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NEW QUESTION: 1
You have an Azure Linux virtual machine that is protected by Azure Backup.
One week ago, two files were deleted from the virtual machine.
You need to restore the deleted files to an on-premises computer as quickly as possible.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:
Explanation:

Explanation

To restore files or folders from the recovery point, go to the virtual machine and choose the desired recovery point.
Step 0. In the virtual machine's menu, click Backup to open the Backup dashboard.
Step 1. In the Backup dashboard menu, click File Recovery.
Step 2. From the Select recovery point drop-down menu, select the recovery point that holds the files you want. By default, the latest recovery point is already selected.
Step 3: To download the software used to copy files from the recovery point, click Download Executable (for Windows Azure VM) or Download Script (for Linux Azure VM, a python script is generated).
Step 4: Copy the files by using AzCopy
AzCopy is a command-line utility designed for copying data to/from Microsoft Azure Blob, File, and Table storage, using simple commands designed for optimal performance. You can copy data between a file system and a storage account, or between storage accounts.
References:
https://docs.microsoft.com/en-us/azure/backup/backup-azure-restore-files-from-vm
https://docs.microsoft.com/en-us/azure/storage/common/storage-use-azcopy

NEW QUESTION: 2
엔터프라이즈 회사의 데이터 과학 팀은 Amazon SageMaker에 쉽게 액세스 할 수 있는 안전하고 비용 효율적인 방법을 제공하고자 합니다. 데이터 과학자는 AWS 지식이 제한되어 있으며 Jupyter 노트북 인스턴스를 시작할 수 있어야 합니다. 노트북 인스턴스에는 복잡한 설정 요구 사항을 노출시키지 않고 머신 러닝 스토리지 볼륨에서 유휴 데이터를 암호화하기 위해 사전 구성된 AWS KMS 키가 있어야 합니다.
회사가 데이터 과학자가 AWS 계정에서 Jupyter 노트북을 최소의 운영 오버 헤드로 시작할 수있는 셀프 서비스 메커니즘을 설정할 수 있는 방법은 무엇입니까?
A. 사전 구성된 KMS 키가있는 AWS :: SageMaker :: Notebooklnstance 리소스 유형을 사용하여 Jupyter 노트북 인스턴스를 시작하는 AWS CloudFormation 템플릿을 생성하십시오. CloudFormation의 Mappings 섹션을 사용하여 인스턴스 크기와 같은 매개 변수 이름을 Small, Large 및 X-Large에 맵핑하여 매개 변수 이름을 단순화 하십시오. 출력 섹션을 사용하여 노트북에 URL을 표시한 다음 템플릿을 데이터 과학자 포트폴리오의 AWS 서비스 카탈로그 제품에 업로드하고 데이터 과학자의 IAM 역할과 공유합니다.
B. 정적 Amazon S3 웹 사이트를 사용하여 서버리스 프론트 엔드를 생성하면 데이터 과학자가 양식을 작성하여 Jupyter 노트북 인스턴스를 요청할 수 있습니다. Amazon API Gateway를 사용하여 S3 웹 사이트에서 요청을 수신하고 중앙 AWS Lambda 함수를 트리거하여 데이터 과학자를 위해 사전 구성된 KMS 키로 노트북 인스턴스를 시작하는 Amazon SageMaker에 대한 API 호출을 수행하십시오. 그런 다음 프론트 엔드 웹 사이트로 다시 전화하여 노트북 인스턴스에 대한 URL을 표시하십시오.
C. 사전 구성된 KMS 키가있는 AWS :: SaqeMaker :: Notebooklnstance 리소스 유형을 사용하여 Jupyter 노트북 인스턴스를 시작하는 AWS CloudFormation 템플릿을 생성하십시오. CloudFormation 템플릿에 사용자에게 친숙한 이름을 추가하십시오. 출력 섹션을 사용하여 노트북의 URL을 표시하십시오.
공유 Amazon S3 버킷을 사용하여 CloudFormation 템플릿을 데이터 과학자에게 배포하십시오.
D. 데이터 과학자가 로컬에서 실행할 수 있는 AWS CLI 스크립트를 생성하십시오. 사전 구성된 KMS 키로 Jupyter 노트북을 시작하기 위해 AWS CLI 스크립트를 실행하는 동안 제공 할 파라미터에 대한 단계별 지침을 제공하십시오. 공유 Amazon S3 버킷을 사용하여 데이터 과학자에게 CLI 스크립트를 배포하십시오.
Answer: A
Explanation:
Explanation
https://aws.amazon.com/blogs/mt/enable-self-service-secured-data-science-using-amazon-sagemaker-notebooks-

NEW QUESTION: 3
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
You have a Microsoft SQL Server data warehouse instance that supports several client applications.
The data warehouse includes the following tables: Dimension.SalesTerritory, Dimension.Customer, Dimension.Date, Fact.Ticket, and Fact.Order. The Dimension.SalesTerritory and Dimension.Customer tables are frequently updated. The Fact.Order table is optimized for weekly reporting, but the company wants to change it daily. The Fact.Order table is loaded by using an ETL process. Indexes have been added to the table over time, but the presence of these indexes slows data loading.
All data in the data warehouse is stored on a shared SAN. All tables are in a database named DB1. You have a second database named DB2 that contains copies of production data for a development environment. The data warehouse has grown and the cost of storage has increased. Data older than one year is accessed infrequently and is considered historical.
You have the following requirements:
* Implement table partitioning to improve the manageability of the data warehouse and to avoid the need
* to repopulate all transactional data each night. Use a partitioning strategy that is as granular as possible.
* Partition the Fact.Order table and retain a total of seven years of data.
* Partition the Fact.Ticket table and retain seven years of data. At the end of each month, the partition structure must apply a sliding window strategy to ensure that a new partition is available for the upcoming month, and that the oldest month of data is archived and removed.
* Optimize data loading for the Dimension.SalesTerritory, Dimension.Customer, and Dimension.Date tables.
* Incrementally load all tables in the database and ensure that all incremental changes are processed.
* Maximize the performance during the data loading process for the Fact.Order partition.
* Ensure that historical data remains online and available for querying.
* Reduce ongoing storage costs while maintaining query performance for current data.
You are not permitted to make changes to the client applications.
You need to implement the data partitioning strategy.
How should you partition the Fact.Order table?
A. Create 2,557 partitions.
B. Use a granularity of two days.
C. Create 730 partitions.
D. Create 17,520 partitions.
Answer: A
Explanation:
Explanation
We create on partition for each day. 7 years times 365 days is 2,555. Make that 2,557 to provide for leap years.
From scenario: Partition the Fact.Order table and retain a total of seven years of data.
Maximize the performance during the data loading process for the Fact.Order partition.

NEW QUESTION: 4
Here is the structure and data of the CUST_TRANS table:
Exhibit:

Dates are stored in the default date format dd-mm-rr in the CUST_TRANS table.
Which three SQL statements would execute successfully? (Choose three.)
A. SELECT * FROM cust_trans WHERE transdate='01-JANUARY-07';
B. SELECT transamt FROM cust_trans WHERE custno > '11';
C. SELECT * FROM cust_trans WHERE transdate = '01-01-07';
D. SELECT custno + 'A' FROM cust_trans WHERE transamt > 2000;
E. SELECT transdate + '10' FROM cust_trans;
Answer: A,B,E