Exam Code: HCE-5210
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NEW QUESTION: 1
You have peering configured as shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Box 1: vNET6 only
Peering status to both VNet1 and Vnet2 are disconnected.
Box 2: delete peering1
Peering to Vnet1 is Enabled but disconnected. We need to update or re-create the remote peering to get it back to Initiated state.
NEW QUESTION: 2
Who is responsible for identifying the appropriate elicitation communication channels?
A. Business sponsor
B. Subject matter expert
C. Business analyst
D. Project manager
NEW QUESTION: 3
A. Option D
B. Option B
C. Option A
D. Option C
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 sorted in ascending order.
B. Keys are presented to a reducer in random 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 reducer in sorted order; values for a given key are not sorted.
Reducer has 3 primary phases:
The Reducer copies the sorted output from each Mapper using HTTP across the network.
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.
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.
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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