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This opens up additional technicalities like the DHCP, NTP and TFTP.
NEW QUESTION: 1
You need to make the line item text field mandatory during document entry. Which objects should you analyze to fulfill this request?
Note: There are 2 correct answers to this question.
A. Account group
B. Posting key
C. Document type
D. G/L account
Answer: B,D
NEW QUESTION: 2
Which statement about wireless intrusion prevention and rogue access point detection is true?
A. A local mode access point provides power to wireless clients.
B. A monitor mode access point can distribute a white list of all known access points.
C. A monitor mode access point performs background scanning in order to detect rogue access points.
D. Any access point that broadcasts the same RF group name or is part of the same mobility group is considered to be a rogue access point.
E. A monitor mode access point is dedicated to scanning (listen-only).
Answer: E
Explanation:
Explanation/Reference:
Explanation:
NEW QUESTION: 3
単一のエンティティの識別が不可能になるように、機密データを取得し、各データオブジェクトから間接識別子を削除するプロセスはどのようなものですか。
A. 暗号化
B. 匿名化
C. マスキング
D. トークン化
Answer: B
Explanation:
Anonymization is a type of masking, where indirect identifiers are removed from a data set to prevent the mapping back of data to an individual. Although masking refers to the overall approach of covering sensitive data, anonymization is the best answer here because it is more specific to exactly what is being asked. Tokenization involves the replacement of sensitive data with a key value that can be matched back to the real value. However, it is not focused on indirect identifiers or preventing the matching to an individual. Encryption refers to the overall process of protecting data via key pairs and protecting confidentiality.
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 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.
B. 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
C. 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.
D. 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.
Answer: D
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)