Write the unit test case to execute it as a user with RequestPostProcessor for the URL. pattern "/" which returns model attribute with key as "message" and value as "Hello World".

Explain how CRUD operations are mapped to SQL. statements, with suitable example.

Explain how testing of custom login form can be performed with the help of an example.

Explain process of creating records using Spring Boot and Hibernate.

What are the Strut2 core components? Explain the working and flow of Struts 2 with the help of suitable diagram.

What do you mean by JDBC? Explain how we retrieve data from database using suitable JSP program.

Explain the various components of JSP with suitable code.

Explain the advantages of Java Server Pages over the servlet. Also, write a JSP program for Fibonacci Series.

Briefly explain servlet life cycle. Also, explain the request and response in the context of HTTP?

What is Servlet interface? Differentiate between GenericServlet and HTTPServlet?

What is the difference between JAR and WAR files? Describe the process of creation, deployment and extraction of WAR files.

What is need of design pattern? Explain the use of Repository Design Pattern with the help of an example.

Analyze the role of feature selection and dimensionality reduction in data mining. Discuss techniques such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and feature selection algorithms. Explain how these techniques help in improving model performance and reducing computational complexity.

Examine the role of association rule mining in data mining. Describe the Apriori algorithm and its variations. Discuss the challenges associated with association rule mining, such as the generation of large numbers of rules and the need for efficient computation.

Evaluate the different clustering techniques, including K-means, hierarchical clustering and DBSCAN. Explain the underlying principles of each technique, and discuss their advantages, limitations, and practical applications.

Compare and contrast the various classification algorithms used in data mining, such as Decision Trees, Naive Bayes, Support Vector Machines, and Neural Networks. Discuss the strengths and weaknesses of each algorithm and provide examples of appropriate use cases for each.

Analyze various data pre-processing techniques such as data cleaning, data integration, data transformation, and data reduction. Explain the significance of each technique in improving the quality of data for mining and provide examples of scenarios where each technique would be applied.

Evaluate the role of data warehousing in supporting business intelligence and analytics. Discuss the process of transforming raw data into actionable insights. Provide examples of business intelligence tools and techniques that leverage data warehousing to enhance decision-making processes.

Explain the use of metadata in data warehousing. Discuss the different types of metadata and their roles. Provide examples of how metadata can enhance the usability, maintenance, and performance of a data warehouse.

Design a data warehouse schema for a retail company. Include fact tables, dimension tables, and consider the star schema and snowflake schema designs. Justify your design choices and discuss how your schema supports efficient query processing and business intelligence needs.