Free Hortonworks HDPCD Exam Actual Questions

The questions for HDPCD were last updated On Mar 31, 2025

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Other certification materials providers often include outdated or removed questions by Hortonworks in their Hortonworks HDPCD exam. These outdated questions lead to customers failing their Hortonworks Data Platform Certified Developer exam.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.

 

Question No. 1

What types of algorithms are difficult to express in MapReduce v1 (MRv1)?

Show Answer Hide Answer
Correct Answer: C

See 3) below.

Limitations of Mapreduce -- where not to use Mapreduce

While very powerful and applicable to a wide variety of problems, MapReduce is not the answer to every problem. Here are some problems I found where MapReudce is not suited and some papers that address the limitations of MapReuce.

1. Computation depends on previously computed values

If the computation of a value depends on previously computed values, then MapReduce cannot be used. One good example is the Fibonacci series where each value is summation of the previous two values. i.e., f(k+2) = f(k+1) + f(k). Also, if the data set is small enough to be computed on a single machine, then it is better to do it as a single reduce(map(data)) operation rather than going through the entire map reduce process.

2. Full-text indexing or ad hoc searching

The index generated in the Map step is one dimensional, and the Reduce step must not generate a large amount of data or there will be a serious performance degradation. For example, CouchDB's MapReduce may not be a good fit for full-text indexing or ad hoc searching. This is a problem better suited for a tool such as Lucene.

3. Algorithms depend on shared global state

Solutions to many interesting problems in text processing do not require global synchronization. As a result, they can be expressed naturally in MapReduce, since map and reduce tasks run independently and in isolation. However, there are many examples of algorithms that depend crucially on the existence of shared global state during processing, making them difficult to implement in MapReduce (since the single opportunity for global synchronization in MapReduce is the barrier between the map and reduce phases of processing)


Question No. 2

Which two of the following statements are true about Pig's approach toward data? Choose 2 answers

Show Answer Hide Answer
Correct Answer: B, E

Question No. 3

Which TWO of the following statements are true regarding Hive? Choose 2 answers

Show Answer Hide Answer
Correct Answer: A, C

Question No. 4

You want to Ingest log files Into HDFS, which tool would you use?

Show Answer Hide Answer
Correct Answer: B

Question No. 5

Given a directory of files with the following structure: line number, tab character, string:

Example:

1 abialkjfjkaoasdfjksdlkjhqweroij

2 kadfjhuwqounahagtnbvaswslmnbfgy

3 kjfteiomndscxeqalkzhtopedkfsikj

You want to send each line as one record to your Mapper. Which InputFormat should you use to complete the line: conf.setInputFormat (____.class) ; ?

Show Answer Hide Answer