ANU UG/Degree 4th Sem(Y23) Data Visualization Unit Wise Important Questions

 ANU UG/Degree 4th Sem(Y23) Data Visualization  Unit Wise Important Questions are now available, these questions are very important for your semester exams. These questions are prepared by top qualified faculty. Read these questions for good marks.

 UNIT I:
Creating Visual Analytics with tableau desktop, connecting to your data-How to Connect to your data, What are generated Values? Knowing when to use a direct connection, Joining tables with tableau, blending different data sources in a single worksheet

Short Answer Questions

1. What is the purpose of connecting to a data source in Tableau Desktop?
2. Define "generated values" in Tableau.
3. What is a direct connection in Tableau and when might you use it?
4. How does Tableau Desktop join tables from the same data source?
5. What is data blending in Tableau?
6. When would you choose blending over joining tables in Tableau?

Long Answer Questions

1. Explain the process of connecting to your data in Tableau Desktop.
2. Discuss the differences between a direct (live) connection and an extract connection in Tableau.
3. Describe what generated values are in Tableau and provide examples of how they can be used in visual analytics.
4. Outline the steps involved in joining tables in Tableau and the importance of choosing the correct join type.
5. Explain data blending in Tableau, including its advantages and limitations compared to joining tables.
6. Critically analyze how Tableau Desktop’s capabilities in data connection, joining, and blending contribute to effective visual analytics.

 UNIT II:
Building your first Visualization- How Me works- Chart types, Text Tables, Maps, bar chart, Line charts, Area Fill charts and Pie charts, scatter plot, Bullet graph, Gantt charts, Sorting data in tableau, Enhancing Views with filters, sets groups and hierarchies.

Short Answer Questions

1. What is a text table in Tableau and what insights does it provide?
2. How are maps used in Tableau to visualize geographic information?
3. Differentiate between bar charts, line charts, area fill charts, and pie charts in Tableau.
4. What is a scatter plot and how does it help in identifying relationships between variables?
5. Explain the purpose of bullet graphs and Gantt charts in Tableau visualizations.
6. How do filters, sets, groups, hierarchies, and sorting enhance data visualization in Tableau?

Long Answer Questions

1. Explain the process of building your first visualization in Tableau, detailing the steps from data selection to creating the final chart.
2. Discuss the various chart types available in Tableau (including text tables, maps, bar charts, line charts, area fill charts, pie charts, scatter plots, bullet graphs, and Gantt charts) and describe the scenarios in which each type is most effective.
3. Analyze how filters, sets, groups, and hierarchies can be used to enhance a visualization in Tableau, providing examples for each.
4. Describe the methods for sorting data in Tableau and explain how effective sorting can improve the clarity and interpretability of visual analytics.
5. Evaluate the use of Gantt charts in Tableau for project management visualizations. What key elements are necessary to build a Gantt chart, and what insights can it offer?
6. Critically assess how the combined use of different chart types and data enhancement techniques (such as filtering and grouping) in Tableau contributes to effective data storytelling.

 UNIT III:
Creating calculations to enhance your data- What is aggregation, what are calculated values and table calculations, Using the calculation dialog box to create, Building formulas using table calculations, Using table calculation functions.

Short Answer Questions

1. What is aggregation in Tableau, and why is it important in data analysis?
2. Define calculated values in Tableau.
3. What are table calculations in Tableau, and how do they differ from regular calculated fields?
4. How is the Calculation Dialog Box used to create new calculations in Tableau?
5. Name two common table calculation functions in Tableau.
6. In what scenario might you choose to use a table calculation over a standard calculated field?

Long Answer Questions

1. Explain the concept of aggregation in Tableau and discuss its significance in summarizing data for visual analytics.
2. Compare and contrast calculated values with table calculations in Tableau, providing examples of when each is most appropriate.
3. Describe the process of creating a calculated field using the Calculation Dialog Box in Tableau, highlighting key features and functionalities.
4. Discuss how to build formulas using table calculations in Tableau, including an explanation of syntax and the role of functions.
5. Analyze the use of table calculation functions in Tableau (such as WINDOW_SUM and RUNNING_AVG) and explain how they can enhance data analysis.
6. Critically evaluate how the combination of aggregation, calculated values, and table calculations can be used to create more dynamic and insightful visualizations in Tableau.

 UNIT IV:
Using maps to improve insights-Create a Standard Map View, Plotting your own locations on a map, Replace Tableau’s standard maps, Shaping data to enable Point-to-Point mapping.

Short Answer Questions

1. What is a Standard Map View in Tableau, and how is it created?
2. How do you plot your own geographic locations on a Tableau map?
3. What steps are involved in replacing Tableau’s standard maps with a custom map?
4. Define point-to-point mapping in Tableau.
5. What data shaping techniques are necessary to enable point-to-point mapping?
6. How does customizing maps enhance insights in Tableau visualizations?

Long Answer Questions

1. Explain the process of creating a Standard Map View in Tableau, including how to configure geographic roles and customize map settings.
2. Describe the method for plotting your own locations on a Tableau map and discuss the importance of using latitude and longitude coordinates.
3. Discuss the procedures and benefits of replacing Tableau’s standard maps with custom maps, including potential challenges and solutions.
4. Outline the concept of point-to-point mapping in Tableau, detailing the data requirements and steps to achieve it.
5. Analyze the role of data shaping in enabling effective point-to-point mapping and how it contributes to more accurate visualizations.
6. Critically evaluate how using customized maps in Tableau can improve data insights, citing examples of when and why to implement these customizations.

 UNIT V:
Developing an Adhoc analysis environment- generating new data with forecasts, providing self evidence adhoc analysis with parameters, Editing views in tableau Server.

Short Answer Questions

1. What is an ad hoc analysis environment in Tableau, and why is it important for data exploration?
2. How does Tableau generate new data with forecasts, and what are the key components of this process?
3. What role do parameters play in enabling self-evident ad hoc analysis in Tableau?
4. Describe the process of generating forecasts in Tableau and how these forecasts aid in data analysis.
5. How can views be edited in Tableau Server to support ad hoc analysis?
6. What are the benefits of providing self-evident ad hoc analysis using interactive features in Tableau?

Long Answer Questions

1. Explain the concept of developing an ad hoc analysis environment in Tableau, including the benefits of integrating forecasting and interactive parameters.
2. Discuss the process of generating new data with forecasts in Tableau, detailing the steps involved and the importance of forecast accuracy.
3. Describe how parameters are utilized to facilitate self-evident ad hoc analysis in Tableau, and provide examples of their practical application.
4. Outline the methods and best practices for editing views in Tableau Server to enhance ad hoc analysis capabilities.
5. Analyze how forecasting in Tableau contributes to proactive decision-making, including a discussion on the assumptions and limitations of the forecasting models.
6. Critically evaluate how the combination of forecasting, parameters, and view editing in Tableau creates a robust environment for dynamic ad hoc analysis.

ANU UG/Degree B.Sc (Honors) 7th Sem Exam Results 2024

 ANU UG/Degree B.Sc (Honors) 7th Sem Exam Results 2024 are now available, the candidates who are looking for results can check their results from here. The last date for revaluation or recounting is 11.03.2025

 

 

 

 

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 ANU UG/Degree B.Sc (Honors) 7th Sem Exam Results 2024

 

ANU UG/Degree 4th Sem(Y23) Introduction to AI Unit Wise Important Questions

ANU UG/Degree 4th Sem(Y23) Introduction to AI  Unit Wise Important Questions are now available, these questions are very important for your semester exams. These questions are prepared by top qualified faculty. Read these questions for good marks.

 

UNIT – I:
Introduction to AI: What is AI? AI problems, foundation of AI and history of AI intelligent agents: Agents and Environments, the concept of rationality, the nature of environments, structure of agents, problem solving agents, problem formulation.

Short Answer Questions

1. What is artificial intelligence (AI) and how is it defined?
2. What are some of the key problems that AI aims to solve?
3. Summarize the foundational concepts and historical milestones in the development of AI.
4. Define an intelligent agent and explain the relationship between agents and their environments.
5. What is meant by the concept of rationality in intelligent agents, and why is it important?
6. How does problem formulation influence the design of problem-solving agents in AI?

Long Answer Questions

1. Discuss the evolution of artificial intelligence, covering its foundational principles, historical developments, and the impact of these milestones on modern AI applications.
2. Explain the concept of an intelligent agent, detailing its structure, the nature of its environment, and how rationality guides its behavior.
3. Analyze various AI problems and describe the approaches used to address them, providing examples of specific techniques or algorithms.
4. Elaborate on the process of problem formulation in AI. How does the way a problem is formulated affect the strategies employed by problem-solving agents?
5. Compare and contrast different types of environments in which intelligent agents operate, and discuss how these environments influence agent design and decision-making.
6. Evaluate the role of intelligent agents in solving complex problems, highlighting the interplay between agent structure, environmental factors, and the concept of rationality.

 UNIT-II:
Searching: Searching for solutions, uniformed search strategies – Breadth first search, depth first Search. Search with partial information (Heuristic search) Hill climbing, A*, AO* Algorithms, Problem reduction, Game Playing-Adversial search, Games, mini-max algorithm, optimal decisions in multiplayer games, Problem in Game playing, Alpha-Beta pruning, Evaluation functions.

Short Answer Questions

1. What is the concept of searching for solutions in AI, and how do uniformed search strategies differ from heuristic search methods?
2. How does breadth-first search (BFS) work, and what are its primary advantages and disadvantages?
3. What is depth-first search (DFS), and in what scenarios is it most effectively applied?
4. Define heuristic search and explain how hill climbing, A*, and AO* algorithms utilize heuristics.
5. What is the role of problem reduction in search strategies, and how does it simplify complex problems?
6. How does the mini-max algorithm function in adversarial game playing, and what is its significance in optimal decision-making?

Long Answer Questions

1. Discuss the differences between uniformed search strategies (BFS and DFS) and heuristic search methods (hill climbing, A*, AO*) in terms of their operational principles, advantages, and limitations.
2. Explain in detail the working mechanisms of BFS and DFS, including scenarios where each is preferable, and illustrate with examples.
3. Analyze heuristic search techniques, focusing on hill climbing, A*, and AO* algorithms. Explain how heuristics guide these searches and discuss potential pitfalls.
4. Elaborate on the concept of problem reduction in search algorithms. How does breaking down a complex problem into simpler sub-problems facilitate more efficient solution finding?
5. Describe the mini-max algorithm used in game playing, detailing how it evaluates moves in adversarial settings, and discuss the challenges associated with its implementation.
6. Evaluate the role of alpha-beta pruning in optimizing game-playing search algorithms, and explain how evaluation functions contribute to effective decision-making in multiplayer games.

 UNIT-III:
Knowledge representation issues, predicate logic- logic programming, semantic nets- frames and inheritance, constraint propagation, representing knowledge using rules, rules based deduction systems. Reasoning under uncertainty, review of probability, Baye’s probabilistic interferences and dempstershafer theory.

Short Answer Questions

1. What are the key challenges in knowledge representation, and how does predicate logic help address these issues in logic programming?
2. Define semantic nets and explain how they represent relationships between concepts.
3. What are frames and inheritance, and how do they enhance the representation of structured knowledge compared to semantic nets?
4. How does constraint propagation work in knowledge representation, and why is it important in rule-based systems?
5. What is a rules-based deduction system, and what role does it play in automated reasoning?
6. Briefly compare Bayesian probabilistic inference and Dempster-Shafer theory in handling uncertainty.

Long Answer Questions

1. Discuss the major issues in knowledge representation and explain how predicate logic and logic programming provide solutions. Include examples of their application in AI systems.
2. Analyze the use of semantic nets, frames, and inheritance in representing knowledge. Compare their strengths and limitations, and explain scenarios where one might be preferred over the others.
3. Describe the concept of constraint propagation in the context of knowledge representation. How does it facilitate the integration of rules in a deduction system?
4. Explain the role of rules-based deduction systems in AI. Discuss how these systems work, including the process of rule evaluation and the challenges involved in designing efficient rule sets.
5. Provide an overview of reasoning under uncertainty, focusing on the fundamental principles of probability. How does Bayesian inference contribute to decision-making in uncertain environments?
6. Compare and contrast Bayesian probabilistic inference with Dempster-Shafer theory. Discuss their theoretical foundations, practical applications, and the types of uncertainty they are best suited to handle.

 UNIT-IV
First order logic: Inference in first order logic, propositional vs. first order inference, unification & lifts forward chaining, Backward chaining, Resolution, learning from observation Inductive learning, Decision trees, Explanation based learning, Statistical Learning methods, Reinforcement Learning.

Short Answer Questions

1. What is first order logic and how does it differ from propositional logic in terms of inference?
2. Explain the concept of unification and its importance in first order logic inference.
3. What is the difference between forward chaining and backward chaining in the context of first order logic?
4. Define resolution in first order logic and discuss its role in automated reasoning.
5. What is inductive learning, and how does it differ from deductive reasoning?

6. Briefly describe decision trees, explanation-based learning, and one statistical learning method, highlighting their applications.

Long Answer Questions

1. Discuss the process of inference in first order logic, contrasting it with propositional inference. Include an explanation of unification and its role in facilitating inference.
2. Compare and contrast forward chaining and backward chaining techniques in first order logic. Provide examples to illustrate scenarios where each method is most effective.
3. Explain the resolution method in first order logic in detail, and analyze its advantages and limitations in automated theorem proving.
4. Elaborate on learning from observation through inductive learning. Discuss how decision trees and explanation-based learning contribute to this process.
5. Analyze statistical learning methods in the context of first order logic and symbolic AI, highlighting their differences and potential complementarities.
6. Provide an overview of reinforcement learning, describing its core principles and discussing how it can be integrated with logic-based approaches in machine learning.

 UNIT-V:
Expert systems:- Introduction, basic concepts, structure of expert systems, the human element in expert systems how expert systems works, problem areas addressed by expert systems, expert systems success factors, types of expert systems, knowledge engineering, scope of knowledge, difficulties in knowledge acquisition methods of machine learning, selecting an appropriate knowledge acquisition method, societal impacts reasoning in artificial intelligence, inference with rules, with frames: model based reasoning, case based reasoning, explanation & meta knowledge inference with uncertainty.

Short Answer Questions

1. What are expert systems and what are the basic components that constitute an expert system?
2. How do expert systems incorporate the human element in their design and decision-making processes?
3. Describe the typical structure of an expert system and explain how it operates.
4. What problem areas are commonly addressed by expert systems, and what factors contribute to their success?
5. Define knowledge engineering in the context of expert systems and discuss the challenges associated with knowledge acquisition.
6. Briefly explain the different reasoning methods in expert systems, including inference with rules, model-based reasoning, and handling uncertainty.

Long Answer Questions

1. Discuss the introduction and basic concepts of expert systems, outlining their historical evolution, fundamental structure, and the role of the human element in their design.
2. Explain in detail how expert systems work by describing their architecture, key components, and success factors, along with examples of problem areas they address.
3. Analyze the role of knowledge engineering within expert systems. Discuss the scope of knowledge, the difficulties in knowledge acquisition, and the various machine learning methods used for acquiring knowledge.
4. Evaluate the challenges and considerations involved in selecting an appropriate knowledge acquisition method for expert systems, and how these methods impact system performance.
5. Describe the different reasoning methods used in expert systems, such as inference with rules, model-based reasoning with frames, case-based reasoning, and explain how each approach deals with uncertainty.
6. Discuss the societal impacts of expert systems by evaluating their benefits and potential challenges in real-world applications, and how they influence decision-making processes in various domains.