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NEW QUESTION: 1
한 회사가 사용자에게 최소한의 지연 시간을 요구하는 모바일 앱용 아키텍처를 만들고 있습니다.
회사의 아키텍처는 Auto Scaling 그룹에서 실행되는 Application Load Balancer 뒤의 Amazon EC2 인스턴스로 구성됩니다. EC2 인스턴스는 Amazon RDS에 연결됩니다.
애플리케이션 베타 테스트에 따르면 데이터를 읽을 때 속도가 느려졌습니다. 그러나 지표는 EC2 인스턴스가 CPU 사용률 임계 값을 넘지 않음을 나타냅니다.
이 문제를 어떻게 해결할 수 있습니까?
A. RDS 인스턴스에 대한 읽기 전용 복제본을 추가하고 읽기 트래픽을 복제본으로 전달
B. Auto Scaling 그룹에서 CPU 사용률 임계 값 줄이기
C. RDS 인스턴스에 다중 AZ 지원을 추가하고 읽기 트래픽을 새 EC2 인스턴스로 전달
D. Application Load Balancer를 Network Load Balancer로 교체
Answer: A
NEW QUESTION: 2
You have been asked to use table compression for two large tables. Given are the details of the tables:
The TRANS_DETtable:
The table is used by an OLTP application.
High volume insert and update operations are performed on the table.
The table is frequently queried using index range scans.
The TRANS_HISTORYtable:
The table is used by a DSS application.
High volume bulk loads are performed on the table.
The table is used to store archival data on which large table full-table scans (FTS) are performed.
Which row store compression would you recommend for these tables with minimal overhead on performance? (Choose the best answer.)
A. basic table compression for both the tables
B. warehouse compression for the TRANS_DETtable and archive compression for the TRANS_HISTORY table
C. advanced row compression for both the tables
D. basic table compression for the TRANS_HISTORYtable and advanced row compression for the TRANS_DETtable
E. basic table compression for the TRANS_DETtable and advanced row compression for the TRANS_HISTORYtable
Answer: A
NEW QUESTION: 3
You configured the Fast Recovery Area (FRA) for your database. The database instance is in ARCHIVELOG mode. The default location for the archived redo log files is the FRA.
Which two files are removed automatically if space is required in the FRA as per the retention policy?
(Choose two.)
A. Archived redo log files that have multiple copies in a different archive location
B. user-managed backups of data files and control files
C. flashback logs
D. RMAN backups that are obsolete
Answer: C,D
NEW QUESTION: 4
会社は、Platform-as-a-Service（PaaS）を使用して新しいデータパイプラインプロセスを作成する予定です。プロセスは次の要件を満たしている必要があります。
摂取：
*複数のデータソースにアクセスする
*ワークフローを調整する機能を提供する
* SQL Server Integration Servicesパッケージを実行する機能を提供します。
格納：
*ビッグデータワークロード向けにストレージを最適化します。
*保存データの暗号化を提供します。
*サイズ制限なしで動作します。
準備とトレーニング：
*探索と視覚化のための完全に管理されたインタラクティブなワークスペースを提供します。
* R、SQL、Python、Scala、およびJavaでプログラミングする機能を提供します。
* Azure Active Directoryでシームレスなユーザー認証を提供します。
モデルとサービス：
*ネイティブのカラムナーストレージを実装します。
* SQL言語のサポート
*構造化ストリーミングのサポートを提供します。
データ統合パイプラインを構築する必要があります。
どのテクノロジーを使用する必要がありますか？回答するには、回答エリアで適切なオプションを選択します。
Answer:
Explanation:
Explanation
Ingest: Azure Data Factory
Azure Data Factory pipelines can execute SSIS packages.
In Azure, the following services and tools will meet the core requirements for pipeline orchestration, control flow, and data movement: Azure Data Factory, Oozie on HDInsight, and SQL Server Integration Services (SSIS).
Store: Data Lake Storage
Data Lake Storage Gen1 provides unlimited storage.
Note: Data at rest includes information that resides in persistent storage on physical media, in any digital format. Microsoft Azure offers a variety of data storage solutions to meet different needs, including file, disk, blob, and table storage. Microsoft also provides encryption to protect Azure SQL Database, Azure Cosmos DB, and Azure Data Lake.
Prepare and Train: Azure Databricks
Azure Databricks provides enterprise-grade Azure security, including Azure Active Directory integration.
With Azure Databricks, you can set up your Apache Spark environment in minutes, autoscale and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch and scikit-learn.
Model and Serve: SQL Data Warehouse
SQL Data Warehouse stores data into relational tables with columnar storage.
Azure SQL Data Warehouse connector now offers efficient and scalable structured streaming write support for SQL Data Warehouse. Access SQL Data Warehouse from Azure Databricks using the SQL Data Warehouse connector.
References:
https://docs.microsoft.com/bs-latn-ba/azure/architecture/data-guide/technology-choices/pipeline-orchestration-da
https://docs.microsoft.com/en-us/azure/azure-databricks/what-is-azure-databricks
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