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NEW QUESTION: 1
Which statement best describes the data path of intermediate key-value pairs (i.e., output of the mappers)?
A. Intermediate key-value pairs are written to HDFS. Reducers copy the intermediate data to the local disks of the machines running the reduce tasks.
B. Intermediate key-value pairs are written to the local disks of the machines running the map tasks, and are then copied to HDFS. Reducers read the intermediate data from HDFS.
C. Intermediate key-value pairs are written to HDFS. Reducers read the intermediate data from HDFS.
D. Intermediate key-value pairs are written to the local disks of the machines running the map tasks, and then copied to the machine running the reduce tasks.
Answer: D
Explanation:
The mapper output (intermediate data) is stored on the Local file system (NOT HDFS) of each individual mapper nodes. This is typically a temporary directory location which can be setup in config by the hadoop administrator. The intermediate data is cleaned up after the Hadoop Job completes.
Note: *Reducers start copying intermediate key-value pairs from the mappers as soon as they are available. The progress calculation also takes in account the processing of data transfer which is done by reduce process, therefore the reduce progress starts showing up as soon as any intermediate key-value pair for a mapper is available to be transferred to reducer. Though the reducer progress is updated still the programmer defined reduce method is called only after all the mappers have finished.
*Reducer is input the grouped output of a Mapper. In the phase the framework, for each Reducer, fetches the relevant partition of the output of all the Mappers, via HTTP.
*Mapper maps input key/value pairs to a set of intermediate key/value pairs.
Maps are the individual tasks that transform input records into intermediate records. The transformed intermediate records do not need to be of the same type as the input records. A given input pair may map to zero or many output pairs.
*All intermediate values associated with a given output key are subsequently grouped by the framework, and passed to the Reducer(s) to determine the final output.
Reference:Questions & Answers for Hadoop MapReduce developers,Where is the Mapper Output (intermediate kay-value data) stored ?

NEW QUESTION: 2
コマンドsetlog checksumを使用する2つの目的はどれですか？ （2つ選択してください。）
A. ログの変更や改ざんを防ぐため
B. FortiAnalyzerからSFTPサーバーへのログのアップロード中にman-in-the-middle攻撃から保護するため
C. ログ通信を暗号化する
D. 同一のログセットを2番目のログサーバーに送信する
Answer: A,B
Explanation:
Explanation
To prevent the log in the store from being modified, you can add a log checksum by using the config system global command. When the log is split, archived, and the log is uploaded (if the feature is enabled), you can configure the FortiAnalyzer to log the log file hash value, timestamp, and authentication code. This can help defend against man-in-the-middle attacks when uploading log transmission data from the FortiAnalyzer to the SFTP server.

NEW QUESTION: 3
What are the steps required to remove July 2005 data from a partitioned table and replace with it with July 2010 data?
A. 1) Detach partition containing July 2005 data 2) Load July 2010 data into a new table 3) Attach the new table as a new partition in the existing partitioned table 4) Rebind the partitioned table
B. 1) Detach partition containing July 2005 data 2) Load July 2010 data into a new table 3) Attach the new table as a new partition in the existing partitioned table 4) Reorg the partitioned table
C. 1) Detach partition containing July 2005 data 2) Load July 2010 data into a new table 3) Attach the new table as a new partition in the existing partitioned table 4) Check constraint integrity for the partitioned table
D. 1) Detach partition containing July 2005 data 2) Load July 2010 data into a new table 3) Attach the new table as a new partition in the existing partitioned table 4) Update statistics on the partitioned table
Answer: C