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AKTU_QP20E290QP | 03-Mar-2021 09:13:51 | 117.55.242.
Printed Page: 1 of 2 Subject Code: KCS
1 | P a g e
Time: 3 Hours Total Marks: 100
Note: 1. Attempt all Sections. If require any missing data; then choose suitably. SECTION A
1. Attempt all questions in brief. 2 x 10 = 20
Q no. Question Marks CO a. What are the different types of data? 2 1 b. Explain decision tree. 2 1 c. Give the full form of RTAP. 2 3 d. List various phases of data analytics lifecycle. 2 1 e. Explain the role of Name Node in Hadoop. 2 5 f. Discuss heartbeat in HDFS. 2 5 g. Differentiate between an RDBMS and Hadoop. 2 5 h. Write names of two visualization tools. 2 4 i. How can you deal with uncertainty? 2 3 j. Data sampling is very crucial for data analytics. Justify the statement. 2 3 SECTION B
2. Attempt any three of the following:
Q no. Question Marks CO a. Explain K-Means algorithms. When would you use k means? State weather the statement “K-Means has an assumption each cluster has a roughly equal number of observations” is true or false. Justify your answer
b. Illustrate and explain the steps involved in Bayesian data analysis. 10 2 c. Suppose that A, B, C, D, E and F are all items. For a particular support threshold, the maximal frequent item sets are {A, B, C} an {D, E}. What is the negative border?
d. Discuss any two techniques used for multivariate analysis. 10 2 e. Design and explain the architecture of data stream model. 10 3 SECTION C
3. Attempt any one part of the following:
Q no. Question Marks CO a. Describe the architecture of HIVE with its features. 10 5 b. Brief about the main components of MapReduce 10 5
4. Attempt any one part of the following:
Q no. Question Marks CO a. Describe any two data sampling techniques. 10 1 b. Explain any one algorithm to count number of distinct elements in a Data stream.
AKTU_QP20E290QP | 03-Mar-2021 09:13:51 | 117.55.242.
Printed Page: 2 of 2 Subject Code: KCS
2 | P a g e
5. Attempt any one part of the following:
Q no. Question Marks CO a. Brief about the working of CLIQUE algorithm. 10 4 b. Cluster the following eight points (with (x, y) representing locations) into three clusters: A1(2, 10), A2(2, 5), A3(8, 4), A4(5, 8), A5(7, 5), A6(6, 4), A7(1, 2), A8(4, 9) Initial cluster centers are A1(2, 10), A4(5, 8) and A7(1, 2). The distance function between two points a = (x1, y1) and b = (x2, y2) is defined as- Ρ (a, b) = |x2 – x1| + |y2 – y1| Use K-Means Algorithm to find the three cluster centers after the second iteration
6. Attempt any one part of the following:
Q no. Question Marks CO a. What is prediction error? State and explain the prediction error in regression and classification with suitable example.
b. Given data = {2, 3, 4, 5, 6, 7; 1, 5, 3, 6, 7, 8}. Compute the principal component using PCA Algorithm.
7. Attempt any one part of the following:
Q no. Question Marks CO a. Develop and explain the data analytics life cycle 10 1 b. Distinguish between supervised and unsupervised learning with example.