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NEW QUESTION: 1
다음 표에 표시된 리소스 그룹이 포함된 Azure 구독이 있습니다.
RG1에는 다음 표에 나와있는 리소스가 포함되어 있습니다.
RG2에는 다음 표에 나와있는 리소스가 포함되어 있습니다.
RG1에서 RG2로 이동할 수있는 자원과 RG2에서 RG1로 이동할수 있는 자원을 식별해야합니다.
어떤 리소스를 식별해야 합니까? 답변하려면 답변 영역에서 적절한 옵션을 선택하십시오.
Answer:
Explanation:
Explanation
Reference:
https://docs.microsoft.com/en-us/azure/governance/blueprints/concepts/resource-locking
NEW QUESTION: 2
회사는 NTP 서버를 사용하여 시스템간에 시간을 동기화합니다. 이 회사는 여러 버전의 Linux 및 Windows 시스템을 실행합니다. NTP 서버에 장애가 발생하여 인스턴스에 대체 NTP 서버를 추가해야 합니다.
실행중인 인스턴스를 재부팅하지 않고 NTP 서버 업데이트를 적용하여 정보를 전파해야 하는 위치는 어디입니까?
A. cfn-init 스크립트
B. DHCP 옵션 설정
C. 인스턴스 사용자 데이터
D. 인스턴스 메타 데이터
Answer: A
NEW QUESTION: 3
You have user profile records in your OLPT database, that you want to join with web logs you have already ingested into the Hadoop file system. How will you obtain these user records?
A. Ingest with Flume agents
B. Pig LOAD command
C. Sqoop import
D. HDFS command
E. Ingest with Hadoop Streaming
F. Hive LOAD DATA command
Answer: B
Explanation:
Apache Hadoop and Pig provide excellent tools for extracting and analyzing data
from very large Web logs.
We use Pig scripts for sifting through the data and to extract useful information from the Web logs.
We load the log file into Pig using the LOAD command.
raw_logs = LOAD 'apacheLog.log' USING TextLoader AS (line:chararray);
Note 1:
Data Flow and Components
*Content will be created by multiple Web servers and logged in local hard discs. This content will then be pushed to HDFS using FLUME framework. FLUME has agents running on Web servers; these are machines that collect data intermediately using collectors and finally push that data to HDFS.
*Pig Scripts are scheduled to run using a job scheduler (could be cron or any sophisticated batch job solution). These scripts actually analyze the logs on various dimensions and extract the results. Results from Pig are by default inserted into HDFS, but we can use storage
implementation for other repositories also such as HBase, MongoDB, etc. We have also tried the solution with HBase (please see the implementation section). Pig Scripts can either push this data to HDFS and then MR jobs will be required to read and push this data into HBase, or Pig scripts can push this data into HBase directly. In this article, we use scripts to push data onto HDFS, as we are showcasing the Pig framework applicability for log analysis at large scale.
*The database HBase will have the data processed by Pig scripts ready for reporting and further slicing and dicing.
*The data-access Web service is a REST-based service that eases the access and integrations with data clients. The client can be in any language to access REST-based API. These clients could be BI- or UI-based clients.
Note 2:
The Log Analysis Software Stack
*Hadoop is an open source framework that allows users to process very large data in parallel. It's based on the framework that supports Google search engine. The Hadoop core is mainly divided into two modules:
1.HDFS is the Hadoop Distributed File System. It allows you to store large amounts of data using multiple commodity servers connected in a cluster.
2.Map-Reduce (MR) is a framework for parallel processing of large data sets. The default implementation is bonded with HDFS.
*The database can be a NoSQL database such as HBase. The advantage of a NoSQL database is that it provides scalability for the reporting module as well, as we can keep historical processed data for reporting purposes. HBase is an open source columnar DB or NoSQL DB, which uses HDFS. It can also use MR jobs to process data. It gives real-time, random read/write access to very large data sets -- HBase can save very large tables having million of rows. It's a distributed database and can also keep multiple versions of a single row.
*The Pig framework is an open source platform for analyzing large data sets and is implemented as a layered language over the Hadoop Map-Reduce framework. It is built to ease the work of developers who write code in the Map-Reduce format, since code in Map-Reduce format needs to be written in Java. In contrast, Pig enables users to write code in a scripting language.
*Flume is a distributed, reliable and available service for collecting, aggregating and moving a large amount of log data (src flume-wiki). It was built to push large logs into Hadoop-HDFS for further processing. It's a data flow solution, where there is an originator and destination for each node and is divided into Agent and Collector tiers for collecting logs and pushing them to destination storage.
Reference: Hadoop and Pig for Large-Scale Web Log Analysis
NEW QUESTION: 4
Employment Laws, Worker Safety laws and Privacy laws are 3 examples of:
A. Laws that apply to temporary workers but not employees
B. Laws that apply to employees but not temporary workers
C. Compliance issues for any supply chain category
D. Complexities in procuring services that are typically not seen in goods procurement
Answer: D
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