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NEW QUESTION: 1
A company builds an application to allow developers to share and compare code. The conversations, code snippets, and links shared by people in the application are stored in a Microsoft Azure SQL Database instance. The application allows for searches of historical conversations and code snippets.
When users share code snippets, the code snippet is compared against previously share code snippets by using a combination of Transact-SQL functions including SUBSTRING, FIRST_VALUE, and SQRT. If a match is found, a link to the match is added to the conversation.
Customers report the following issues:
Delays occur during live conversations
A delay occurs before matching links appear after code snippets are added to conversations You need to resolve the performance issues.
Which technologies should you use? To answer, drag the appropriate technologies to the correct issues. Each technology may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:
Explanation

Box 1: memory-optimized table
In-Memory OLTP can provide great performance benefits for transaction processing, data ingestion, and transient data scenarios.
Box 2: materialized view
To support efficient querying, a common solution is to generate, in advance, a view that materializes the data in a format suited to the required results set. The Materialized View pattern describes generating prepopulated views of data in environments where the source data isn't in a suitable format for querying, where generating a suitable query is difficult, or where query performance is poor due to the nature of the data or the data store.
These materialized views, which only contain data required by a query, allow applications to quickly obtain the information they need. In addition to joining tables or combining data entities, materialized views can include the current values of calculated columns or data items, the results of combining values or executing transformations on the data items, and values specified as part of the query. A materialized view can even be optimized for just a single query.
References:
https://docs.microsoft.com/en-us/azure/architecture/patterns/materialized-view

NEW QUESTION: 2
Which of the following value will be accepted for my_var?
1. variable "my_var"
2. {
3. type = string
4. }
A. None of the above
B. 0
C. Both A and B
D. "15"
Answer: C
Explanation:
The Terraform language will automatically convert number and bool values to string values when needed, and vice-versa as long as the string contains a valid representation of a number or boolean value.
Example
* true converts to "true", and vice-versa
* false converts to "false", and vice-versa
* 15 converts to "15", and vice-versa
Where possible, Terraform automatically converts values from one type to another in order to produce the expected type. If this isn't possible, Terraform will produce a type mismatch error and you must update the configuration with a more suitable expression.
https://www.terraform.io/docs/configuration/expressions.html#type-conversion

NEW QUESTION: 3
企業は、AWS CloudFormationスタックに更新をデプロイする前に、インフラストラクチャの各変更を確認したいと考えています。
管理者は実装前にこれらの変更の影響を理解することができますか？
A. 実行中のスタックの変更セットを作成します。
B. Application Load Balancerとターゲットグループを使用してカナリアデプロイを実行します。
C. AWS Elastic Beanstalkを使用して青/緑の戦略を実装します。
D. UpdateStack API呼び出しを使用して更新を送信します。
Answer: A
Explanation:
Explanation
https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/using-cfn-updating-stacks-changesets.html