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NEW QUESTION: 1
Which of the following statements are true regarding device management on FortiManager? (Choose two.)
A. FortiGate in transparent mode configurations are not counted toward the device count on FortiManager.
B. FortiGate devices in an HA cluster that has five VDOMs are counted as five separate devices.
C. FortiGate devices in HA cluster devices are counted as a single device.
D. The maximum number of managed devices for each ADOM is 500.
Answer: B,C
NEW QUESTION: 2
Which two next-generation encryption algorithms does Cisco recommend? (Choose two.)
A. DH-1024
B. 3DES
C. MD5
D. AES
E. DES
F. SHA-384
Answer: D,F
Explanation:
The following table shows the relative security level provided by the recommended and NGE algorithms.
The security level is the relative strength of an algorithm. An algorithm with a security level of x bits is
stronger than one of y bits if x > y. If an algorithm has a security level of x bits, the relative effort it would
take to "beat" the algorithm is of the same magnitude of breaking a secure x-bit symmetric key algorithm
(without reduction or other attacks). The 128-bit security level is for sensitive information and the 192-bit
level is for information of higher importance.
Reference: http://www.cisco.com/c/en/us/about/security-center/next-generation-cryptography.html
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. Sqoop import
B. Ingest with Hadoop Streaming
C. Ingest with Flume agents
D. Hive LOAD DATA command
E. HDFS command
F. Pig LOAD command
Answer: F
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
ワイヤレスコントローラで外部RADIUSサーバを設定しています。 どちらの項目をコントローラで定義する必要がありますか？ （2つ選択）
A. server IP address
B. encryption
C. shared secret
D. VLAN
E. SSID
Answer: A,C