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Question 1 of 60 Quiz ID: q1

What is the primary purpose of data analytics according to the lecture?

To store large amounts of data efficiently

To process data to infer patterns, correlations, or models for prediction

To create backup copies of business databases

To design user interfaces for database applications

Question 2 of 60 Quiz ID: q2

Which of the following is NOT mentioned as a common business decision supported by data analytics?

What product to suggest for purchase to individual customers

What products to manufacture in what quantity

How to design the company's organizational structure

What insurance premium to charge

Question 3 of 60 Quiz ID: q3

In the ETL vs ELT approaches, what is the key difference?

ETL is faster than ELT

ELT requires more storage space

ETL transforms data before loading, while ELT loads data before transforming

ELT is only used for small datasets

Question 4 of 60 Quiz ID: q4

Which component is NOT part of the common steps in data analytics mentioned in the lecture?

Gather data from multiple sources into one location

Generate aggregates and reports summarizing data

Design new database schemas from scratch

Build predictive models for decision making

Question 5 of 60 Quiz ID: q5

According to the lecture, what is a key advantage of using predictive models in business?

They eliminate the need for human decision making

They can predict customer likelihood of loan default to make lending decisions

They guarantee 100% accurate predictions

They reduce the cost of data storage

Question 6 of 60 Quiz ID: q6

What is the relationship between machine learning and data mining according to the lecture?

They are completely different fields with no overlap

Machine learning is a subset of data mining

Data mining extends machine learning techniques to run on very large datasets

Data mining is outdated and has been replaced by machine learning

Question 7 of 60 Quiz ID: q7

Which term is mentioned as a synonym for data analytics?

Data warehousing

Business intelligence (BI)

Decision support

Online analytical processing

Question 8 of 60 Quiz ID: q8

What is the primary limitation of data sources that necessitates data warehousing?

They are too slow for real-time queries

They often store only current data, not historical data

They use incompatible hardware

They are too expensive to maintain

Question 9 of 60 Quiz ID: q9

According to the lecture, what is a data warehouse?

A physical storage facility for computer hardware

A repository of information gathered from multiple sources, stored under a unified schema, at a single site

A type of database that only stores current transactions

A software tool for creating data visualizations

Question 10 of 60 Quiz ID: q10

What is a key benefit of data warehousing mentioned in the lecture?

It eliminates the need for backup systems

It shifts decision support query load away from transaction processing systems

It reduces the total amount of data stored

It automatically generates business reports

Question 11 of 60 Quiz ID: q11

In warehouse architecture design, what is the difference between source driven and destination driven approaches?

Source driven is faster than destination driven

Source driven: data sources transmit new information; Destination driven: warehouse requests information

Source driven uses more storage space

Destination driven is more secure

Question 12 of 60 Quiz ID: q12

Why is keeping a warehouse exactly synchronized with data sources often too expensive?

It requires too much storage space

It needs expensive hardware

Methods like two-phase commit are resource-intensive

It requires specialized programming languages

Question 13 of 60 Quiz ID: q13

What is typically acceptable regarding data freshness in data warehouses?

Data must be updated in real-time

Data can be slightly out-of-date

Data should never be more than 1 hour old

Only historical data is acceptable

Question 14 of 60 Quiz ID: q14

Which of the following is NOT mentioned as a warehouse design issue?

Data transformation and cleansing

How to propagate updates

What data to summarize

How to design user interfaces

Question 15 of 60 Quiz ID: q15

What is an example of data cleansing mentioned in the lecture?

Removing old records from the database

Correcting mistakes in addresses and merging address lists from different sources

Converting data from one file format to another

Encrypting sensitive customer information

Question 16 of 60 Quiz ID: q16

Why might raw data be too large to store online in a warehouse?

Raw data contains too many errors

Raw data is always unstructured

Storage and processing limitations make aggregate values more practical

Raw data violates privacy regulations

Question 17 of 60 Quiz ID: q17

What does OLAP stand for and what is its primary characteristic?

Online Analytical Processing; batch processing of large datasets

Online Analytical Processing; interactive analysis with negligible delay

Offline Analytical Processing; processing data during off-peak hours

Optimal Linear Analytical Processing; mathematical optimization of queries

Question 18 of 60 Quiz ID: q18

The example relation used to illustrate OLAP concepts is sales(item_name, color, clothes_size, quantity). What does this represent?

A raw transaction log

A simplified version of the sales fact table joined with dimension tables

A customer relationship management table

An inventory management system

Question 19 of 60 Quiz ID: q19

What is a data cube in the context of OLAP?

A three-dimensional database storage structure

A multidimensional generalization of a cross-tab

A cube-shaped data visualization

A compression algorithm for large datasets

Question 20 of 60 Quiz ID: q20

What is pivoting in OLAP operations?

Rotating the display of a data visualization

Changing the dimensions used in a cross-tab

Creating a backup copy of data

Sorting data in ascending or descending order

Question 21 of 60 Quiz ID: q21

Which OLAP operation involves creating a cross-tab for fixed values only?

Pivoting

Rollup

Slicing

Drill down

Question 22 of 60 Quiz ID: q22

What is the difference between slicing and dicing in OLAP?

Slicing is faster than dicing

Dicing is used when values for multiple dimensions are fixed

Slicing works with numerical data, dicing with categorical data

There is no difference; they are synonymous

Question 23 of 60 Quiz ID: q23

What does rollup accomplish in OLAP operations?

Creates more detailed views of data

Moves from finer-granularity data to coarser granularity

Combines multiple databases into one

Reverses previous operations

Question 24 of 60 Quiz ID: q24

Drill down is described as:

The same operation as rollup

Moving from coarser-granularity data to finer-granularity data

A data storage optimization technique

A method for data backup

Question 25 of 60 Quiz ID: q25

What is the purpose of hierarchies on dimensions in OLAP?

To organize data storage more efficiently

To let dimensions be viewed at different levels of detail

To improve query performance

To reduce data redundancy

Question 26 of 60 Quiz ID: q26

According to the example given, the datetime dimension can be used to aggregate by which of the following?

Hour of day, date, day of week only

Month, quarter, year only

Hour of day, date, day of week, month, quarter, or year

Only by calendar year

Question 27 of 60 Quiz ID: q27

Which of the following is NOT mentioned as a data visualization tool?

Tableau

plotly

Microsoft Excel

Google Charts

Question 28 of 60 Quiz ID: q28

What technology is typically used for frontend data visualization tools?

Java and C++

HTML and JavaScript

Python and R

SQL and PL/SQL

Question 29 of 60 Quiz ID: q29

How is data mining defined in relation to machine learning?

Data mining and machine learning are completely different

Data mining has similar goals to machine learning, but operates on very large volumes of data

Data mining is simpler than machine learning

Data mining only works with structured data

Question 30 of 60 Quiz ID: q30

What does KDD stand for in the context of data mining?

Knowledge Database Development

Key Data Decisions

Knowledge Discovery in Databases

Kernel Density Distribution

Question 31 of 60 Quiz ID: q31

In decision trees, what determines when a node becomes a leaf node?

When the tree reaches a predetermined depth

When all items belong to the same class OR all attributes have been considered

When there are fewer than 10 data points

When the algorithm runs out of memory

Question 32 of 60 Quiz ID: q32

How do you make a prediction using a decision tree?

Calculate the average of all leaf values

Use the most common value in the training data

Traverse tree from top to make a prediction

Apply a mathematical formula to the root node

Question 33 of 60 Quiz ID: q33

In the Bayes theorem formula p(cj|d) = p(d|cj)p(cj)/p(d), what does p(cj|d) represent?

Probability of generating instance d given class cj

Probability of instance d being in class cj

Probability of occurrence of class cj

Probability of instance d occurring

Question 34 of 60 Quiz ID: q34

In the Bayes theorem formula, what does p(d|cj) represent?

Probability of instance d being in class cj

Probability of generating instance d given class cj

Probability of class cj given instance d

Joint probability of d and cj

Question 35 of 60 Quiz ID: q35

What is the main goal of Support Vector Machine (SVM) classifiers?

Find the line that passes through the most data points

Find the maximum margin line that divides classes with maximum distance from nearest points

Find the shortest line that separates the classes

Find multiple lines that intersect at the center of the data

Question 36 of 60 Quiz ID: q36

How do SVMs work in n-dimensions compared to 2 dimensions?

They use multiple lines instead of one

They use a plane instead of a line to divide points

They cannot work in more than 2 dimensions

They require different algorithms entirely

Question 37 of 60 Quiz ID: q37

What are kernel functions in the context of SVMs?

Functions that calculate distances between points

Non-linear transformation functions used before classification

Functions that determine the number of classes

Error measurement functions

Question 38 of 60 Quiz ID: q38

How can SVMs handle N-ary classification (more than 2 classes)?

By using N different kernel functions

By creating N decision trees

By doing N binary classifications (in class i vs. not in class i)

SVMs cannot handle more than 2 classes

Question 39 of 60 Quiz ID: q39

In neural networks, what determines the classification decision?

The number of layers in the network

The input values only

Pick the class with maximum likelihood from output values

The average of all node values

Question 40 of 60 Quiz ID: q40

What are the key components that determine neural network behavior?

The number of input nodes

The weights associated with edges

The activation functions only

The number of output classes

Question 41 of 60 Quiz ID: q41

How does the backpropagation algorithm work?

It processes all training instances simultaneously

Weights are set randomly, then instances are processed one at a time, adjusting weights when classification is wrong

It works backwards from output to input without using training data

It requires manual adjustment of weights by the programmer

Question 42 of 60 Quiz ID: q42

What characterizes deep neural networks?

They use only linear functions

They have a large number of layers with large number of nodes in each layer

They are faster than shallow networks

They require less training data

Question 43 of 60 Quiz ID: q43

What type of neural network architecture is mentioned for image processing?

Recurrent networks

Convolutional networks

Feedforward networks

Adversarial networks

Question 44 of 60 Quiz ID: q44

How does regression differ from classification according to the lecture?

Regression is faster than classification

Regression deals with prediction of a value, rather than a class

Regression only works with numerical data

Regression requires more training data

Question 45 of 60 Quiz ID: q45

What is the goal of linear regression?

To classify data into categories

To infer coefficients for the equation Y = a₀ + a₁X₁ + a₂X₂ + ... + aₙXₙ

To reduce the dimensionality of data

To cluster similar data points

Question 46 of 60 Quiz ID: q46

Why might regression fits only be approximate?

Due to insufficient computational power

Because of noise in the data or because the relationship is not exactly polynomial

Due to limitations in the regression algorithm

Because linear relationships don't exist in real data

Question 47 of 60 Quiz ID: q47

In association rules, what do the left and right hand sides represent?

Input and output variables

Antecedent and consequent

Independent and dependent variables

Cause and correlation

Question 48 of 60 Quiz ID: q48

What is support in association rules?

The computational resources required

A measure of what fraction of the population satisfies both the antecedent and consequent

The confidence level of the rule

The number of transactions in the database

Question 49 of 60 Quiz ID: q49

What does confidence measure in association rules?

The total number of occurrences of the rule

How often the consequent is true when the antecedent is true

The statistical significance of the relationship

The strength of correlation between variables

Question 50 of 60 Quiz ID: q50

If a rule 'bread → milk' has 80% confidence, what does this mean?

80% of all customers buy both bread and milk

80% of purchases that include bread also include milk

80% of purchases that include milk also include bread

There's an 80% chance the rule is correct

Question 51 of 60 Quiz ID: q51

What is the intuitive goal of clustering?

To predict future values

To find clusters of points such that similar points lie in the same cluster

To reduce the size of the dataset

To identify the most important features

Question 52 of 60 Quiz ID: q52

What is a centroid in clustering?

The largest point in a cluster

The first point assigned to a cluster

A point defined by taking average of coordinates in each dimension

The point furthest from other clusters

Question 53 of 60 Quiz ID: q53

What is one approach to formalizing clustering using distance metrics?

Maximize the distance between all points

Group points into k sets such that average distance of points from the centroid of their assigned group is minimized

Ensure each cluster has the same number of points

Create clusters with equal variance

Question 54 of 60 Quiz ID: q54

What is text mining according to the lecture?

A method for compressing text files

Application of data mining to textual documents

A technique for translating text between languages

A way to search for specific words in documents

Question 55 of 60 Quiz ID: q55

What is an example of sentiment analysis mentioned in the lecture?

Translating customer reviews into different languages

Learning to predict if a user review is positive or negative about a product

Counting the number of words in customer feedback

Identifying the demographic of review writers

Question 56 of 60 Quiz ID: q56

What challenge is illustrated by the 'Michael Jordan' example in entity recognition?

Names that are difficult to pronounce

Names that are spelled incorrectly

Names that could refer to different famous people (basketball player vs ML expert)

Names that appear in multiple documents

Question 57 of 60 Quiz ID: q57

According to the lecture, how can knowledge graphs be constructed?

Only through manual data entry

By information extraction from different sources, such as Wikipedia

Only from structured databases

Through social media analysis only

Question 58 of 60 Quiz ID: q58

Which of these is NOT mentioned as a type of mining or analytics technique in the lecture?

Text mining

Sentiment analysis

Image mining

Information extraction

Question 59 of 60 Quiz ID: q59

What is the primary difference between OLTP and data warehouse systems according to the lecture?

OLTP systems are faster

Data warehouses shift decision support query load away from transaction processing systems

OLTP systems store more data

Data warehouses are more secure

Question 60 of 60 Quiz ID: q60

Which statement best summarizes the overall scope of data analytics as presented in the lecture?

Data analytics is only concerned with storing large amounts of data

Data analytics encompasses data warehousing, OLAP, and data mining to support business decision making

Data analytics is limited to statistical analysis of numerical data

Data analytics focuses primarily on database design and optimization

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