DP-100 Practice Test

Question 66

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 is 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 easily converted 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?

1. Create a tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/sales.csv' file every month. Register the dataset with the name sales_dataset each month, replacing the existing dataset and specifying a tag named month indicating the month and year it was registered. Use this dataset for all experiments.
2. Create a tabular dataset that references the datastore and specifies the path 'sales/*/sales.csv', register the dataset with the name sales_dataset and a tag named month indicating the month and year it was registered, and use this dataset for all experiments.
3. Create a new tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/sales.csv' file every month. Register the dataset with the name sales_dataset_MM-YYYY each month with appropriate MM and YYYY values for the month and year. Use the appropriate month-specific dataset for experiments.
4. Create a tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/sales.csv' file. Register the dataset with the name sales_dataset each month as a new version and with a tag named month indicating the month and year it was registered. Use this dataset for all experiments, identifying the version to be used based on the month tag as necessary.

Question 67

An organization uses Azure Machine Learning service and wants to expand their use of machine learning. You have the following compute environments. The organization does not want to create another compute environment. You need to determine which compute environment to use for the following scenarios. Which compute types should you use? To answer, drag the appropriate compute environments to the correct scenarios. Each compute environment 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. Select and Place:

Question 68

You create an Azure Machine Learning compute target named ComputeOne by using the STANDARD_D1 virtual machine image. ComputeOne is currently idle and has zero active nodes. You define a Python variable named ws that references the Azure Machine Learning workspace. You run the following Python code: For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:

Question 69

You are developing a deep learning model by using TensorFlow. You plan to run the model training workload on an Azure Machine Learning Compute Instance. You must use CUDA-based model training. You need to provision the Compute Instance. Which two virtual machines sizes can you use? To answer, select the appropriate virtual machine sizes in the answer area. NOTE: Each correct selection is worth one point. Hot Area:

Question 70

You are analyzing a raw dataset that requires cleaning. You must perform transformations and manipulations by using Azure Machine Learning Studio. You need to identify the correct modules to perform the transformations. Which modules should you choose? To answer, drag the appropriate modules to the correct scenarios. Each module 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. Select and Place: