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Data Warehousing And Data Mining : MCQs & PYQs with Explanation

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Data Warehousing And Data Mining : Solved MCQs and PYQs

This section contains carefully selected MCQs and Previous Year Questions with explanations to help students understand concepts and prepare effectively for examinations, interviews, and competitive tests.

Topics Covered
Data Warehousing Data Warehouse Architecture ETL Data Integration Data Cleaning Data Transformation Data Marts OLTP OLAP OLAP Operations Star Schema Snowflake Schema Fact And Dimension Tables Data Mining Fundamentals Data Mining Techniques Association Rules Classification Clustering

Q: 1Which of following is not a data classification technique?

A

Bayesian belief networks

B

Support Vector Machine

C

KNN (K-Nearest Neighbours)

D

Principal component analysis

Option D

Data classification is a type of supervised learning where the goal is to assign data points to predefined classes based on training data.

TECHNIQUE NAMEDESCRIPTION
Bayesian Belief NetworksProbabilistic models that classify data based on conditional dependencies between variables; useful for uncertain or probabilistic scenarios.
Support Vector Machine (SVM)Finds the optimal hyperplane that separates data points into different classes, works well for high-dimensional data.
K-Nearest Neighbors (KNN)Classifies a data point based on the majority class of its nearest neighbors in the feature space, simple and intuitive.
Decision TreesBuilds a tree-like model of decisions and their possible consequences to classify data, interpretable and widely used.
Random ForestAn ensemble of decision trees that improves classification accuracy by aggregating multiple trees' predictions.
Neural NetworksModel’s complex relationships using layers of interconnected nodes, suitable for large and complex datasets.

Principal Component Analysis (PCA), is not a classification technique. PCA is a dimensionality reduction technique used to reduce the number of features while preserving variance.

Q: 2What is the total number of non-empty subsets of a 100-item frequent itemset?

A

100

B

2100

C

2100 – 1

D

100!

Option C

For a set containing n items, the total number of subsets is given by 2n. This includes the empty set. To find the number of non-empty subsets, we subtract the empty set, i.e., 2n−1. 
Here, the itemset has 100 items, so the total number of non-empty subsets is 2100−1.

Q: 3Which of the following is not a data mining technique?

A

Link Analysis

B

Database Segmentation

C

Predictive Modeling

D

Evaluation

Option D

Data Mining is the process of extracting useful patterns, relationships, and knowledge from large amounts of data. Different techniques are used in data mining to analyze and predict information.
Evaluation is not considered a standard data mining technique. It is generally a process used to measure or assess the performance of a model or system.

Q: 4Which of the following statement is incorrect?

A

OLTP adopts Entity-Relationship model

B

OLAP adopts star or snowflake model

C

OLTP consists of read-only operations

D

OLTP consists of short, atomic transactions

Option C

OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) serve different purposes in database systems:
OLTP:

  • Focuses on day-to-day transactional operations such as insert, update, and delete.
  • Uses Entity-Relationship (ER) models to represent data.
  • Consists of short, atomic transactions to ensure consistency and quick response times.
  • Not limited to read-only operations, in fact, OLTP involves frequent updates.

OLAP:

  • Optimized for analysis and querying large volumes of data.
  • Uses star or snowflake schemas for multidimensional data modeling.
  • Operations are mostly read-only, like slicing, dicing, and aggregation.

Q: 5Which of the following is not a data mining language?

A

R Language

B

Weka

C

Python

D

BASIC

Option D

Data Mining languages and tools are used to analyze large amounts of data, discover patterns, and perform machine learning or statistical operations.

  • R Language is widely used for statistical analysis, data mining, machine learning, and data visualization.
  • Weka is a popular data mining and machine learning tool used for classification, clustering, and prediction.
  • Python is commonly used in data mining because it provides powerful libraries such as NumPy, Pandas, Scikit-learn etc.

However, BASIC (Beginners All-Purpose Symbolic Instruction Code) is a general-purpose programming language and is not commonly used as a data mining language or tool.

Q: 6In the context of data warehousing, the semantic heterogeneity and structure of data, are challenges in which of following?

A

Data reduction

B

Data integration

C

Data cleaning

D

Data transformation

Option B

In a Data Warehouse, data comes from multiple heterogeneous sources, such as different databases, formats, and structures. The process of combining this data into a single, consistent view is known as Data Integration.

Q: 7In context of mining descriptive statistical measures of data, which of the following sets represents measures of the central tendency and measures of the dispersion of data respectively?

A

{Mean, Mode, Range}, {Median, Variance, Standard Deviation}

B

{Mean, Mode, Median}, {Range, Variance, Standard Deviation}

C

{Median, Variance, Standard Deviation}, {Mean, Mode, Range}

D

{Mean, Range, Variance}, {Mode, Median, Standard Deviation}

Option B

In descriptive statistics, data is analyzed using two main types of measures, measures of central tendency and measures of dispersion.
Measures of Central Tendency describe the center or average of a data set. They indicate where most of the data values lie.

  • Mean (Arithmetic Average).
  • Median (Middle Value).
  • Mode (Most Frequent Value).

Measures of Dispersion describe the spread or variability of data. They show how much the data values differ from the central value. Common examples include:

  • Range.
  • Variance.
  • Standard.

Q: 8In context of multidimensional data models for a data warehouse, the fact table contains:

A

Names of the facts as well as keys to each of the related dimension tables.

B

List of dimensions.

C

List of users or expert.

D

Fact table is abstract and it remains empty.

Option A

In a multidimensional data model used in a data warehouse, data is organized into fact tables and dimension tables.
The Fact Table is the central table that contains quantitative data, also called measures or facts such as sales amount, quantity, or profit.
In addition to the facts, the fact table contains foreign keys that link it to the associated dimension tables.

Dimension Tables provide descriptive context for the facts, such as time, product, customer, or location.

Q: 9If a decision tree classifier keeps expanding until every training instance is correctly classified but test error rate begin to increase what is the most likely outcome?

A

Underfitting

B

Generalization

C

Overfitting

D

Cross-validation error minimized

Option C

When a decision tree is grown to perfectly classify every training instance, it may start capturing noise and random fluctuations in the training data rather than just the underlying patterns. This results in a phenomenon called overfitting, where the model performs exceptionally well on the training set but fails to generalize to new, unseen data.

Q: 10In data warehouse technology, a multiple dimensional view can be implemented using different OLAP storage models. Which of the following correctly distinguishes between ROLAP, MOLAP, and HOLAP?

A

ROLAP uses multidimensional arrays.
MOLAP uses relational tables.
HOLAP uses only materialized views.

B

ROLAP use a relational or extended-relational DBMS to store and manage warehouse data.
MOLAP uses array-based multidimensional storage engines.
HOLAP combines ROLAP and MOLAP technology.

C

ROLAP is faster than MOLAP in indexing of summarized data.
MOLAP supports sparse data better than ROLAP.
HOLAP doesn't support drill-down operations.

D

ROLAP can only co support numeric data types.
MOLAP supports single-level storage.
HOLAP requires columnar storage engines.

Option B

In Data Warehouse technology, OLAP (Online Analytical Processing) systems provide multidimensional views of data for fast analysis. There are three main OLAP storage models.

OLAP TYPESTORAGEDATA REPRESENTATIONADVANTAGESREMARK
ROLAP (Relational OLAP)Relational or extended-relational databasesData stored in tables;. Multidimensional views generated using SQL.Scales well for large datasets, supports detailed dataSlower query performance on aggregated data.
MOLAP (Multidimensional OLAP)Specialized multidimensional storage engines.Data stored in arrays / cubes.Fast query performance, efficient aggregation, and summary.Handles sparse data well using compression.
HOLAP (Hybrid OLAP)Combines relational tables and multidimensional cubes.Detailed data in ROLAP, aggregated data in MOLAP.Balances storage efficiency and query performance.Provides both scalability and speed.

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