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NEW QUESTION: 1
You want to capture column group usage and gather extended statistics for better cardinality estimates for the customers table in the SH schema.
Examine the following steps:
1. Issue the SELECTDBMS_STATS. CREATE_EXTENDED_STATS('SH', 'CUSTOMERS')from dual statement.
2.Execute the dbms_stats.seed_col_usage (null,'SH',500) procedure.
3.Execute the required queries on the customers table.
4.Issue the select dbms_stats.reportwcol_usage('SH', 'customers') from dual statement.
Identify the correct sequence of steps.
A. 3, 2, 4, 1
B. 2, 3, 4, 1
C. 3, 2, 1, 4
D. 4, 1, 3, 2
Answer: B
Explanation:
Explanation/Reference:
Explanation:
Step 1 (2). Seed column usage
Oracle must observe a representative workload, in order to determine the appropriate column groups.
Using the new procedure DBMS_STATS.SEED_COL_USAGE, you tell Oracle how long it should observe the workload.
Step 2: (3) You don't need to execute all of the queries in your work during this window. You can simply run explain plan for some of your longer running queries to ensure column group information is recorded for these queries.
Step 3. (1) Create the column groups
At this point you can get Oracle to automatically create the column groups for each of the tables based on the usage information captured during the monitoring window. You simply have to call the DBMS_STATS.CREATE_EXTENDED_STATS function for each table.This function requires just two arguments, the schema name and the table name. From then on, statistics will be maintained for each column group whenever statistics are gathered on the table.
Note:
* DBMS_STATS.REPORT_COL_USAGE reports column usage information and records all the SQL operations the database has processed for a given object.
* The Oracle SQL optimizer has always been ignorant of the implied relationships between data columns within the same table. While the optimizer has traditionally analyzed the distribution of values within a column, he does not collect value-based relationships between columns.
* Creating extended statistics
Here are the steps to create extended statistics for related table columns withdbms_stats.created_extended_stats:
1 - The first step is to create column histograms for the related columns.
2 - Next, we run dbms_stats.create_extended_stats to relate the columns together.
Unlike a traditional procedure that is invoked via an execute ("exec") statement, Oracle extended statistics are created via a select statement.

NEW QUESTION: 2
A company collects temperature, humidity, and atmospheric pressure data in cities across multiple continents.
The average volume of data collected per site each day is 500 GB. Each site has a high-speed internet connection. The company's weather forecasting applications are based in a single Region and analyze the data daily.
What is the FASTEST way to aggregate data for all of these global sites?
A. Enable Amazon S3 Transfer Acceleration on the destination bucket. Use multipart uploads to directly upload site data to the destination bucket.
B. Upload site data to an Amazon S3 bucket in the closest AWS Region. Use S3 cross-Region replication to copy objects to the destination bucket.
C. Upload site data to an Amazon S3 bucket in the closest AWS Region. Use S3 cross-Region replication to copy objects to the destination bucket.
D. Upload the data to an Amazon EC2 instance in the closes Region. Store the data in an Amazon EBS volume. One a day take an EBS snapshot and copy it to the centralize Region. Restore the EBS volume in the centralized Region and run an analysis on the data daily.
Answer: B
Explanation:
Explanation
Step -1 To transfer to S3 from global sites : Amazon S3 Transfer Acceleration enables fast, easy, and secure transfers of files over long distances between your client and your Amazon S3 bucket. S3 Transfer Acceleration leverages Amazon CloudFront's globally distributed AWS Edge Locations. Used to accelerate object uploads to S3 over long distances (latency). Transfer acceleration is as secure as a direct upload to S3.
Step -2 : When the application analyze/aggregate the data from S3 and then again upload the results - Multipart upload
http://lavnish.blogspot.com/2017/06/aws-s3-cross-region-replication.html
https://aws.amazon.com/s3/transfer-acceleration/

NEW QUESTION: 3
기업의 애플리케이션은 점점 더 대중화되고 있으며 데이터베이스 서버의 대량 읽기 때문에 지연이 발생합니다.
이 서비스에는 다음과 같은 속성이 있습니다.
* 자동 확장 기능이 있는 ALB (Application Load Balancer)를 사용하여 한 지역에서 호스트되는 고 가용성 REST API입니다.
단일 Availability Zone에서 Amazon EC2 인스턴스에서 호스팅되는 MySQL 데이터베이스.
회사는 대기 시간을 줄이고 지역 내 데이터베이스 읽기 성능을 향상 시키며 데이터 또는 성능 손실 (HA / DR)없이 자동으로 실시간 복구를 수행 할 수있는 다중 지역 재해 복구 기능을 원합니다.
어떤 배포 전략이 이러한 요구 사항을 충족합니까?
A. AWS CloudFormation StackSets를 사용하여 API 레이어를 두 영역에 배포합니다. 한 영역에 여러 개의 읽기 복제본이 있고 원본 데이터베이스 클러스터와 다른 영역에 읽기 복제본이 있는 MySQL 데이터베이스 클러스터가있는 Amazon Aurora로 데이터베이스를 마이그레이션 하십시오. Amazon Route 53 상태 검사를 사용하여 기본로드 밸런서에 대한 상태 검사에 실패하면 대기 영역에 대한 DNS 페일 오버를 트리거합니다. Route 53 페일 오버의 경우 교차 영역 데이터베이스 복제본을 마스터로 승격시키고 대기 영역에서 새 읽기 복제본을 작성하십시오.
B. Redis Multi-AZ에 Amazon ElastiCache를 사용하여 자동 장애 조치를 사용하여 데이터베이스 읽기 쿼리를 캐시합니다. AWS OpsWorks를 사용하여 두 영역에 API 계층, 캐시 계층 및 기존 데이터베이스 계층을 배포합니다. 장애가 발생할 경우 데이터베이스의 Amazon Route 53 상태 검사를 사용하여 기본 영역의 상태 검사에 실패하면 대기 영역으로의 DNS 페일 오버를 트리거합니다. MySQL 데이터베이스를 자주 백업하고 활성 region에 장애가 발생할 경우 백업을 대기 영역에 복사하고 대기 데이터베이스를 복원하십시오.
C. 자동 장애 조치 기능을 갖춘 Redis Multi-AZ 용 Amazon ElastiCache를 사용하여 데이터베이스 읽기 쿼리를 캐시합니다. AWS OpsWorks를 사용하여 두 영역에 API 계층, 캐시 계층 및 기존 데이터베이스 계층을 배포합니다. 기본 영역의 상태 검사에 실패하면 ALB에서 Amazon Route 53 상태 검사를 사용하여 대기 영역에 대한 DNS 페일 오버를 트리거합니다. MySQL 데이터베이스를 자주 백업하고 활성 region에 장애가 발생할 경우 백업을 대기 영역에 복사하고 대기 데이터베이스를 복원하십시오.
D. AWS CloudFormation StackSets을 사용하여 API 레이어를 두 영역에 배포합니다. 자동 확장 그룹에 데이터베이스를 추가하십시오. 두 번째 영역의 데이터베이스에 읽기 복제본을 추가합니다. 기본 영역에서 Amazon Route 53 상태 검사를 사용하지 못합니다. 지역 간 데이터베이스 복제본을 마스터로 승격시키고 대기 영역에서 새 읽기 복제본을 빌드하십시오.
Answer: A

NEW QUESTION: 4
各特性を正しいストレージタイプに一致させます。

Answer:
Explanation:

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