Hadoop Quiz
Settings
.
- 1.Data locality is considered when scheduling
- A.
Job tracker
- B.
Map task
- C.
Reduce task
- D.
Task tracker
- 2.Task scheduling is handled by
- A.
Reduce task
- B.
Task tracker
- C.
Map task
- D.
Job tracker
- 3.Input splits created by
- A.
Driver program
- B.
Job tracker
- C.
Map task
- D.
Reduce task
- 4.When is the earliest point at which the reduce method of a given Reducer can be called?
- A.
As soon as at least one mapper has finished processing its input split.
- B.
As soon as a mapper has emitted at least one record.
- C.
Not until all mappers have finished processing all records.
- D.
It depends on the InputFormat used for the job.
- 5.Which describes how a client reads a file from HDFS?
- A.
The client queries the NameNode for the block location(s). The NameNode returns the block location(s) to the client. The client reads the data directory off the DataNode(s).
- B.
The client queries all DataNodes in parallel. The DataNode that contains the requested data responds directly to the client. The client reads the data directly off the DataNode.
- C.
The client contacts the NameNode for the block location(s). The NameNode then queries the DataNodes for block locations. The DataNodes respond to the NameNode, and the NameNode redirects the client to the DataNode that holds the requested data block(s). The client then reads the data directly off the DataNode.
- D.
The client contacts the NameNode for the block location(s). The NameNode contacts the DataNode that holds the requested data block. Data is transferred from the DataNode to the NameNode, and then from the NameNode to the client.
- 6.You are developing a combiner that takes as input Text keys, IntWritable values, and emits Text keys, IntWritable values. Which interface should your class implement?
- A.
Combiner (Text, IntWritable, Text, IntWritable)
- B.
Mapper (Text, IntWritable, Text, IntWritable)
- C.
Reducer (Text, Text, IntWritable, IntWritable)
- D.
Combiner (Text, Text, IntWritable, IntWritable)
- 7.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.
- 8.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.
Both the keys and values passed to a reducer always appear in sorted order.
- C.
Neither keys nor values are in any predictable order.
- D.
The keys given to a reducer are in sorted order but the values associated with each key are in no predictable order
- 9.You’ve built a MapReduce job that denormalizes a very large table, resulting in an extremely large amount of output data. Which two cluster resources will your job stress? (Choose two).
- A.
Processor
- B.
RAM
- C.
Network I/O
- D.
Disk I/O
- 10.In the execution of a MapReduce job, where does the Mapper place the intermediate data of each Map task?
- A.
The Hadoop framework hold the intermediate data in the TaskTracker's memory
- B.
The Mapper transfers the intermediate data to the JobTracker, which then sends it to the Reducers
- C.
The Mapper stores the intermediate data on the underlying filesystem of the local disk of the machine which ran Map task
- D.
The Mapper transfers the intermediate data to the reducers as soon as it is generated by the Map task
Back to top