Study with CCD-333 most valid questions & verified answers

Cloudera CCD-333 Exam Overview:

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Cloudera Certified Developer for Apache Hadoop Sample Questions:

1. In the standard word count MapReduce algorithm, why might using a combiner reduce the overall Job running time?

A) Because combiners perform local aggregation of word counts, and then transfer that data to reducers without writing the intermediate data to disk.
B) Because combiners perform local aggregation of word counts, thereby allowing the mappers to process input data faster.
C) Because combiners perform local aggregation of word counts, thereby reducing the number of key-value pairs that need to be snuff let across the network to the reducers.
D) Because combiners perform local aggregation of word counts, thereby reducing the number of mappers that need to run.

2. You have a large dataset of key-value pairs, where the keys are strings, and the values are integers. For each unique key, you want to identify the largest integer. In writing a MapReduce program to accomplish this, can you take advantage of a combiner?

A) No, a combiner would not be useful in this case.
B) Yes, but the number of unique keys must be known in advance.
C) Yes, as long as all the integer values that share the same key fit into memory on each node.
D) Yes.
E) Yes, as long as all the keys fit into memory on each node.

3. You use the hadoop fs -put command to write a 300 MB file using an HDFS block size of 64 MB. Just after this command has finished writing 200 MB of this file, what would another user see when trying to access this file?

A) They would see the current state of the file, up to the last bit written by the command.
B) They would see the content of the file through the last completed block.
C) They would see Hadoop throw an concurrentFileAccessException when they try to access this file.
D) They would see no content until the whole file is written and closed.

4. If you run the word count MapReduce program with m mappers and r reducers, how many output files will you get at the end of the job? And how many key-value pairs will there be in each file? Assume k is the number of unique words in the input files.

A) There will be m files, each with exactly k/m key value pairs.
B) There will be r files, each with approximately k/m key-value pairs.
C) There will be r files, each with exactly k/r key-value pairs.
D) There will be r files, each with approximately k/r key-value pairs.
E) There will be m files, each with approximately k/m key-value pairs.

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

Solutions: