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最新的 CCDH CCD-410 免費考試真題:

1. Your client application submits a MapReduce job to your Hadoop cluster. Identify the Hadoop daemon on which the Hadoop framework will look for an available slot schedule a MapReduce operation.

A) NameNode
B) Secondary NameNode
C) DataNode
D) JobTracker
E) TaskTracker

2. You wrote a map function that throws a runtime exception when it encounters a control character in input data. The input supplied to your mapper contains twelve such characters totals, spread across five file splits. The first four file splits each have two control characters and the last split has four control characters.
Indentify the number of failed task attempts you can expect when you run the job with mapred.max.map.attempts set to 4:

A) You will have five failed task attempts
B) You will have twenty failed task attempts
C) You will have seventeen failed task attempts
D) You will have forty-eight failed task attempts
E) You will have twelve failed task attempts

3. All keys used for intermediate output from mappers must:

A) Implement WritableComparable.
B) Implement a splittable compression algorithm.
C) Override isSplitable.
D) Be a subclass of FileInputFormat.
E) Implement a comparator for speedy sorting.

4. You've written a MapReduce job that will process 500 million input records and generated 500 million key-value pairs. The data is not uniformly distributed. Your MapReduce job will create a significant amount of intermediate data that it needs to transfer between mappers and reduces which is a potential bottleneck. A custom implementation of which interface is most likely to reduce the amount of intermediate data transferred across the network?

A) WritableComparable
B) OutputFormat
C) InputFormat
D) Writable
E) Partitioner
F) Combiner

5. You want to count the number of occurrences for each unique word in the supplied input data. You've decided to implement this by having your mapper tokenize each word and emit a literal value 1, and then have your reducer increment a counter for each literal 1 it receives. After successful implementing this, it occurs to you that you could optimize this by specifying a combiner. Will you be able to reuse your existing Reduces as your combiner in this case and why or why not?

A) No, because the Reducer and Combiner are separate interfaces.
B) Yes, because the sum operation is both associative and commutative and the input and output types to the reduce method match.
C) No, because the sum operation in the reducer is incompatible with the operation of a Combiner.
D) Yes, because Java is a polymorphic object-oriented language and thus reducer code can be reused as a combiner.
E) No, because the Combiner is incompatible with a mapper which doesn't use the same data type for both the key and value.

問題與答案：