At ValidExamDumps, we consistently monitor updates to the Hortonworks HDPCD exam questions by Hortonworks. Whenever our team identifies changes in the exam questions,exam objectives, exam focus areas or in exam requirements, We immediately update our exam questions for both PDF and online practice exams. This commitment ensures our customers always have access to the most current and accurate questions. By preparing with these actual questions, our customers can successfully pass the Hortonworks Data Platform Certified Developer exam on their first attempt without needing additional materials or study guides.
Other certification materials providers often include outdated or removed questions by Hortonworks in their Hortonworks HDPCD exam. These outdated questions lead to customers failing their Hortonworks Data Platform Certified Developer exam. In contrast, we ensure our questions bank includes only precise and up-to-date questions, guaranteeing their presence in your actual exam. Our main priority is your success in the Hortonworks HDPCD exam, not profiting from selling obsolete exam questions in PDF or Online Practice Test.
Review the following data and Pig code:
What command to define B would produce the output (M,62,95l02) when invoking the DUMP operator on B?
Your cluster's HDFS block size in 64MB. You have directory containing 100 plain text files, each of which is 100MB in size. The InputFormat for your job is TextInputFormat. Determine how many Mappers will run?
Each file would be split into two as the block size (64 MB) is less than the file size (100 MB), so 200 mappers would be running.
Note:
If you're not compressing the files then hadoop will process your large files (say 10G), with a number of mappers related to the block size of the file.
Say your block size is 64M, then you will have ~160 mappers processing this 10G file (160*64 ~= 10G). Depending on how CPU intensive your mapper logic is, this might be an
acceptable blocks size, but if you find that your mappers are executing in sub minute times, then you might want to increase the work done by each mapper (by increasing the block size to 128, 256, 512m - the actual size depends on how you intend to process the data).
You want to count the number of occurrences for each unique word in the supplied input dat
a. 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?
Combiners are used to increase the efficiency of a MapReduce program. They are used to aggregate intermediate map output locally on individual mapper outputs. Combiners can help you reduce the amount of data that needs to be transferred across to the reducers. You can use your reducer code as a combiner if the operation performed is commutative and associative. The execution of combiner is not guaranteed, Hadoop may or may not execute a combiner. Also, if required it may execute it more then 1 times. Therefore your MapReduce jobs should not depend on the combiners execution.
You have just executed a MapReduce job. Where is intermediate data written to after being emitted from the Mapper's map method?
The mapper output (intermediate data) is stored on the Local file system (NOT HDFS) of each individual mapper nodes. This is typically a temporary directory location which can be setup in config by the hadoop administrator. The intermediate data is cleaned up after the Hadoop Job completes.
