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NEW QUESTION: 1
You need to troubleshoot your Best Service Routing (BSR) polling vectors to verify that they are operating as intended.
Which command do you use to do this?
A. list trace trunk
B. list trace vdn
C. monitor bcms trunk
D. monitor bcms hunt group
Answer: B
Explanation:
Explanation/Reference:
Reference: https://downloads.avaya.com/css/P8/documents/100180552 (p.9)
NEW QUESTION: 2
Which Documentum Foundation Service (DFS) would you use to obtain default Data
Dictionary metadata information regarding a repository?
A. Schema Service
B. Object Service
C. Query Service
D. Search Service
Answer: A
NEW QUESTION: 3
展示を参照してください。 R1がloopback0インターフェイスをBGPピアにアドバタイズするために入力するコマンドはどれですか。
A. Network 172.16.1.32 mask 255.255.255.224
B. Network 172.16.1.32 0.0.0.31
C. Network 172.16.1.32 255.255.255.224
D. Network 172.16.1.32 mask 0.0.0.31
E. Network 172.16.1.0 0.0.0.255
F. Network 172.16.1.33 mask 255.255.255.224
Answer: A
NEW QUESTION: 4
How are keys and values presented and passed to the reducers during a standard sort and shuffle phase of MapReduce?
A. Keys are presented to reducer in sorted order; values for a given key are not sorted.
B. Keys are presented to reducer in sorted order; values for a given key are sorted in ascending order.
C. Keys are presented to a reducer in random order; values for a given key are not sorted.
D. Keys are presented to a reducer in random order; values for a given key are sorted in ascending order.
Answer: A
Explanation:
Reducer has 3 primary phases:
1.Shuffle
The Reducer copies the sorted output from each Mapper using HTTP across the network.
2.Sort
The framework merge sorts Reducer inputs by keys (since different Mappers may have output the same key).
The shuffle and sort phases occur simultaneously i.e. while outputs are being fetched they are merged.
SecondarySort
To achieve a secondary sort on the values returned by the value iterator, the application should extend the key with the secondary key and define a grouping comparator. The keys will be sorted using the entire key, but will be grouped using the grouping comparator to decide which keys and values are sent in the same call to reduce.
3. Reduce
In this phase the reduce(Object, Iterable, Context) method is called for each <key, (collection of values)> in the sorted inputs.
The output of the reduce task is typically written to a RecordWriter via TaskInputOutputContext.write(Object, Object).
The output of the Reducer is not re-sorted.
Reference: org.apache.hadoop.mapreduce, Class Reducer<KEYIN,VALUEIN,KEYOUT,VALUEOUT>
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