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Cloudera Certified Developer for Apache Hadoop Sample Questions:

1. In a MapReduce job, you want each of you input files processed by a single map task. How do you configure a MapReduce job so that a single map task processes each input file regardless of how many blocks the input file occupies?

A) Increase the parameter that controls minimum split size in the job configuration.
B) Set the number of mappers equal to the number of input files you want to process.
C) Write a custom FileInputFormat and override the method isSplittable to always return false.
D) Write a custom MapRunner that iterates over all key-value pairs in the entire file.

2. 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) Reducer <Text, IntWritable, Text, IntWritable>
B) Combiner <Text, Text, IntWritable, IntWritable>
C) Combiner <Text, IntWritable, Text, IntWritable>
D) Mapper <Text, IntWritable, Text, IntWritable>
E) Reducer <Text, Text, IntWritable, IntWritable>

3. Which of the following best describes the workings of TextInputFormat?

A) Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReader of the split that contains the end of the broken line.
B) The input file is split exactly at the line breaks, so each Record Reader will read a series of complete lines.
C) Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReaders of both splits containing the broken line.
D) Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReader of the split that contains the beginning of the broken line.
E) Input file splits may cross line breaks. A line that crosses tile splits is ignored.

4. In a MapReduce job with 500 map tasks, how many map task attempts will there be?

A) Between 500 and 1000.
B) Exactly 500.
C) At least 500.
D) At most 500.
E) It depends on the number of reducers in the job.

5. 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 data file in theDistributedCacheand read the data into memory in the map method of the mapper.
B) Place the datafile in the DataCache and read the data into memory in the configure method ofthe mapper.
C) Serialize the data file, insert it in the Jobconf object, and read the data into memory in the configure method of the mapper.
D) Place the data file in theDistributedCacheand read the data into memory in the configure method of the mapper.

Solutions: