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NEW QUESTION: 1
Which open source cloud computing platform uses the neutron project as a networking as a service between devices like cisco nexus, cisco APIC cisco CSR 1000v and cisco UCS?
A. Openstack
B. Cisco UCS director
C. Opendaylight
D. Ansible
Answer: A
NEW QUESTION: 2
Ein Techniker ersetzt das LCD-Panel in einem Laptop. Welche der folgenden Komponenten wird während der Reparatur am wahrscheinlichsten versehentlich beschädigt?
A. Das Motherboard
B. Die Lünette
C. Der Digitalisierer
D. Das Scharnier
Answer: B
NEW QUESTION: 3
注：この質問は、同じシナリオを提示する一連の質問の一部です。 シリーズの各質問には、上記の目標を達成できる独自の解決策が含まれています。 いくつかの質問セットには1つ以上の正しい解決策があるかもしれないが、他の質問セットには正しい解決策がないかもしれない。
このセクションの質問に答えると、それに戻ることはできません。 その結果、これらの質問はレビュー画面に表示されません。
新しいActive Directoryフォレストを展開します。
複数のメンバーサーバーに対してグループ管理サービスアカウント（gMSA）を作成できることを確認する必要があります。
解決策：各メンバサーバーのコンピュータアカウントにKerberos制約付き委任を構成します。
これは目標を満たしていますか？
A. はい
B. いいえ
Answer: B
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 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.
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. 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
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)
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