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Download DemoVendor: Hortonworks
Certifications: HCAHD
Exam Code: HADOOP-PR000007
Exam Name: Hortonworks Certified Apache Hadoop 2.0 Developer (Pig and Hive Developer)
Updated:
Q&As: 108
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VCE
You have the following key-value pairs as output from your Map task:
(the, 1)
(fox, 1)
(faster, 1)
(than, 1)
(the, 1) (dog, 1)
How many keys will be passed to the Reducer's reduce method?
A. Six
B. Five
C. Four
D. Two
E. One
F. Three
Correct Answer: B
Explanation: Only one key value pair will be passed from the two (the, 1) key value pairs.
For each intermediate key, each reducer task can emit:
A. As many final key-value pairs as desired. There are no restrictions on the types of those key-value pairs (i.e., they can be heterogeneous).
B. As many final key-value pairs as desired, but they must have the same type as the intermediate key-value pairs.
C. As many final key-value pairs as desired, as long as all the keys have the same type and all the values have the same type.
D. One final key-value pair per value associated with the key; no restrictions on the type.
E. One final key-value pair per key; no restrictions on the type.
Correct Answer: C
Reference: Hadoop Map-Reduce Tutorial; Yahoo! Hadoop Tutorial, Module 4: MapReduce
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.
Correct 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
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
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