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?
Answer(s): D
Specify the path.Example:The following code gets the workspace existing workspace and the desired datastore by name. And then passes the datastore and file locations to the path parameter to create a new TabularDataset, weather_ds. from azureml.core import Workspace, Datastore, Dataset datastore_name = 'your datastore name'# get existing workspaceworkspace = Workspace.from_config()# retrieve an existing datastore in the workspace by namedatastore = Datastore.get(workspace, datastore_name)# create a TabularDataset from 3 file paths in datastoredatastore_paths = [(datastore, 'weather/2018/11.csv'),(datastore, 'weather/2018/12.csv'),(datastore, 'weather/2019/*.csv')]weather_ds = Dataset.Tabular.from_delimited_files(path=datastore_paths)
DRAG DROP (Drag and Drop is not supported)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:
Answer(s): A
Box 1: nb_server Box 2: mlc_cluster With Azure Machine Learning, you can train your model on a variety of resources or environments, collectively referred to as compute targets. A compute target can be a local machine or a cloud resource, such as an Azure Machine Learning Compute, Azure HDInsight or a remote virtual machine.
https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target https://docs.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets
HOTSPOT (Drag and Drop is not supported)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:
Box 1: Yes ComputeTargetException class: An exception related to failures when creating, interacting with, or configuring a compute target. This exception is commonly raised for failures attaching a compute target, missing headers, and unsupported configuration values.Create(workspace, name, provisioning_configuration)Provision a Compute object by specifying a compute type and related configuration.This method creates a new compute target rather than attaching an existing one.Box 2: Yes Box 3: No The line before print('Step1') will fail.
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.computetarget
HOTSPOT (Drag and Drop is not supported)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:
CUDA is a parallel computing platform and programming model developed by Nvidia for general computing on its own GPUs (graphics processing units). CUDA enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
https://www.infoworld.com/article/3299703/what-is-cuda-parallel-programming-for-gpus.html
DRAG DROP (Drag and Drop is not supported)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:
Box 1: Clean Missing Data Box 2: SMOTE Use the SMOTE module in Azure Machine Learning Studio 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.Box 3: Convert to Indicator ValuesUse the Convert to Indicator Values module in Azure Machine Learning Studio. The purpose of this module is to convert columns that contain categorical values into a series of binary indicator columns that can more easily be used as features in a machine learning model.Box 4: Remove Duplicate Rows
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/convert-to-indicator-values
Share your comments for Microsoft DP-100 exam with other users:
best study material for exam
i need dump
very helpful
helpful dump questions