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NEW QUESTION: 1
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
NEW QUESTION: 2
vCenter Server Appliance Instance can be backed up using which Client Interface?
A. Virtual Appliance Management Interface
B. vSphere Web Client Interface
C. vSphere Client Interface
D. VMware Host Client Interface
Answer: A
NEW QUESTION: 3
You need to participate in enterprise risk management and complete an HR-audit.
Which of the following is the best definition of an HR-audit in regard to risk management?
A. Identify the total number of employees by years of employment in the organization and verify their experience, education, and skills
B. Identify the competency of employees in each areas of the organization
C. Identify the total number of employees in the organization
D. Identify the HR areas that may be out of compliance with legal requirements
Answer: D
Explanation:
Reference: Professional in Human Resources Certification Study Guide, Sybex, ISBN: 9780-470-43096-5. Chapter Four: Workforce Planning and Employment. Official PHR and SPHR Certification Guide, HR Certification Institute, ISBN: 978-1-586-44149-4, Section III, The US Body of Knowledge.
Chapter: Business Management and Strategy
Objective: Strategic Management
NEW QUESTION: 4
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 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?
A. Create a tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/ sales.csv' file. Register the dataset with the name 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
B. 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.
C. 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.
D. 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.
Answer: B
Explanation:
Explanation
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 workspace
workspace = Workspace.from_config()
# retrieve an existing datastore in the workspace by name
datastore = Datastore.get(workspace, datastore_name)
# create a TabularDataset from 3 file paths in datastore
datastore_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)