You need to divide data into two distinct datasets.
Which module should you use?

You have a dataset that contains 2,000 rows. You are building a machine learning classification model by using Azure Learning Studio. You add a Partition and Sample module to the experiment. You need to configure the module. You must meet the following requirements: Divide the data into subsets
Assign the rows into folds using a round-robin method
Allow rows in the dataset to be reused
How should you configure the module? To answer, select the appropriate options in the dialog box in the answer area. NOTE: Each correct selection is worth one point.

You are implementing a machine learning model to predict stock prices.
The model uses a PostgreSQL database and requires GPU processing.
You need to create a virtual machine that is pre-configured with the required tools.
What should you do?

A set of CSV files contains sales records. All the CSV files have the same data schema.
Each CSV file contains the sales record for a particular month and has the filename sales.csv. Each file in stored in a folder that indicates the month and year when the data was recorded. The folders are in an Azure blob container for which a datastore has been defined in an Azure Machine Learning workspace. The folders are organized in a parent folder named sales to create the following hierarchical structure:

At the end of each month, a new folder with that month’s sales file is added to the sales folder.
You plan to use the sales data to train a machine learning model based on the following requirements:
You must define a dataset that loads all of the sales data to date into a structure that can be easilyconverted to a dataframe.
You must be able to create experiments that use only data that was created before a specific previous
month, ignoring any data that was added after that month.
You must register the minimum number of datasets possible.
You need to register the sales data as a dataset in Azure Machine Learning service workspace.
What should you do?

You use the following code to run a script as an experiment in Azure Machine Learning: 

You must identify the output files that are generated by the experiment run.
You need to add code to retrieve the output file names.
Which code segment should you add to the script?