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NEW QUESTION: 1
Note: This question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question is independent of the other questions in this series. Information and details provided in a question apply only to that question.
You create a table by running the following Transact-SQL statement:

You need to develop a query that meets the following requirements:
* Output data by using a tree-like structure.
* Allow mixed content types.
* Use custom metadata attributes.
Which Transact-SQL statement should you run?


A. Option C
B. Option H
C. Option E
D. Option A
E. Option G
F. Option D
G. Option F
H. Option B
Answer: G
Explanation:
Explanation
In a FOR XML clause, you specify one of these modes: RAW, AUTO, EXPLICIT, and PATH.
* The EXPLICIT mode allows more control over the shape of the XML. You can mix attributes and elements at will in deciding the shape of the XML. It requires a specific format for the resulting rowset that is generated because of query execution. This rowset format is then mapped into XML shape. The power of EXPLICIT mode is to mix attributes and elements at will, create wrappers and nested complex properties, create space-separated values (for example, OrderID attribute may have a list of order ID values), and mixed contents.
* The PATH mode together with the nested FOR XML query capability provides the flexibility of the EXPLICIT mode in a simpler manner.
References: https://msdn.microsoft.com/en-us/library/ms178107.aspx

NEW QUESTION: 2
You are producing a multiple linear regression model in Azure Machine Learning Studio.
Several independent variables are highly correlated.
You need to select appropriate methods for conducting effective feature engineering on all the data.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:
Explanation:

Explanation:
Step 1: Use the Filter Based Feature Selection module
Filter Based Feature Selection identifies the features in a dataset with the greatest predictive power.
The module outputs a dataset that contains the best feature columns, as ranked by predictive power. It also outputs the names of the features and their scores from the selected metric.
Step 2: Build a counting transform
A counting transform creates a transformation that turns count tables into features, so that you can apply the transformation to multiple datasets.
Step 3: Test the hypothesis using t-Test
References:
https://docs.microsoft.com/bs-latn-ba/azure/machine-learning/studio-module-reference/filter-based-feature-selection
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/build-counting-transform

NEW QUESTION: 3
회사는 모든 이메일이 7 년 동안 외부 적으로 Pe를 저장하고 보관해야 한다는 규정 요구 사항을 준수해야 합니다. 관리자가 온 프레미스에서 압축 된 이메일 파일을 생성했으며 관리 형 서비스가 파일을 AWS 스토리지로 전송하기를 원합니다.
솔루션 설계자가 추천해야 하는 관리 형 서비스는 무엇입니까?
A. Amazon Elastic File System (Amazon EPS)
B. Amazon S3 Glacier
C. AWS 백업
D. AWS 스토리지 게이트웨이
Answer: D