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購買後，立即下載 CCD-410 題庫 (Cloudera Certified Developer for Apache Hadoop (CCDH)): 成功付款後, 我們的體統將自動通過電子郵箱將您已購買的產品發送到您的郵箱。（如果在12小時內未收到，請聯繫我們，注意：不要忘記檢查您的垃圾郵件。）

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最新的 CCDH CCD-410 免費考試真題:

1. You need to create a job that does frequency analysis on input data. You will do this by writing a Mapper that uses TextInputFormat and splits each value (a line of text from an input file) into individual characters. For each one of these characters, you will emit the character as a key and an InputWritable as the value. As this will produce proportionally more intermediate data than input data, which two resources should you expect to be bottlenecks?

A) Processor and RAM
B) Processor and disk I/O
C) Disk I/O and network I/O
D) Processor and network I/O

2. When can a reduce class also serve as a combiner without affecting the output of a MapReduce program?

A) When the signature of the reduce method matches the signature of the combine method.
B) Always. The point of a combiner is to serve as a mini-reducer directly after the map phase to increase performance.
C) When the types of the reduce operation's input key and input value match the types of the reducer's output key and output value and when the reduce operation is both communicative and associative.
D) Always. Code can be reused in Java since it is a polymorphic object-oriented programming language.
E) Never. Combiners and reducers must be implemented separately because they serve different purposes.

3. You write MapReduce job to process 100 files in HDFS. Your MapReduce algorithm uses TextInputFormat: the mapper applies a regular expression over input values and emits key-values pairs with the key consisting of the matching text, and the value containing the filename and byte offset. Determine the difference between setting the number of reduces to one and settings the number of reducers to zero.

A) There is no difference in output between the two settings.
B) With zero reducers, all instances of matching patterns are gathered together in one file on HDFS. With one reducer, instances of matching patterns are stored in multiple files on HDFS.
C) With zero reducers, no reducer runs and the job throws an exception. With one reducer, instances of matching patterns are stored in a single file on HDFS.
D) With zero reducers, instances of matching patterns are stored in multiple files on HDFS. With one reducer, all instances of matching patterns are gathered together in one file on HDFS.

4. 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.

5. Which project gives you a distributed, Scalable, data store that allows you random, realtime read/write access to hundreds of terabytes of data?

A) Hue
B) Pig
C) Flume
D) HBase
E) Oozie
F) Sqoop
G) Hive

問題與答案：