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NEW QUESTION: 1
What do you have to define in an attribute view to reduce the initial amount of data selected from the data foundation tables?
A. A column marked as "Hidden" in the output structure
B. A dynamic filter value on a measure in the attribute view
C. A static filter value on any column in the data foundation tables
D. A restricted column in the output structure
Answer: C

NEW QUESTION: 2
Assuming default settings, which best describes the order of data provided to a reducer's reduce method:
A. The keys given to a reducer aren't in a predictable order, but the values associated with those keys always are.
B. Neither keys nor values are in any predictable order.
C. Both the keys and values passed to a reducer always appear in sorted order.
D. The keys given to a reducer are in sorted order but the values associated with each key are in no predictable order
Answer: D
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>

NEW QUESTION: 3



A. C
B. B
C. A
D. D
Answer: C