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NEW QUESTION: 1
You are developing a TM1 model for a city aquarium and must add these new elements to the Animal dimension: Amphibians. Frog, and Salamander. Assume that Amphibians is the parent of Frog and Salamander and that you want the children to automatically roll up to the parent.
Which weighting and element type will you assign to each element?
A. Amphibians Element Type = N, Weights Frog Element Type = N, Weight=0 Salamander Element Type
= N, Weight=0
B. Amphibians Element Type = C.
Weight=1 Frog Element Type = N, Weight=0 Salamander Element Type = N, Weight=0
C. Amphibians Element Type = C, Weight=1 Frog Element Type = N, Weight=1 Salamander Element Type = N, Weight=1
D. Amphibians Element Type = P, Weight=1 Frog Element Type = N, Weight=1 Salamander Element Type = N, Weight=1
Answer: C
Explanation:
Explanation/Reference:
Explanation:

NEW QUESTION: 2
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. Sqoop import
C. Hive LOAD DATA command
D. Pig LOAD command
E. HDFS command
F. Ingest with Hadoop Streaming
Answer: D
Explanation:
Explanation/Reference:
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: 3
Your company uses Key Management Service (KMS) for Microsoft Volume Activation. You have a Windows 7 image, which will be deployed to the companys client computers. You deploy the image to a client computer as a test. When you log on to the client computer, a warning states that the grace period for activation has expired.
You need to prevent the grace period from expiring before the image is deployed to the client computers.
Which command should you run?
A. slmgr.vbs /cpky
B. Sysprep /audit
C. slmgr.vbs /ckms
D. Sysprep /generalize
Answer: D

NEW QUESTION: 4
Contoso.com에서 관리자로 일합니다. Contoso.com 네트워크는 Contoso.com이라는 단일 도메인으로 구성됩니다. 도메인 컨트롤러를 포함하여 Contoso.com 도메인의 모든 서버에는 Windows Server가 있습니다.
2012 R2가 설치되었습니다.
Contoso.com 도메인의 서버에 대한 컴퓨터 계정을 호스팅하는 ENSUREPASSServ라는 OU (조직 구성 단위)에 새 GPO (그룹 정책 개체)를 만들고 연결했습니다.
Contoso.com 도메인의 모든 서버에서 로컬 그룹에 그룹을 추가하는 작업을 수행했습니다. 그러나이 그룹을 로컬 그룹에서 제거해서는 안됩니다.
다음 중 어떤 조치를 취해야 합니까?
A. 사용자 그룹 추가를 고려해야 합니다.
B. 제한된 그룹 추가를 고려해야 합니다.
C. 서버 그룹 추가를 고려해야 합니다.
D. 글로벌 그룹 추가를 고려해야 합니다.
Answer: B
Explanation:
설명
그룹 정책의 제한된 그룹은 권한이나 그룹 구성원을 중앙에서 모든 도메인 컴퓨터 나 서버에 위임하는 간단한 방법입니다. 제한된 그룹을 사용하면 주어진 계정에 가장 낮은 권한을 적용하는 것이 더 쉽습니다.
컴퓨터 구성 \ Windows 설정 \ 보안 설정 \ 제한된 그룹 제한된 그룹을 통해 관리자는 보안에 민감한 그룹에 대한 두 가지 속성 (즉,
"제한된"그룹). 두 속성은 Members 및 Member Of입니다.
구성원 목록은 제한된 그룹에 속하는 사람과 그렇지 않은 사람을 정의합니다.
소속 그룹 목록은 제한된 그룹이 속하는 다른 그룹을 지정합니다. 제한된 그룹 정책이 시행되면 구성원 목록에없는 제한된 그룹의 현재 구성원이 제거됩니다.