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NEW QUESTION: 1
在一台 MSR 路由器上看到路由表如下:
Destination/Mask Proto Pre Cost NextHop Interface
6.6.6.0/24 Static 60 0 100.1.1.1 GE0/0
8.8.8.8/32 Direct 0 0 127.0.0.1 InLoop0
20.1.1.0/24 Static 60 0 100.1.1.1 GE0/0
30.0.0.0/8 RIP 100 1 100.1.1.1 GE0/0
那么对此路由表的分析正确的是 ______ 。
A. 该路由器上接口 GE0/0 的 IP 地址为 100.1.1.1
B. 目的网段为 8.8.8.8/32 的路由下一跳接口为 InLoop0 ,说明该路由下一跳是类似于 Null0 的虚接口,该路由属于黑洞路由
C. 该路由表不是该路由器的完整路由表,完整的路由表至少应该有接口 GE0/0 的直连网段路由
D. 该路由器运行的是 RIPv1 ,因为目的网段 30.0.0.0 的掩码是自然掩码
Answer: C
NEW QUESTION: 2
What are the two policy types that can use a web reputation profile to perform reputation-based processing?
(Choose two.)
A. encryption policies
B. profile policies
C. access policies
D. decryption policies
Answer: C,D
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. Pig LOAD command
B. Ingest with Hadoop Streaming
C. Hive LOAD DATA command
D. Ingest with Flume agents
E. HDFS command
F. Sqoop import
Answer: A
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
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some questions sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a 3-TB database. The database server has 64 CPU cores.
You plan to migrate the database to Microsoft Azure SQL Database.
You need to select the service tier for the Azure SQL database. The solution must meet or exceed the current processing capacity.
Solution: You select the Basic service tier.
Does this meet the goal?
A. No
B. Yes
Answer: A
Explanation:
Explanation
Premium service is required for 3 TB of storage.
Single database DTU and storage limits
References: https://docs.microsoft.com/en-us/azure/sql-database/sql-database-service-tiers-dtu
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