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NEW QUESTION: 1
You are sitting in an airport preparing to use a free WLAN. When you look at the connections in your Windows 7 wireless client, you see a network name and three boxes with a yellow shield.
What does this mean?
A. A secure ad hoc network is nearby.
B. The WLAN in the airport is open.
C. A secure WLAN is in the airport.
D. You cannot connect to this WLAN.
E. An open ad hoc network is nearby.
F. You must provide a username and password to access the WLAN in the airport.
Answer: E
Explanation:
Explanation/Reference:
Explanation:
You can connect to a detected network by clicking the wireless connection icon in the status bar, clicking an SSID name (the signal strength of the associated AP is represented by green bars, five bars representing the best signal; an exclamation mark inside a yellow shield is displayed if the SSID has no security [Open authentication, no encryption]), clicking Connect and completing the security parameters when applicable.
http://www.ciscopress.com/articles/article.asp?p=2131139

NEW QUESTION: 2
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、記載された目標を達成する可能性のある独自のソリューションが含まれています。一部の質問セットには複数の正しい解決策がある場合もあれば、正しい解決策がない場合もあります。
このシナリオで質問に答えた後、その質問に戻ることはできません。その結果、これら
質問はレビュー画面に表示されません。
Table1という名前のテーブルを含むDB1という名前のAzure SQLデータベースがあります。 Table1には、varchar（22）であるCustomer_IDという名前のフィールドがあります。
次の要件を満たすには、Customer_IDフィールドにマスキングを実装する必要があります。
*最初の2つのプレフィックス文字を公開する必要があります。
*最後の4つのプレフィックス文字を公開する必要があります。
他のすべての文字はマスクする必要があります。
解決策：データマスキングを実装し、乱数関数マスクを使用します。
これは目標を達成していますか？
A. いいえ
B. はい
Answer: A
Explanation:
Explanation
Must use Custom Text data masking, which exposes the first and last characters and adds a custom padding string in the middle.
References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-dynamic-data-masking-get-started

NEW QUESTION: 3
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 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 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
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)

NEW QUESTION: 4

A. Option D
B. Option B
C. Option A
D. Option C
Answer: D