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NEW QUESTION: 1
A. Right([Product Name]; 5)
B. Right((Product Name), 5)
C. Right(5, [Product Name])
D. Right(5; [Product Name])
Answer: A
NEW QUESTION: 2
Which of the following statements are true regarding N Bids and a Buy?
Please choose the correct answer.
Response:
A. Require collaboration with a minimum number of suppliers
B. Both Require collaboration with a minimum number of suppliers and Used for both goods and services
C. None of the above
D. Used for both goods and services
Answer: B
NEW QUESTION: 3
In the zx2 chipset, which internal I/O bus handles data transfers between PCI interfaces and main memory?
A. pcioa
B. xio
C. ioa-x
D. mio
Answer: D
NEW QUESTION: 4
CSVファイルのセットには販売レコードが含まれています。すべてのCSVファイルは同じデータスキーマを持っています。
各CSVファイルには、特定の月の販売レコードが含まれ、ファイル名はsales.csvです。各ファイルは、データが記録された月と年を示すフォルダーに保存されます。フォルダーは、Azure MachineLearningワークスペースでデータストアが定義されているAzureBLOBコンテナー内にあります。フォルダーは、salesという名前の親フォルダーに編成され、次の階層構造を作成します。
月末に、その月のsalesファイルを含む新しいフォルダーがsalesフォルダーに追加されます。
販売データを使用して、次の要件に基づいて機械学習モデルをトレーニングすることを計画しています。
*これまでのすべての販売データを、データフレームに簡単に変換できる構造にロードするデータセットを定義する必要があります。
*特定の前月より前に作成されたデータのみを使用し、その月以降に追加されたデータを無視する実験を作成できる必要があります。
*可能な限り最小数のデータセットを登録する必要があります。
Azure MachineLearningサービスワークスペースに販売データをデータセットとして登録する必要があります。
あなたは何をするべきか？
A. データストアを参照し、毎月各 'sales / mm-yyyy /sales.csv'ファイルを明示的に指定する表形式のデータセットを作成します。毎月sales_datasetという名前でデータセットを登録し、既存のデータセットを置き換えて、登録された月と年を示すmonthという名前のタグを指定します。このデータセットをすべての実験に使用します。
B. データストアを参照し、パス 'sales / * / sales.csv'を指定する表形式のデータセットを作成し、sales_datasetという名前と、登録された月と年を示すmonthという名前のタグでデータセットを登録します。これを使用します。すべての実験のデータセット。
C. データストアを参照し、毎月各 'sales / mm-yyyy /sales.csv'ファイルを明示的に指定する新しい表形式のデータセットを作成します。毎月sales_dataset_MM-YYYYという名前のデータセットを、月と年に適切なMM値とYYYY値で登録します。実験には、適切な月固有のデータセットを使用してください。
D. データストアを参照し、各 'sales / mm-yyyy /sales.csv'ファイルを明示的に指定する表形式のデータセットを作成します。データセットを毎月の名前で新しいバージョンとして登録し、月という名前のタグで登録した月と年を示します。このデータセットをすべての実験に使用し、に基づいて使用するバージョンを特定します。
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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