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NEW QUESTION: 1
What is a benefit of a Dell EMC SC Series Flash-optimized solution?
A. Writes are sent to Tier 1 write-intensive SSDs for better performance
B. Support for write-intensive and read-intensive SSDs in the same tier
C. Snapshot data is sent to RAID 10 write-intensive SSDs to optimize capacity
D. RAID 5 /RAID 6 is converted to RAID 10 for increased efficiency
Answer: C

NEW QUESTION: 2
What is an accurate statement about incremental forever backups?
A. Requires periodic full back ups
B. Copies the data that has changed since the last full back up
C. Backup is not created directly from the production data
D. Requires only one initial full back up
Answer: D

NEW QUESTION: 3
무선 서비스를 제공하는 회사는 사용자 활동에 대한 로그 파일을 저장하고 분석하는 솔루션이 필요합니다.
현재 로그 파일은 Amazon EC2 인스턴스의 Amazon Linux에 매일 제공됩니다. 배치 스크립트는 하루에 한 번 실행되어 타사 도구에서 분석하는데 사용되는 데이터를 집계합니다. 타사 도구로 푸시 된 데이터는 최종 사용자를위한 시각화를 생성하는 데 사용됩니다. 배치 스크립트는 유지 관리가 번거롭고 계속 증가하는 데이터 볼륨을 타사 도구에 제공하는 데 몇 시간이 걸립니다. 이 회사는 비용 절감을 원하며 개발 노력을 최소화하고 관리 오버 헤드를 줄이는 새로운 도구를 고려할 수 있습니다. 이 회사는 최소한의 오버 헤드로 분석을 거의 실시간으로 저장하고 수행 할 수있는보다 민첩한 솔루션을 구축하려고합니다. 이 솔루션은 회사의 최종 사용자 기반 성장을 충족시키기 위해 비용 효율적이고 확장 가능해야 합니다.
어떤 솔루션이 회사의 요구 사항을 충족합니까?
A. Auto Scaling 그룹의 EC2 인스턴스에서 실행중인 Amazon Kinesis 에이전트를 사용하여 데이터를 수집하여 Amazon Kinesis Data Forehose 전송 스트림으로 보냅니다. Kinesis Data Firehose 전송 스트림은 데이터를 Amazon ES로 직접 전달합니다. Kibana를 사용하여 데이터를 시각화하십시오.
B. EC2 인스턴스에서 실행중인 Amazon Kinesis 에이전트를 사용하여 데이터를 수집하여 Amazon Kinesis Data Firehose 전송 스트림으로 보냅니다. Kinesis Data Firehose 전송 스트림은 데이터를 Amazon S3에 전달합니다. AWS Lambda 함수를 사용하여 Amazon S3에서 Amazon ES로 데이터를 전달하십시오. Kibana를 사용하여 데이터를 시각화하십시오.
C. Amazon EC2의 데이터를 실시간으로 실패하고 Amazon S3에 데이터를 저장하는 Python 스크립트를 개발하십시오. copy 명령을 사용하여 Amazon S3에서 Amazon Redshift로 데이터를 복사하십시오. Amazon EC2에서 실행되는 비즈니스 인텔리전스 도구를 Amazon Redshift에 연결하고 시각화를 만듭니다.
D. Amazon EBS 최적화 EC2 인스턴스에서 실행중인 메모리 내 캐싱 애플리케이션을 사용하여 거의 실시간으로 로그 데이터를 캡처하십시오. 동일한 EC2 인스턴스에 Amazon ES 클러스터를 설치하여 거의 실시간으로 Amazon EC2에 제공되는 로그 파일을 저장합니다. Kibana 플러그인을 설치하여 시각화를 작성하십시오.
Answer: A
Explanation:
https://docs.aws.amazon.com/firehose/latest/dev/writing-with-agents.html

NEW QUESTION: 4
Overview:
Litware, Inc. is a company that manufactures personal devices to track physical activity and other health- related data.
Litware has a health tracking application that sends health-related data from a user's personal device to Microsoft Azure.
Litware has three development and commercial offices. The offices are located in the United States, Luxembourg, and India.
Litware products are sold worldwide. Litware has commercial representatives in more than 80 countries.
Existing Environment:
In addition to using desktop computers in all of the offices, Litware recently started using Microsoft Azure resources and services for both development and operations.
Litware has an Azure Machine Learning solution.
Litware recently extended its platform to provide third-party companies with the ability to upload data from devices to Azure. The data can be aggregated across multiple devices to provide users with a comprehensive view of their global health activity.
While the upload from each device is small, potentially more than 100 million devices will upload data daily by using an Azure event hub.
Each health activity has a small amount of data, such as activity type, start date/time, and end date/time.
Each activity is limited to a total of 3 KB and includes a customer identification key.
In addition to the Litware health tracking application, the users' activities can be reported to Azure by using an open API.
The developers at Litware perform Machine Learning experiments to recommend an appropriate health activity based on the past three activities of a user.
The Litware developers train a model to recommend the best activity for a user based on the hour of the day.
Requirements:
Litware plans to extend the existing dashboard features so that health activities can be compared between the users based on age, gender, and geographic region.
Minimize the costs associated with transferring data from the event hub to Azure Storage.
Litware identifies the following technical requirements:
Data from the devices must be stored for three years in a format that enables the fast processing of

date fields and filtering.
The third-party companies must be able to use the Litware Machine Learning models to generate

recommendations to their users by using a third-party application.
Any changes to the health tracking application must ensure that the Litware developers can run the

experiments without interrupting or degrading the performance of the production environment.
Activity tracking data must be available to all of the Litware developers for experimentation. The developers must be prevented from accessing the private information of the users.
When the Litware health tracking application asks users how they feel, their responses must be reported to Azure.
You need to recommend a data handling solution to support the planned changes to the dashboard.
What is the best recommendation to achieve the goal? More than one answer choice may achieve the goal. Select the BEST answer.
A. compression
B. encryption
C. anonymization
D. obfuscation
Answer: D
Explanation:
Explanation/Reference:
Explanation:
From scenario: Litware plans to extend the existing dashboard features so that health activities can be compared between the users based on age, gender, and geographic region.
The developers must be prevented from accessing the private information of the users.
Dynamic Data Masking can be used to hide or obfuscate sensitive data, by controlling how the data appears in the output of database queries.
Dynamic Data Masking rules can be defined on particular columns, indicating how the data in those columns will appear when queried. There are no physical changes to the data in the database itself; the data remains intact and is fully available to authorized users or applications. Database operations remain unaffected, and the masked data has the same data type as the original data, so DDM can often be applied without making any changes to database procedures or application code.
Reference: https://blogs.technet.microsoft.com/dataplatforminsider/2016/01/25/use-dynamic-data- masking-to-obfuscate-your-sensitive-data/