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2024

i) Which among the following are not among Various Operations in Data Warehousing?​

a) Sticking
b) Dice
c) Drill down
d) Roll up

Answer: a) Sticking

Explanation: Dice, Drill Down, and Roll Up are standard OLAP operations in Data Warehousing. "Sticking" is not a data warehouse operation.

ii) Which of the following is not a basic data mining task?​

a) Spoofing
b) Prediction
c) Classification
d) Clustering

Answer: a) Spoofing

Explanation: Prediction, Classification, and Clustering are common data mining tasks. Spoofing is a cybersecurity attack technique.

iii) Which of the following is a subset of data warehouse focused on a specific functional area?​

a) Data mart
b) Association rules
c) Flat files
d) Database

Answer: a) Data mart

Explanation: A Data Mart is a smaller subset of a Data Warehouse designed for a specific department or business function.

iv) The star schema is composed of ______ fact table.​

a) one
b) two
c) three
d) four

Answer: a) one

Explanation: A Star Schema consists of one central fact table connected to multiple dimension tables.

v) Preprocessing step is used in Data ______​

a) cleaning
b) integration
c) transformation
d) reduction

Answer: a) cleaning

Explanation: Data preprocessing primarily involves cleaning data by handling missing values, noise, and inconsistencies.

vi) Which of the following is true –​

a) The output of KDD is data
b) The output of KDD is Query
c) The output of KDD is Information
d) The output of KDD is useful information

Answer: d) The output of KDD is useful information

Explanation: KDD (Knowledge Discovery in Databases) aims to extract useful and meaningful knowledge from data.

vii) What is the minimum no. of variables/features required to perform clustering?​

a) 0
b) 1
c) 2
d) 3

Answer: b) 1

Explanation: Clustering can be performed even with a single feature by grouping data based on similarity in that feature.

viii) Which of the following algorithm is most sensitive to outliers?​

a) K-means clustering algorithm
b) K-medians clustering algorithm
c) K-modes clustering algorithm
d) K-medoids clustering algorithm

Answer: a) K-means clustering algorithm

Explanation: K-Means uses the mean as the cluster center, which is highly affected by extreme values (outliers).

ix) Which of the following is an unsupervised data mining technique?​

a) Cluster analysis only
b) Regression Analysis only
c) Classification only
d) Both b & c

Answer: a) Cluster analysis only

Explanation: Clustering is an unsupervised technique, whereas Classification and Regression are supervised techniques.

x) Rule based classification algorithms generate ______ rule to perform the classification.​

a) if-then
b) while
c) do while
d) switch

Answer: a) if-then

Explanation: Rule-based classifiers use IF-THEN rules to classify data.

xi) Which of the following is not an issue in data mining?​

a) High dimensionality
b) Shortage of data
c) Overfitting
d) Outliers

Answer: b) Shortage of data

Explanation: High dimensionality, overfitting, and outliers are common data mining challenges. Data mining generally deals with large datasets.

xii) In OLAP, we are interested in ______.​

a) levels only
b) dimensions only
c) measures only
d) measures and dimensions

Answer: d) measures and dimensions

Explanation: OLAP analysis involves both measures (facts) and dimensions.

xiii) Which of the following is not true about scatter plots?​

a) It is used in the case of univariate distribution
b) It is used to identify relationship between attributes
c) It is used to identify clusters
d) It is used to identify outliers

Answer: a) It is used in the case of univariate distribution

Explanation: Scatter plots require two variables and are used to study relationships, clusters, and outliers.

xiv) Point out the wrong statement.​

a) k-means clustering is a method of vector quantization
b) k-means clustering aims to partition n observations into k clusters
c) k-nearest neighbor is same as k-means
d) none of the mentioned

Answer: c) k-nearest neighbor is same as k-means

Explanation: KNN is a supervised classification algorithm, whereas K-Means is an unsupervised clustering algorithm.

xv) ______ is concerned with discovering the model underlying the link structures of the web.​

a) Web content mining
b) Web structure mining
c) Web usage mining
d) All of the above

Answer: b) Web structure mining

Explanation: Web Structure Mining analyzes hyperlinks and relationships among web pages to discover underlying structures.

2025 (Suplimentary exam)

i) Which of the following is used to define the multidimensional model of the data warehouse?​

(a) Tree
(b) Table
(c) Data cube
(d) None of these

Answer: (c) Data cube

Explanation: A Data Cube represents data in multiple dimensions and is the fundamental multidimensional model used in data warehouses.

ii) Which of the following is a subset of data warehouse focused on a specific functional area?​

(a) Clustering
(b) Prediction
(c) Data mart
(d) Database

Answer: (c) Data mart

Explanation: A Data Mart is a department-oriented subset of a data warehouse designed for a specific business function.

iii) What is data mining?​

(a) Storing data
(b) Extracting useful patterns or information from large datasets
(c) Searching data
(d) Sorting data

Answer: (b) Extracting useful patterns or information from large datasets

Explanation: Data mining is the process of discovering meaningful patterns, trends, and knowledge from large datasets.

iv) Which of the following is not an issue in data mining?​

(a) Data shortage
(b) Outliers
(c) Overfitting
(d) High dimensionality

Answer: (a) Data shortage

Explanation: Common data mining issues include outliers, overfitting, and high dimensionality. Data mining generally deals with large volumes of data.

v) Sometimes the data under analysis has too many attributes, some of which may not be useful as per the task being performed. This problem is referred to as-​

(a) Missing values
(b) Curse of dimensionality
(c) Underfitting
(d) Outliers

Answer: (b) Curse of dimensionality

Explanation: As the number of features increases, analysis becomes more difficult and less effective, known as the curse of dimensionality.

vi) Which of the following helps to identify abstracted patterns in unlabelled data?​

(a) Unsupervised learning
(b) Supervised learning
(c) Hybrid learning
(d) Reinforcement learning

Answer: (a) Unsupervised learning

Explanation: Unsupervised learning discovers hidden structures and patterns in unlabeled data.

(a) K-means clustering
(b) Hierarchical clustering
(c) K-medoids clustering
(d) Apriori algorithm

Answer: (d) Apriori algorithm

Explanation: Apriori is a popular algorithm used for mining association rules and frequent itemsets.

viii) PAM is a-​

(a) Regression technique
(b) Clustering technique
(c) Classification technique
(d) Data selection technique

Answer: (b) Clustering technique

Explanation: PAM (Partitioning Around Medoids) is a clustering algorithm similar to K-Medoids.

ix) Where is data warehousing used?​

(a) Decision support system
(b) Transaction system
(c) Logical system
(d) Temporary system

Answer: (a) Decision support system

Explanation: Data warehouses are designed to support decision-making and business intelligence activities.

x) Which of the following helps to infer a model from labelled data?​

(a) Unsupervised learning
(b) Supervised learning
(c) Hybrid learning
(d) Reinforcement learning

Answer: (b) Supervised learning

Explanation: Supervised learning uses labeled data to learn and predict outcomes.

xi) In which of the following case the Naïve Bayes' algorithm does not work well?​

(a) When there is a multiclass classification
(b) When the Naïve assumption is true
(c) When faster classification is required
(d) When there is the case of zero frequency

Answer: (d) When there is the case of zero frequency

Explanation: Naïve Bayes suffers from the zero-frequency problem when a feature value never appears in the training data for a class.

xii) Which of the following statement is not true about Naïve Bayes classifier?​

(a) It cannot be used for binary as well as multiclass classification
(b) It is the most popular choice for text classification problems
(c) It performs well in multiclass prediction as compared to other algorithms
(d) It is one of the fast and easy classification algorithms to predict a class of test datasets

Answer: (a) It cannot be used for binary as well as multiclass classification

Explanation: Naïve Bayes can be used for both binary and multiclass classification problems.

xiii) Binary classification can be done using-​

(a) K-means algorithm
(b) K-medoids algorithm
(c) Apriori algorithm
(d) None of these

Answer: (d) None of these

Explanation: K-Means and K-Medoids are clustering algorithms, while Apriori is an association rule algorithm. None are standard binary classification algorithms.

xiv) Which of the following is required by K-means clustering?​

(a) Number of clusters
(b) Defined distance metric
(c) Initial guess as to cluster centroids
(d) All of these

Answer: (d) All of these

Explanation: K-Means requires the number of clusters (K), a distance measure, and initial centroid positions.

xv) Web mining includes-​

(a) Mining web linkage structures
(b) Mining web contents
(c) Mining web access patterns
(d) All of these

Answer: (d) All of these

Explanation: Web Mining consists of Web Structure Mining, Web Content Mining, and Web Usage Mining.

2025

i) Where is data warehousing used?​

a) Logical system
b) Transaction system
c) Decision support system
d) None of the above

Answer: c) Decision support system

Explanation: Data warehouses are primarily used in Decision Support Systems (DSS) for analysis and decision-making.

ii) Which of the following statement is true regarding classification?​

a) It is a measure of accuracy
b) It is a subdivision of a set
c) It is the task of assigning a classification
d) None of the above

Answer: c) It is the task of assigning a classification

Explanation: Classification is the process of assigning data items to predefined classes or categories.

iii) Which statement is incorrect regarding data cleaning?​

a) correcting the inconsistent data
b) process of data cleaning
c) conversion of the wrong data to the right data
d) All of the above

Answer: b) process of data cleaning

Explanation: Options (a) and (c) describe data cleaning activities. Option (b) is not a meaningful statement about data cleaning and is therefore incorrect.

iv) Among which of the following can query the unstructured textual data?​

a) Information retrieval
b) Information access
c) Information manipulation
d) Information update

Answer: a) Information retrieval

Explanation: Information Retrieval (IR) is used to search and retrieve relevant information from unstructured textual data.

v) Which of the listed below helps to infer a model from labelled data?​

a) Hybrid learning
b) Unsupervised learning
c) Supervised learning
d) Reinforcement learning

Answer: c) Supervised learning

Explanation: Supervised learning uses labeled data to learn a model and make predictions.

vi) Data warehouse deals with which type of data that is never found in the operational environment?​

a) Normalized
b) Informal
c) Summarized
d) Denormalized

Answer: c) Summarized

Explanation: Data warehouses store summarized and aggregated data for analysis, which is generally not present in operational systems.

vii) To detect fraudulent usage of credit cards, the following data mining task should be used:​

a) Feature selection
b) Prediction
c) Outlier analysis
d) All of the above

Answer: c) Outlier analysis

Explanation: Fraudulent transactions are unusual and deviate from normal behavior, making outlier analysis effective for fraud detection.

viii) Which of the following is used to find inherent regularities in data?​

a) Clustering
b) Frequent pattern analysis
c) Regression analysis
d) Outlier analysis

Answer: b) Frequent pattern analysis

Explanation: Frequent pattern analysis discovers recurring relationships and regularities in data.

ix) What is the main purpose of association rule learning?​

a) To predict continuous numerical values.
b) To identify relationships and patterns between items in datasets.
c) To group similar data points into clusters.
d) To classify data into predefined categories.

Answer: b) To identify relationships and patterns between items in datasets.

Explanation: Association rule learning discovers correlations and associations among items in large datasets.

x) What is a key feature of the Apriori algorithm?​

a) It creates a decision tree for classification tasks.
b) It uses a divide-and-conquer approach to mine rules.
c) It generates frequent itemsets using a bottom-up approach.
d) It clusters data into predefined groups.

Answer: c) It generates frequent itemsets using a bottom-up approach.

Explanation: Apriori builds larger frequent itemsets from smaller frequent itemsets iteratively.

xi) Which of the following is true about unsupervised learning?​

a) It uses labelled data to make predictions.
b) It groups data into predefined categories.
c) It identifies hidden patterns or structures in unlabelled data.
d) It requires a target variable for analysis.

Answer: c) It identifies hidden patterns or structures in unlabelled data.

Explanation: Unsupervised learning works on unlabeled data to discover hidden patterns and structures.

xii) What is a key characteristic of K-Means clustering?​

a) It starts with random data points as initial cluster centers.
b) It identifies outliers in the dataset.
c) It builds a hierarchy of clusters.
d) It ensures clusters are non-overlapping by default.

Answer: a) It starts with random data points as initial cluster centers.

Explanation: K-Means begins with randomly selected centroids and iteratively updates them.

xiii) What is the primary goal of classification in supervised learning?​

a) To group similar data points into clusters.
b) To identify outliers in the dataset.
c) To assign data points to predefined categories or classes.
d) To predict continuous numerical values.

Answer: c) To assign data points to predefined categories or classes.

Explanation: Classification predicts the class label of a data instance.

xiv) Which of the following is a key assumption of the Naïve Bayes classifier?​

a) Features are dependent on each other.
b) Features are conditionally independent given the class label.
c) Classes have overlapping boundaries.
d) Data points must be equally distributed across classes.

Answer: b) Features are conditionally independent given the class label.

Explanation: Naïve Bayes assumes that features are independent of one another when the class label is known.

xv) What is the primary goal of web mining?​

a) To improve the visual design of web pages.
b) To discover useful patterns and insights from web data.
c) To optimize web server performance.
d) To automate the creation of web content.

Answer: b) To discover useful patterns and insights from web data.

Explanation: Web mining extracts useful knowledge and patterns from web content, structure, and usage data.