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NEW QUESTION: 1
SPECT bone imaging

A. 0
B. 1
C. 2
D. 76977
Answer: C

NEW QUESTION: 2
Which type of attack can be prevented by setting the native VLAN to an unused VLAN?
A. MAC-address spoofing
B. VLAN-hopping attacks
C. CAM-table overflow
D. denial-of-service attacks
Answer: B

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. HDFS command
B. Ingest with Flume agents
C. Ingest with Hadoop Streaming
D. Sqoop import
E. Hive LOAD DATA 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
In your test database:
You are using Recovery Manager (RMAN) to perform incremental backups of your test database

The test database is running in NOARCHIVELOG mode

One of the data files is corrupted

All online redo log files are lost because of a media failure

Which option must you consider in this scenario?
A. Configuring the database in ARCHVIELOG mode and then using incremental backup to recover the database
B. Creating a new test database because the database is not recoverable due to the fact that the database is configured in NOARCHIVELOG mode
C. Using incremental backups to recover the database by using the RECOVER DATABASE NOREDO command and then using the RESETLOGS option to open the database.
D. Using incremental backup to recover the damaged data file and then manually creating the online redo log files
Answer: C
Explanation:
Explanation/Reference:
Explanation:
Example 3-6 Recovering a NOARCHIVELOG Database
You can perform limited recovery of changes to a database running in NOARCHIVELOG mode by applying incremental backups. The incremental backups must be consistent, like all backups of a database run in NOARCHIVELOG mode, so you cannot back up the database when it is open.
Assume that you run database prod in NOARCHIVELOG mode with a recovery catalog. You shut down the database consistently and make a level 0 backup of database prod to tape on Sunday afternoon. You shut down the database consistently and make a level 1 differential incremental backup to tape at 3:00 a.m. on Wednesday and Friday.
On Saturday, a media failure destroys half the data files and the online redo logs. Because the online logs are lost, you must specify the NOREDO option in the RECOVER command. Otherwise, RMAN searches for the redo logs after applying the Friday incremental backup and issues an error message when it does not find them.
After connecting RMAN to prod and the catalog database, recover as follows:
STARTUP FORCE NOMOUNT;
RESTORE CONTROLFILE; # restore control file from consistent backup
ALTER DATABASE MOUNT;
RESTORE DATABASE; # restore data files from consistent backup
RECOVER DATABASE NOREDO; # specify NOREDO because online redo logs are lost ALTER DATABASE OPEN RESETLOGS; The recovered database reflects only changes up through the time of the Friday incremental backup.
Because there are no archived redo log files, there is no way to recover changes made after the incremental backup.