Answer the following questions
1. What is Data Mining?
2. What is Data Warehousing?
3. Explain Spark.
4. What is the application of Artificial Intelligence in the healthcare industry?
5. Explain the Advantages and Disadvantages of Data
Mining.
6. What are the Advantages and Disadvantages of
Artificial Intelligence?
7. Define Search strategy
8. What is AI? Explain its applications
9. State the things required to be considered when
we want to build an Al system that is used to solve a particular problem.
10. Which are the techniques of AI?
11. State 4 components using which problem can
be well formulated.
12. Give state-space representation for the
"Water Jug Problem"
13. Explain the production system in detail.
14. Discuss problem characteristics in detail.
15. Give state-space representation for "Tower
of Hanol Problem".
16. What are the two advantages of Depth First
Search?
17. Write down the algorithm of the Best-First
Search Algorithm.
18. Write Hill Climbing Algorithm.
19. Write down the algorithm of Generate and
Test.
20. What are the advantages and disadvantages of
Iterative Deepening Search?
21. State in which two failure conditions where
depth limited search terminated?
22. What are the advantages and disadvantages of
Uniform Cost Search?
23. Write down the algorithm of Breadth-First
Search with its advantages.
24. What are OLTP and OLAP database systems?
25. List the major steps involved in the ETL
process
26. What is a data warehouse?
27. What is the major difference between the star
schema and the schema?
28. Describe the applications of a data warehouse.
29. What are the major differences between OLTP and
OLAP systems?
30. Explain the star scheme technique of modeling a
data warehouse.
31. Explain a multidimensional view and a data
cube.
32. Define Data Mining.
33. What are the types of data?
34. Compare descriptive and predictive data mining.
35. What is prediction?
36. Why do we need data pre-processing?
37. What is Data integration?
38. What is Data Cleaning? Describe various methods
of Data Cleaning.
39. Discuss about the major issues in Data Mining.
40. Explain various accuracy measures in the data
mining.
41. Describe techniques of Data Mining?
42. Differentiate between Data Verification and
Discovery.
43. Explain FP-tree Algorithm.
44. What is spark?
45. What are the features of spark?
46. What is RDD?
47. What are the data formats supported by Spark?
48. Do you need to install Spark on all nodes of
the YARN cluster?
49. Define functions of SparkCore.
50. How is Apache Spark different from
MapReduce?
Define the terms.
1. Data Mining
2. Data Warehousing
3. Spark
4. Artificial Intelligence
5. Problem Space
6. Ignorable Problem
7. Recoverable Problem
8. Irrecoverable Problem
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