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NEW QUESTION: 1
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section. you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You create a table named Products by running the following Transact-SQL statement:

You have the following stored procedure:

You need to modify the stored procedure to meet the following new requirements:
- Insert product records as a single unit of work.
- Return error number 51000 when a product fails to insert into the database.
- If a product record insert operation fails, the product information must not be permanently written to the database.
Solution: You run the following Transact-SQL statement:

Does the solution meet the goal?
A. No
B. Yes
Answer: B
Explanation:
Explanation
If the INSERT INTO statement raises an error, the statement will be caught and an error 51000 will be thrown.
In this case no records will have been inserted.
Note:
You can implement error handling for the INSERT statement by specifying the statement in a TRY...CATCH construct.
If an INSERT statement violates a constraint or rule, or if it has a value incompatible with the data type of the column, the statement fails and an error message is returned.
References: https://msdn.microsoft.com/en-us/library/ms174335.aspx

NEW QUESTION: 2
HPE OneViewはインフラストラクチャ管理をどのように簡素化しますか？
A. シリコンルートオブトラストを通じて、業界をリードするインフラストラクチャセキュリティを提供します。
B. API駆動型インターフェースを介して、サードパーティのインフラストラクチャ自動化ツールとの統合を提供します。
C. コンピューティング、ストレージ、ファブリック全体の管理をサポートする統合プラットフォームを提供します。
D. 物理サーバーと仮想マシンを管理します。
Answer: B,C

NEW QUESTION: 3
You have a dataset that contains information about taxi journeys that occurred during a given period.
You need to train a model to predict the fare of a taxi journey.
What should you use as a feature?
A. the trip distance of individual taxi journeys
B. the number of taxi journeys in the dataset
C. the fare of individual taxi journeys
D. the trip ID of individual taxi journeys
Answer: A
Explanation:
Explanation
The label is the column you want to predict. The identified Features are the inputs you give the model to predict the Label.
Example:
The provided data set contains the following columns:
vendor_id: The ID of the taxi vendor is a feature.
rate_code: The rate type of the taxi trip is a feature.
passenger_count: The number of passengers on the trip is a feature.
trip_time_in_secs: The amount of time the trip took. You want to predict the fare of the trip before the trip is completed. At that moment, you don't know how long the trip would take. Thus, the trip time is not a feature and you'll exclude this column from the model.
trip_distance: The distance of the trip is a feature.
payment_type: The payment method (cash or credit card) is a feature.
fare_amount: The total taxi fare paid is the label.
Reference:
https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/predict-prices