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NEW QUESTION: 1
ストレージ管理者によってプロビジョニングされているいくつかの新しいストレージ階層を利用するためには、大きなヒープテーブルのパーティションはOracle12cのデータベース内の他の表領域に移動する必要がありますか。
ローカルおよびグローバル・パーティションBツリー索引は表で定義されています。大量の取引は日中に表にアクセスし、メディアボリュームの取引は夜間や週末の間にそれにアクセスします。
最小の混乱させるイオンが必要とされて中断させます。
どの3つのステートメントはこの要件について真ですか。
A. パーティションは同じ表領域に圧縮することができます。
B. パーティションは新しい表領域に圧縮することができます。
C. ローカル索引はパーティションを移動した後、手動で再作成する必要があります。
D. グローバル索引はパーティションを移動した後、手動で再作成する必要があります。
E. パーティションが新しい表領域にオンラインで移動することができます。
Answer: A,B,E
Explanation:
A: You can create and rebuild indexes online. Therefore, you can update base tables at the same time you are building or rebuilding indexes on that table. You can perform DML operations while the index build is taking place, but DDL operations are not allowed. Parallel execution is not supported when creating or rebuilding an index online.
D: Moving (Rebuilding) Index-Organized Tables
Because index-organized tables are primarily stored in a B-tree index, you can encounter fragmentation as a consequence of incremental updates. However, you can use the ALTER TABLE...MOVE statement to rebuild the index and reduce this fragmentation.
C: If a table can be compressed in the new tablespace, also it can be compressed in the same tablespace.
Incorrect:
Not B, not E: Local and Global indexes can be automatically rebuild with UPDATE INDEXES when you move the table.

NEW QUESTION: 2
An airline has .csv-formatted data stored in Amazon S3 with an AWS Glue Data Catalog. Data analysts want to join this data with call center data stored in Amazon Redshift as part of a dally batch process. The Amazon Redshift cluster is already under a heavy load. The solution must be managed, serverless, well-functioning, and minimize the load on the existing Amazon Redshift cluster. The solution should also require minimal effort and development activity.
Which solution meets these requirements?
A. Create an external table using Amazon Redshift Spectrum for the call center data and perform the join with Amazon Redshift.
B. Unload the call center data from Amazon Redshift to Amazon S3 using an AWS Lambda function.
Perform the join with AWS Glue ETL scripts.
C. Export the call center data from Amazon Redshift to Amazon EMR using Apache Sqoop. Perform the join with Apache Hive.
D. Export the call center data from Amazon Redshift using a Python shell in AWS Glue. Perform the join with AWS Glue ETL scripts.
Answer: A

NEW QUESTION: 3
DRAG DROP - (Topic 2)


Answer:
Explanation: