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| Section | Objectives |
|---|---|
| Data Processing Tools | - Hive
|
| Workflow and Scheduling | - Oozie
|
| Data Ingestion and Integration | - Sqoop
|
| Hadoop Ecosystem and Architecture | - Hadoop Distributed File System (HDFS)
|
| Data Formats and Storage | - Serialization formats
|
| Data Processing with MapReduce | - Optimization and debugging
|
1. To process input key-value pairs, your mapper needs to load a 512 MB data file in memory. What is the best way to accomplish this?
A) Place the datafile in the DataCache and read the data into memory in the configure method ofthe mapper.
B) Place the data file in theDistributedCacheand read the data into memory in the configure method of the mapper.
C) Place the data file in theDistributedCacheand read the data into memory in the map method of the mapper.
D) Serialize the data file, insert it in the Jobconf object, and read the data into memory in the configure method of the mapper.
2. Given a Mapper, Reducer, and Driver class packaged into a jar, which is the correct way of submitting the job to the cluster?
A) hadoop jar MyJar.jar MyDriverClass inputdir outputdir
B) hadoop jar class MyJar.jar MyDriverClass inputdir outputdir
C) jar MyJar.jar
D) jar MyJar.jar MyDriverClass inputdir outputdir
3. You need to create a GUI application to help your company's sales people add and edit customer information. Would HDFS be appropriate for this customer information file?
A) Yes, because HDFS is optimized for random access writes.
B) No, because HDFS can only be accessed by MapReduce applications.
C) No, because HDFS is optimized for write-once, streaming access for relatively large files.
D) Yes, because HDFS is optimized for fast retrieval of relatively small amounts of data.
4. Does the MapReduce programming model provide a way for reducers to communicate with each other?
A) Yes, all reducers can communicate with each other by passing information through the jobconf object.
B) Yes, reducers can communicate with each other by dispatching intermediate key value pairs that get shuffled to another reduce
C) Yes, reducers running on the same machine can communicate with each other through shared memory, but not reducers on different machines.
D) No, each reducer runs independently and in isolation.
5. For each intermediate key, each reducer task can emit:
A) As many final key-value pairs as desired, but they must have the same type as the intermediate key-value pairs.
B) One final key-value pair per value associated with the key; no restrictions on the type.
C) 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)
D) 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.
E) One final key value pair per key; no restrictions on the type.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: E |
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