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NEW QUESTION: 1
Given the code fragment:

Which code fragment, when inserted at line 3, enables the code to print 10:20?

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

NEW QUESTION: 2
You administer a Microsoft SQL Server 2012 database named ContosoDb. The database contains a table named Suppliers and a column named IsActive in the Purchases schema. You create a new user named ContosoUser in ContosoDb. ContosoUser has no permissions to the Suppliers table. You need to ensure that ContosoUser can delete rows that are not active from Suppliers. You also need to grant ContosoUser only the minimum required permissions. Which Transact-SQL statement should you use?
A. CREATE PROCEDURE Purchases.PurgeInactiveSuppliers AS DELETE FROM Purchases.Suppliers WHERE IsActive = 0 GO GRANT EXECUTE ON Purchases.PurgeInactiveSuppliers TO ContosoUser
B. GRANT SELECT ON Purchases.Suppliers TO ContosoUser
C. GRANT DELETE ON Purchases.Suppliers TO ContosoUser
D. CREATE PROCEDURE Purchases.PurgeInactiveSuppliers WITH EXECUTE AS USER = 'dbo' AS DELETE FROM Purchases.Suppliers WHERE IsActive = 0 GO GRANT EXECUTE ON Purchases.PurgelnactiveSuppliers TO ContosoUser
Answer: A
Explanation:
Reference: http://msdn.microsoft.com/en-us/library/ms188354.aspx Reference: http://msdn.microsoft.com/en-us/library/ms187926.aspx

NEW QUESTION: 3
대형 소비재 제조업체는 다음과 같은 제품을 판매합니다
* 34 가지의 치약 변형
* 48 가지 칫솔 변형
* 43 가지 구강 세척제
이 모든 제품의 전체 판매 내역은 Amazon S3에서 확인할 수 있습니다. 현재이 회사는 맞춤형 자동 회귀 통합 이동 평균 (ARIMA) 모델을 사용하여 해당 제품에 대한 수요를 예측하고 있습니다. 회사는 곧 새로운 제품에 대한 수요를 예측하려고 합니다. 머신 러닝 전문가가 어떤 솔루션을 적용해야 합니까?
A. 새로운 제품에 대한 수요를 예측하기 위해 사용자 정의 XGBoost 모델을 훈련
B. 맞춤형 ARIMA 모델을 교육하여 신제품에 대한 수요를 예측하십시오.
C. Amazon SageMaker DeepAR 알고리즘을 훈련하여 새로운 제품에 대한 수요 예측
D. Amazon SageMaker k- 평균 클러스터링 알고리즘을 훈련시켜 신제품에 대한 수요를 예측하십시오.
Answer: C
Explanation:
Explanation
The Amazon SageMaker DeepAR forecasting algorithm is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNN). Classical forecasting methods, such as autoregressive integrated moving average (ARIMA) or exponential smoothing (ETS), fit a single model to each individual time series. They then use that model to extrapolate the time series into the future.