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NEW QUESTION: 1


Answer:
Explanation:

Explanation

NEW QUESTION: 2
Contoso, Ltd., implements SharePoint Server 2013 to manage computer aided design (CAD) documents.
Users report that when they perform a search, text within CAD documents does not appear in the search results. Only file properties display in search results.
You need to ensure that search results display file properties and text within the documents.
Which two actions will achieve the goal? Each correct answer presents part of the solution. (Choose two.)
A. Validate that the content processing component has a format handler that can parse each file format.
B. Configure that crawl component to report results to the content database.
C. Enable the content processing component to parse files that have the file format and file format and file name extension.
D. Configure the crawl component to use custom entity extractors.
Answer: A,D
Explanation:
Explanation/Reference:
References:
https://docs.microsoft.com/en-us/sharepoint/technical-reference/default-crawled-file-name-extensions-and- parsed-file-types
https://docs.microsoft.com/en-us/sharepoint/search/create-and-deploy-custom-entity-extractors

NEW QUESTION: 3
Which two are true about Smart Scan?
A. A query rewrite may occur to a container table stored Exadata, and it will always benefit firom Smart Scan.
B. Column projection does not contribute to the perfiormance benefit ofi Smart Scan
C. It is possible to offload single row fiunctions to the storage servers.
D. Some joins can be offloaded to the storage servers.
E. a query rewrite may occur to a container table stored in Exadata but will never benefit From Smart scan.
F. All joins can be offloaded to the storage servers.
Answer: C,D
Explanation:
C: With Exadata storage, database operations are handled much more efficiently. Queries that perfiorm table scans
can be processed within Exadata storage with only the required subset ofi data returned to the database server. Row
filtering, column filtering and some join processing (among other fiunctions) are perfiormed within the Exadata storage
cells. When this takes place only the relevant and required data is returned to the database server.
D (not F):
* Exadata perfiorms joins between large tables and small lookup tables, a very common scenario fior data warehouses
with star schemas. Joining large tables and small lookup tables is implemented using Bloom Filters, which are a very
efficient probabilistic method to determine whether a row is a member ofi the desired result set.
* Ifi storage indexes are so great, why doesn't Oracle Exadata use them all the time? The short answer is that they are
created and used only when they will be beneficial.
* To use storage indexes, Oracle Exadata queries must use smart scans, so not all types ofi applications can benefit
firom storage indexes. Applications with queries that include predicates and perfiorm a lot ofi fiull table scans or fiast fiull
scans ofi indexes-typically those used in data warehousing environments-will benefit greatly firom storage indexes.
Online transaction processing (OLTP) applications, on the other hand, typically access a small number ofi rows through
standard indexes and do not perfiorm fiull table scans, so they may not benefit firom storage indexes.
Note:
* Storage indexes reside in the memory ofi the storage servers-also called storage cells-and significantly reduce
unnecessary I/O by excluding irrelevant database blocks in the storage cells.
* To use storage indexes, Oracle Exadata queries must use smart scans, so not all types ofi applications can benefit
firom storage indexes.
Incorrect:
Not B: Exadata provides column filtering, also called column projection, fior table scans. Only the columns requested
are returned to the database server rather than all columns in a table. For example, when the fiollowing SQL is issued,
only the employee_name and employee_number columns are returned firom Exadata to the database kernel.
SELECT employee_name, employee_number FROM employee_table.
For tables with many columns, or columns containing LOBs (Large Objects), the I/O bandwidth saved can be very
large. Using both predicate and column filtering dramatically improves perfiormance and reduces I/O bandwidth
consumption. In addition, column filtering also applies to indexes, allowing fior even fiaster query perfiormance.
Refierence: Oracle Communications Data Model Implementation and Operations Guide, Exadata Smart Scan
Processing and Storage Index

NEW QUESTION: 4
会社は、Amazon Route 53、AWS Elastic Beanstalk、Amazon RDSを使用する単一のAWSアカウントで本番アプリケーションのワークロードを実行します。セキュリティインシデントが発生した場合、セキュリティチームはアプリケーションワークロードを新しいAWSアカウントにフェイルオーバーすることを望んでいます。また、セキュリティチームは、フォレンジック分析中に、元のAWSアカウントのAWSリソースにアクセスせずに、元のアカウントへのすべてのアクセスをすぐにブロックしたいと考えています。
セキュリティインシデントの前に2番目のアカウントにフェールオーバーする準備をするための最も費用効果の高い方法は何ですか？
A. インシデント後に別のAWSアカウントで使用するためにAmazon Route 53構成を保存/コピーします。
Elastic Beanstalkの設定を別のアカウントにミラーリングします。 RDSデータベースのスナップショットを別のアカウントにコピーします。
B. Amazon Route 53設定を専用AWSアカウントに移行します。 Elastic Beanstalk設定を別のアカウントでミラーリングします。別のアカウントでRDSデータベースリードレプリカを有効にします。
C. Amazon Route 53構成を専用AWSアカウントに移行します。 Elastic Beanstalk設定ファイルを別のAWSアカウントに保存/コピーします。 RDSデータベースのスナップショットを別のアカウントにコピーします。
D. インシデント後に別のAWSアカウントで使用するためにAmazon Route 53構成を保存/コピーします。
Elastic Beanstalk設定ファイルを別のアカウントに保存/コピーします。別のアカウントでRDSデータベースのリードレプリカを有効にします。
Answer: C
Explanation:
https://docs.aws.amazon.com/elasticbeanstalk/latest/dg/environment-configuration- savedconfig.html
https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/hosted-zones-migrating.html