Cloudera Certified Developer for Apache Hadoop : CCD-333

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Cloudera CCD-333 Exam Syllabus Topics:

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, thereby reducing the number of mappers that need to run.
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, and then transfer that data to reducers without writing the intermediate data to disk.

2. 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 MapRunner that iterates over all key-value pairs in the entire file.
D) Write a custom FileInputFormat and override the method isSplittable to always return false.

3. What is the preferred way to pass a small number of configuration parameters to a mapper or reducer?

A) As a custom input key-value pair passed to each mapper or reducer.
B) Using a plain text file via the Distributedcache, which each mapper or reducer reads.
C) Through a static variable in the MapReduce driver class (i.e., the class that submits the MapReduce job).
D) As key-value pairs in the jobconf object.

4. Which of the following statements best describes how a large (100 GB) file is stored in HDFS?

A) The file is divided into variable size blocks, which are stored on multiple data nodes. Each block is replicated three times by default.
B) The file is divided into fixed-size blocks, which are stored on multiple datanodes. Each block is replicated three times by default.HDFS guarantees that different blocks from the same file are never on the same datanode.
C) The master copy of the file is stored on a single datanode. The replica copies are divided into fixed-size blocks, which are stored on multiple datanodes.
D) The file is divided into fixed-size blocks, which are stored on multiple datanodes. Each block is replicated three times by default. Multiple blocks from the same file might reside on the same datanode.
E) The file is replicated three times by default. Eachcopy of the file is stored on a separate datanodes.

5. Your cluster has 10 DataNodes, each with a single 1 TB hard drive. You utilize all your disk capacity for HDFS, reserving none for MapReduce. You implement default replication settings. What is the storage capacity of your Hadoop cluster (assuming no compression)?

A) about 10 TB
B) about 3 TB
C) about 11 TB
D) about 5 TB

Solutions: