Hadoop Mahout MCQs
Hadoop Mahout MCQs : This section focuses on "Mahout" in Hadoop. These Multiple Choice Questions (MCQ) should be practiced to improve the hadoop skills required for various interviews (campus interviews, walk-in interviews, company interviews), placements, entrance exams and other competitive examinations.
1. Which of the following is true about mahout?
A. A mahout is one who drives an elephant as its master.
B. Apache Mahout is an open source project that is primarily used for creating scalable machine learning algorithms
C. Mahout lets applications to analyze large sets of data effectively and in quick time.
D. All of the above
View Answer
Ans : D
Explanation: All the above statement is true.
2. In which year Apache Mahout started?
A. 2007
B. 2008
C. 2009
D. 2010
View Answer
Ans : B
Explanation: Apache Mahout started as a sub-project of Apache's Lucene in 2008.
3. Mahout provides ____________ libraries for common and primitive Java collections.
A. python
B. perl
C. java
D. C
View Answer
Ans : C
Explanation: Maths operations are focused on linear algebra and statistics.
4. Which of the following is not a features of Mahout?
A. Mahout lets applications to analyze large sets of data effectively and in quick time.
B. Mahout includes matrix and vector libraries.
C. Mahout comes with distributed fitness function capabilities for evolutionary programming.
D. All of the above
View Answer
Ans : D
Explanation: All of the above is feature of Mahout.
5. Mahout provides an implementation of a ______________ identification algorithm which scores collocations using log-likelihood ratio.
A. collocation
B. compaction
C. collection
D. None of the above
View Answer
Ans : A
Explanation: The log-likelihood score indicates the relative usefulness of a collocation with regards other term combinations in the text.
6. Point out the wrong statement.
A. 'Taste' collaborative-filtering recommender component of Mahout was originally a separate project and can run standalone without Hadoop
B. Integration of Mahout with initiatives such as the Pregel-like Giraph are actively under discussion
C. Calculating the LLR is very straightforward
D. None of the above
View Answer
Ans : D
Explanation: There are a couple ways to run the llr-based collocation algorithm in mahout.
7. ____________ generates NGrams and counts frequencies for ngrams, head and tail subgrams.
A. CollocationDriver
B. CollocDriver
C. CarDriver
D. All of the above
View Answer
Ans : B
Explanation: Each call to the mapper passes in the full set of tokens for the corresponding document using a StringTuple.
8. ________ phase merges the counts for unique ngrams or ngram fragments across multiple documents.
A. CollocCombiner
B. CollocReducer
C. CollocMerger
D. None of the above
View Answer
Ans : A
Explanation: The combiner treats the entire GramKey as the key and as such, identical tuples from separate documents are passed into a single call to the combiner's reduce method, their frequencies are summed and a single tuple is passed out via the collector.
9. How many classification does Naive Bayes Classifier have?
A. 1
B. 2
C. 3
D. 4
View Answer
Ans : B
Explanation: Mahout uses the Naive Bayes classifier algorithm. It uses two implementations: Distributed Naive Bayes classification and Complementary Naive Bayes classification
10. How many algorithms mahout supports for clustering?
A. 1
B. 2
C. 3
D. 4
View Answer
Ans : B
Explanation: Mahout supports two main algorithms for clustering namely: Canopy clustering and K-means clustering
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