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NEW QUESTION: 1
You are optimizing the performance of a batch update process. You have tables and indexes that were created by running the following Transact-SQL statements:

The following query runs nightly to update the isCreditValidated field:

You review the database and make the following observations:
* Most of the IsCreditValidated values in the Invoices table are set to a value of 1.
* There are many unique InvoiceDate values.
* The CreditValidation table does not have an index.
* Statistics for the index IX_invoices_CustomerID_Filter_IsCreditValidated indicate there are no individual seeks but multiple individual updates.
You need to ensure that any indexes added can be used by the update query. If the IX_invoices_CustomerId_Filter_IsCreditValidated index cannot be used by the query, it must be removed.
Otherwise, the query must be modified to use with the index.
Which three actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Create a nonclustered index for invoices in IsCreditValidated, InvoiceDate with an include statement using IsCreditNote and CustomerID.
B. Rewrite the update query so that the condition for IsCreditValidated = 0 precedes the condition for IsCreditNote = 1.
C. Add a filtered nonclustered index to Invoices on InvoiceDate that selects where IsCreditNote= 1 and IsCreditValidated = 0.
D. Drop the IX_invoices_CustomerId_Filter_IsCreditValidatedIndex.
E. Add a nonclustered index for CreditValidation on CustomerID.
Answer: B,C,D
Explanation:
Explanation
A filtered index is an optimized nonclustered index especially suited to cover queries that select from a welldefined subset of data. It uses a filter predicate to index a portion of rows in the table. A well-designed filtered index can improve query performance as well as reduce index maintenance and storage costs compared with full-table indexes.
References:
https://docs.microsoft.com/en-us/sql/relational-databases/indexes/create-filtered-indexes

NEW QUESTION: 2
You need to recommend a solution for configuring the Azure Multi-Factor Authentication (MFA) settings.
What should you include in the recommendation? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

References:
https://docs.microsoft.com/en-us/azure/active-directory/identity-protection/howto-sign-in-risk-policy
https://docs.microsoft.com/en-us/azure/active-directory/identity-protection/howto-mfa-policy

NEW QUESTION: 3
경매 웹 사이트를 통해 사용자는 수집 가능한 품목에 입찰 할 수 있습니다. 경매 규칙에 따르면 각 입찰은 한 번만 접수된 순서대로 처리되어야 합니다. 현재 구현은 Amazon Kinesis Data Streams에 입찰 레코드를 쓰는 Amazon EC2 웹 서버를 기반으로 합니다.
단일 t2.large 인스턴스에는 Kinesis Data Streams에서 들어오는 입찰을 읽고 각 입찰을 처리하는 입찰 프로세서를 실행하는 cron 작업이 있습니다. 경매 사이트의 인기가 높아지고 있지만 사용자는 일부 입찰이 등록되지 않았다고 불평하고 있습니다.
문제 해결은 수요가 많은 시간 동안 입찰 프로세서가 너무 느려 처리 중 때때로 충돌하며 처리중인 레코드를 추적하지 못하는 경우가 있습니다. 입찰 처리의 안정성을 높이려면 어떤 변화가 있습니까?
A. EC2 인스턴스 유형을 t2.large에서 더 큰 일반 컴퓨팅 인스턴스 유형으로 전환하십시오. Kinesis Data Streams의 IncomingRecords 지표를 기반으로 입찰 프로세서를 실행하는 EC2 인스턴스 수를 확장하는 Auto Scaling 그룹에 입찰 프로세서 EC2 인스턴스를 배치합니다.
B. Kinesis Data Streams에 입찰을 게시 할 때 Amazon Kinesis Producer Library (KPL)를 사용하도록 웹 애플리케이션을 리팩터링하십시오. 입찰 프로세서를 리팩터링하여 Kinesis Data Streams의 각 레코드를 읽지 않고 처리하고 처리중인 것으로 플래그를 지정합니다. 각 입찰 처리 실행이 시작될 때 Kinesis Data Streams에서 처리되지 않은 레코드를 검색합니다.
C. 웹 애플리케이션을 리팩터링하여 각 수신 입찰을 Kinesis Data Streams 대신 Amazon SQS FIFO 대기열에 게시합니다. 지속적으로 SQS 대기열에 입찰 프로세서를 리팩터링하십시오. 최소 및 최대 크기가 1 인 Auto Scaling 그룹에 입찰 처리 EC2 인스턴스를 배치하십시오.
D. 웹 애플리케이션을 리팩터링하여 각 수신 입찰을 Kinesis Data Streams 대신 Amazon SNS 주제에 게시합니다. 사용자가 입찰을 제출하자마자 각 입찰을 처리하는 AWS Lambda 함수를 트리거하도록 SNS 주제를 구성하십시오.
Answer: A