TU Wien:Business Intelligence VU (Tjoa)/Exam 2021-01-11

The exam consisted of two parts. The exam was conducted in TUWEL.

To attend the exam, you had to join a Zoom meeting with two devices, one filming from the front and one from the side/back. Students were let into the meeting one at a time. For 25 students, this process lasted about 1 hour and 10 minutes. Between the parts, there was a short break (bathroom allowed).

The exact grading scheme for the exam and scaling were unknown at the time of taking the exam. Results were expected at the end of January/beginning of February.

Part 1: SC (True-False)

20 true/false questions from a question pool ranging over all slides. Work time 20 minutes. Correct answer: +2 points, False answer: -1 point, No answer: 0 points.

• Ordinal data allows distances
• MapReduce consists in Map/Shift&Sort/Reduce phase
• KNN with even value of K
• IMPALA allows UPDATE & DELETE (?)
• IMPALA symmetric node architecture?
• Drill Down brings from a detailed to an aggregated view
• Something about type of clustering
• Data Mining goal success criteria can not be subjective
• Hadoop is fault tolerant
• Fayadd KDD focuses on Data Mining aspects
• Fayyad KDD focuses on Deployment
• Hadoop is for large streaming reads
• A DWH is a subject-oriented, integrated, time-variant, nonvolatile collection of data
• TDWI working definition of the DWH focuses on data integration and analysis and not on business decision support (something like that)
• In a mediator integration approach everybody talks directly to everybody else
• ETL only needed for integrated DWH
• Single Linkage identifies contiguous clusters well
• Complete Linkage identifies globular clusters well
• Change of a single data point can change Decision Trees dramatically
• A carefully pruned Decision tree usually has lower validation set error than an un-pruned one
• Model for deployment is chosen by lowest training set error
• Cross-validation is used for a more robust estimate for error

Part 2: Open Questions

Work time: 25 minutes.

The questions were selected from a question pool. Two questions each from the Data Warehousing/Data Mining part, meaning 4 questions in total. Average time to answer: 6.25 minutes, which is extremely short even for a TU exam.

Example questions:

Data Warehousing

• Describe OLAP and OLTP and their characteristics
• Describe the ETL process
• Heterogeneity challenges integration
• Star vs Snowflake
• DW vs Lake
• Inmon Approach pros/cons
• Inmon Kimball pros/cons
• Inmon/Kimball
• Describe 2 scaling approaches their adv. and differences

Data Mining

• What is a lazy learner? Example? When to use?
• K-means algorithm, discuss robustness w.r.t. initialization
• 1-n encoding and Binning (Explain, when to use, example)
• KNN
• describe and differences: a)define business criteria b) define business goals c) data mining success criteria d) data mining goals