MCS 224 Solved Guess Paper from Unnati Educations is a 16-page exam file carrying 15 high-probability descriptive questions for Artificial Intelligence and Machine Learning, every one of them answered in full. It is built for learners sitting the December 2026 Term-End Examination of the Master of Computer Applications (MCA New) Semester III, and for anyone re-appearing in this paper after a back attempt.
Five complete questions and answers from the file are printed further down this page. Read them first. If the depth and the language suit the way you write in the examination hall, the remaining 10 questions with answers are one WhatsApp message away.
MCS 224 key facts at a glance
- Course code and title: MCS 224, Artificial Intelligence and Machine Learning
- Programme: Master of Computer Applications (MCA New), Semester III
- Credits: 4 credits, classed as a Theory course
- Course material: 4 blocks and 16 units
- Term-End paper: 3 hours, 100 maximum marks, 70 per cent weightage
- Guess paper size: 15 questions and 16 pages
- Shown on this page: 5 full solutions, so 10 remain in the paid file
- Sessions studied: 6 Term-End papers from June 2023 to December 2025
- Counselling sessions prescribed: 6 theory sessions of 2 hours each
What you get in this MCS 224 Solved Guess Paper for Artificial Intelligence and Machine Learning
You get 15 descriptive questions with complete model answers across 16 pages, mapped block by block and marked HIGH or MEDIUM by likelihood. Nothing in the file is a hint or a bare heading. Every question carries the answer you would actually write in the answer book, sized to its marks.
The 15 questions run in this order: types of AI, AI against ML against Deep Learning, the Turing Test with the Chinese Room criticism, the three learning paradigms, state space search with the water jug problem, BFS and DFS, forward and backward chaining, Dempster-Shafer theory, semantic nets with frames and scripts, the machine learning cycle, biological against artificial neuron, classification against clustering, ensemble learning, dimensionality reduction, and the confusion matrix.
Four of those answers carry hand-drawn style block diagrams, because IGNOU asks for diagrams by name in this paper. The Turing Test diagram, the agent and environment loop for reinforcement learning, the six-stage machine learning cycle and the neuron comparison figure are all drawn out, so you are not left guessing what to reproduce under time pressure. Comparison tables are supplied for every "differentiate between" question.
Answer lengths follow the mark weight rather than a fixed template. A 5 or 6 mark answer runs to roughly 150 words, a 10 mark answer to roughly 250 words, and each one opens with a definition, carries the scholar's name where the book gives one, and closes with a short evaluative line. That closing line is what separates a pass answer from a scoring answer in Term-End marking.
If you are also collecting material for other papers of the same semester, our IGNOU Solved Guess Paper library follows the same structure for every course code we cover, so the study routine you build for this subject transfers straight across.
MCS 224 sample questions with answers you can read right now
These 5 samples are printed exactly as they appear in the file, one from each of the four blocks, so you can judge the answer quality before you pay for anything. They are Q1, Q5, Q7, Q13 and Q14 of the 15. The other 10 stay in the complete file.
Sample 1. Types of Artificial Intelligence, ANI and AGI and ASI (6 marks, Block 1)
Question: Compare Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). Give a suitable example for each.
Meaning of the Three Categories of AI
"Weak AI is a term for thinking that is simulated; such systems seem to act intelligently, but they don't have any awareness of what they are doing."
- Artificial Narrow Intelligence (ANI) — Also called Weak or Narrow AI; thinking is only simulated. Systems act intelligently but have no awareness, built for one specific job — e.g. a chatbot that talks but does not know why.
- Artificial General Intelligence (AGI) — Strong or General AI — actual thinking, like a conscious human with a subjective mind. It solves problems on its own — e.g. self-driving cars and hospital operating rooms.
- Artificial Super Intelligence (ASI) — Strong AI smarter than humans, the next step after AGI. It makes rational decisions and even builds emotional relationships; the difference between AGI and ASI is marginal.
Conclusion: AI is classified by capability into ANI (task-specific simulation), AGI (human-level conscious thinking) and ASI (beyond-human intelligence).
Sample 2. State Space Search and the Water Jug Problem (10 marks, Block 1)
Question: What do you understand by State Space Search? Explain the state space representation of the Water Jug Problem and write its production rules.
Meaning of State Space Search
"A state space represents a problem in terms of states and operators that change states; it is the set of all states reachable from the initial state."
A state is a representation of elements at a given moment. Among all possible states there are two special states — the initial state (start point) and the final state (goal). A successor function (a set of operators) is used to move from one state to another. The state space consists of nodes representing each state, arcs representing the legal moves, an initial state and a goal state, taking the form of a tree or a graph. Searching is a sequence of actions that takes you from the initial state to the goal state.
Water Jug Problem
Given a 4-gallon and a 3-gallon jug with no measuring marks, the state is an ordered pair (x, y) where x is the water in the 4-gallon jug and y in the 3-gallon jug. The start state is (0,0) and the goal state is (2,n). The production rules are:
| Rule | Production | Meaning |
|---|---|---|
| R1 | (x,y | x<4) → (4,y) | Fill the 4-gallon jug |
| R2 | (x,y | y<3) → (x,3) | Fill the 3-gallon jug |
| R3 | (x,y | x>0) → (0,y) | Empty the 4-gallon jug |
| R4 | (x,y | y>0) → (x,0) | Empty the 3-gallon jug |
| R5 | (x,y | x+y≥4, y>0) → (4, y−(4−x)) | Pour 3-gallon into 4-gallon until full |
| R6 | (x,y | x+y≥3, x>0) → (x−(3−y), 3) | Pour 4-gallon into 3-gallon until full |
Conclusion: The water jug problem is represented as a state space of (x,y) pairs, where production rules act as operators to move from the start state (0,0) toward the goal (2,n).
Sample 3. Forward Chaining and Backward Chaining Systems (10 marks, Block 2)
Question: Explain forward chaining and backward chaining systems in rule-based systems, with suitable examples.
Meaning of Chaining in Rule-Based Systems
"In a forward chaining system we start with the initial facts, and keep using the rules to draw new intermediate conclusions given those facts." — IGNOU MCS-224
Forward Chaining
We start with the initial facts and keep applying IF-THEN rules to draw new intermediate conclusions or take actions. The process terminates when the final conclusion is established. Forward chaining systems are primarily data-driven.
Backward Chaining
We start with some goal statements to be established and keep looking for rules that would allow us to conclude them, setting new sub-goals in the process. In the next round the sub-goals become the new goals; the process terminates when all sub-goals are given facts. Backward chaining systems are goal-driven.
| Aspect | Forward Chaining | Backward Chaining |
|---|---|---|
| Starting point | Initial known facts | Goal statement to be proved |
| Direction | Facts → conclusions | Goal → sub-goals → facts |
| Nature | Data-driven | Goal-driven |
| Terminates when | Final conclusion is established | All sub-goals are given facts |
Conclusion: Each rule is an independent piece of knowledge; forward chaining moves from facts to a conclusion (data-driven), while backward chaining moves from a goal back to the facts (goal-driven).
Sample 4. Ensemble Learning (5 marks, Block 3)
Question: What is ensemble learning? Briefly discuss any one (or all) of the ensemble learning methods.
Meaning of Ensemble Learning
"Ensemble learning is a general meta approach to machine learning that combines predictions from different models to improve predictive performance."
The subject of ensemble learning is dominated by three primary methods:
- Bagging — Fitting multiple decision trees to various samples of the same dataset and averaging the results to produce the final prediction.
- Stacking — Fitting multiple types of models to the same data and then using another model to learn how to combine the predictions in the best way possible.
- Boosting — Successively adding ensemble members that correct prior models' predictions and producing a weighted average of the predictions.
Conclusion: Ensemble learning combines several models — through Bagging, Stacking or Boosting — to achieve better predictive performance than any single model.
Sample 5. Dimensionality Reduction (6 marks, Block 4)
Question: Explain the term 'Dimensionality Reduction'. Name the general techniques used to perform it, and give its merits and limitations.
Meaning of Dimensionality Reduction
"Dimension reduction is one of the preprocessing phases that occurs during data mining; it may be beneficial in minimising the impacts of noise, correlation, and excessive dimensionality."
Approaches and Techniques
Dimensionality reduction can be done in one of two ways — by feature selection or by feature extraction. The techniques frequently used are Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Singular Value Decomposition (SVD).
Merits and Limitation
- Less noise and correlation — Minimises the impacts of noise, correlation and excessive dimensionality.
- Simpler problems — A 3-D problem can be translated to 2-D and a 1-D one to a line, making problems easier to visualise.
- Limitation — Strongly correlated features (e.g. humidity and rainfall) may be collapsed into one underlying feature, cutting down the number of characteristics — which can risk loss of information.
Conclusion: Dimensionality reduction lowers the number of attributes through feature selection or extraction (using PCA, LDA, SVD), reducing noise and complexity while risking some information loss.
Notice what these five have in common. Each one opens with a quoted definition taken straight from the IGNOU book, keeps the exact counts the book gives, and ends with an evaluative sentence. That is the shape an examiner rewards, and it is the shape all 15 answers in this file follow.
Inside the MCS 224 guess paper PDF with page previews
Seven page images from the actual 16-page file are shown below, including the cover, a diagram answer and a table answer, so you can see the formatting before buying. These are screenshots of the real document, not mock-ups.
How this MCS 224 guess paper was built from previous year question papers
Every question in the file earned its place from a frequency map of six Term-End papers, June 2023 through December 2025, with the December 2025 paper studied line by line as the most recent evidence. Papers from June 2022 onward were read as background; the six mapped sessions are the ones that set the priority labels.
The method is plain. Each descriptive question in each of those papers was logged against the block and unit it came from. Topics that appeared four or more times, or that were absent for two consecutive sessions after a run of appearances, were marked HIGH. Topics with two or three appearances were marked MEDIUM. A topic seen once stayed out of the 15 or was kept only as light backup.
That second rule matters more than students expect. Setters at IGNOU rotate rather than repeat, so a topic that carried three appearances and then went quiet is often the one due back. The Turing Test is the clearest case in this subject: strong in 2023, quiet since, and still carrying a HIGH label in our file for exactly that reason.
Numerical items were deliberately kept out. KNN with Euclidean distance, K-Means, Apriori association rules, PCA arithmetic and fuzzy set operations do appear regularly, but a written model answer helps far less than working the sums yourself. If you want the raw papers to practise those on, start with our IGNOU Previous Year Question Paper collection and time yourself against them.
Answers were then written against the official Self Learning Material rather than from general AI knowledge, which is why definitions carry the book's exact wording, its scholar attributions such as Turing 1950, Searle, Dempster and Shafer, and Jain et al. 1996, and its exact counts. You can check any of it against the source blocks on eGyanKosh.
MCS 224 block and unit index for Artificial Intelligence and Machine Learning
MCS 224 runs to 4 blocks and 16 units, and the guess paper draws from all four. The table below is the official course structure, with the topics each unit actually carries, so you can see where each of the 15 questions sits.
| Block | Unit | Unit title | Key topics inside the unit |
|---|---|---|---|
| Block 1 AI Introduction | 1 | Introduction to Artificial Intelligence | What is AI, examples and approaches, brief history, AI against ML against Deep Learning, intelligent agents, stimulus-response agents |
| 2 | Problem Solving using Search | State space search, state space graphs, feature based state spaces, puzzle and n-queen and road map problems, adversarial search, MinMax, alpha-beta pruning | |
| 3 | Uninformed and Informed Search | Iterative deepening, bidirectional search, evaluation functions, A* and AO*, admissibility of A*, IDA*, recursive best first search | |
| 4 | Predicate and Propositional Logic | Syntax and semantics, validity and satisfiability, interpretation, entailment, proof systems, natural deduction, propositional resolution | |
| Block 2 Knowledge Representation | 5 | First Order Logic | Syntax and interpretations, semantics of quantifiers, entailment in FOL, first order resolution, clausal form conversion, unification, most general unifier |
| 6 | Rule based Systems and other formalism | Forward chaining, backward chaining, conflict resolution, semantic nets, frames, scripts | |
| 7 | Probabilistic Reasoning | Probability review, Bayesian theory, Bayes networks, probabilistic inference, Dempster-Shafer theory of evidence | |
| 8 | Fuzzy and Rough Set | Fuzzy sets and representation, fuzzy inferences, rough set theory | |
| Block 3 Machine Learning I | 9 | Introduction to Machine Learning Methods | Techniques of machine learning, reinforcement learning and algorithms, deep learning algorithms, ensemble methods |
| 10 | Classification | Supervised learning, Naive Bayes, KNN, decision trees, logistic regression, support vector machines | |
| 11 | Regression | Linear regression, polynomial regression, support vector regression | |
| 12 | Neural Networks and Deep Learning | Artificial neural networks, multilayer feedforward networks with sigmoid activation, back propagation, feedforward networks for classification and regression | |
| Block 4 Machine Learning II | 13 | Feature selection and Extraction | Dimensionality reduction, principal component analysis, linear discriminant analysis, singular value decomposition |
| 14 | Association Rules | Pattern search, Apriori algorithm and its variants, FP Tree Growth, Pincer Search | |
| 15 | Clustering | Types of clustering, partition based and hierarchical and density based techniques, K-Means, agglomerative and divisive, DBSCAN, fuzzy clustering | |
| 16 | Machine Learning Programming using Python | Implementation of the algorithms covered in the earlier units of the course |
Two things stand out when you read that index against the paper. Unit 16 is a programming unit and almost never produces a descriptive Term-End question, so it is safe to keep light. Units 6, 7, 9 and 13 are the opposite, and between them they feed six of the 15 questions in the file.
Topic frequency across the last six MCS 224 question papers
Below is the working frequency map that decided the priority labels, covering all six sessions from June 2023 to December 2025. It is published here in full because a claim about what is likely to be asked is worth nothing unless you can see the evidence behind it.
| Topic | Jun 23 | Dec 23 | Jun 24 | Dec 24 | Jun 25 | Dec 25 | Priority |
|---|---|---|---|---|---|---|---|
| Types of AI, ANI and AGI and ASI | Yes | Yes | Yes | HIGH | |||
| AI against ML against Deep Learning | Yes | Yes | Yes | HIGH | |||
| Turing Test with block diagram | Yes | Yes | HIGH | ||||
| Chinese Room Test | Yes | Yes | MEDIUM | ||||
| Supervised, unsupervised and RL | Yes | Yes | Yes | HIGH | |||
| State space search and water jug | Yes | Yes | Yes | Yes | HIGH | ||
| BFS and DFS algorithms | Yes | Yes | Yes | Yes | HIGH | ||
| MinMax and alpha-beta pruning | Yes | Yes | Yes | MEDIUM | |||
| Informed against uninformed search | Yes | Yes | MEDIUM | ||||
| Forward and backward chaining | Yes | Yes | Yes | HIGH | |||
| Predicate against propositional logic | Yes | Yes | MEDIUM | ||||
| PNF and FOPL transformation | Yes | Yes | MEDIUM | ||||
| FOL symbolization of sentences | Yes | Yes | Yes | Yes | Yes | MEDIUM | |
| Resolution and unification | Yes | Yes | MEDIUM | ||||
| Semantic nets, frames and scripts | Yes | Yes | Yes | MEDIUM | |||
| Dempster-Shafer and evidence types | Yes | Yes | HIGH | ||||
| Fuzzy set operations | Yes | Yes | MEDIUM | ||||
| Machine learning cycle diagram | Yes | Yes | MEDIUM | ||||
| Neural network, biological against artificial | Yes | Yes | MEDIUM | ||||
| Classification against clustering | Yes | Yes | Yes | HIGH | |||
| Ensemble learning | Yes | Yes | Yes | HIGH | |||
| Dimensionality reduction | Yes | Yes | Yes | HIGH | |||
| Confusion matrix and model evaluation | Yes | Yes | Yes | MEDIUM | |||
| Linear, logistic and polynomial regression | Yes | Yes | Yes | Yes | Yes | MEDIUM | |
| KNN classification, numerical | Yes | Yes | Yes | Yes | HIGH | ||
| K-Means clustering, numerical | Yes | Yes | Yes | HIGH | |||
| Apriori and association rules, numerical | Yes | Yes | Yes | Yes | HIGH | ||
| Hierarchical against partition clustering | Yes | Yes | Yes | Yes | MEDIUM | ||
| Principal component analysis | Yes | Yes | MEDIUM | ||||
| Bayes theorem and Naive Bayes | Yes | Yes | MEDIUM |
Read the numerical rows carefully. KNN, K-Means and Apriori are all marked HIGH and all three are near-certain to appear, yet none of them is answered in the guess paper. That is deliberate, and it is stated on the file itself. Those questions are won by practising the arithmetic, and pretending otherwise would waste your time.
MCS 224 exam pattern and marks distribution you should plan for
The MCS 224 Term-End paper runs 3 hours for 100 maximum marks at 70 per cent weightage, with Question 1 compulsory and any three of the remaining questions to be attempted. Question 1 carries roughly 40 marks split into seven or eight short parts of 3 to 7 marks each; the rest are 20 marks apiece, so 40 plus 60 gives you 100.
Those 20-mark questions are almost never a single continuous answer. They break as 10 plus 10, as 5 plus 5 plus 10, or as short notes in a four-by-five or five-by-four pattern. This is why the guess paper is written in 5, 6 and 10 mark units rather than as long essays: the units are what you actually assemble in the hall.
Word targets follow from that. Plan roughly 150 words for a 5 or 6 mark part, 250 words for a 10 mark part, and about 450 words if a full 20 marks ever lands on one topic. Writing 400 words for a 5 mark part costs you time you will need elsewhere and earns nothing extra. If you want to check this pattern yourself, the MCS 224 Question paper archive on our site holds the session-wise papers.
One more scheduling detail worth knowing. IGNOU prescribes six theory counselling sessions of two hours each for this course, mapped as Units 1 to 3, then Unit 4 with Unit 5, then Units 6 to 8, then Units 9 to 11, then Unit 12 with Unit 13, then Units 14 to 16. If you attended those, the guess paper will line up with what your counsellor emphasised.
Does MCS 224 have a practical file or a lab record
No. MCS 224 is a 4-credit Theory course and carries no practical file, no lab record and no viva. The practical component of this semester sits in a separate course code, and students regularly confuse the two when ordering material from us.
The AI and machine learning practical work belongs to MCSL-228, the AI and Machine Learning Lab, a 2-credit practical course with 20 sessions of three hours each. Its lab record is prepared session-wise from the MCSL-228 lab manual and is assessed separately at your Learner Support Centre, with 75 per cent attendance compulsory before you can sit the Term-End practical examination.
So the checklist for Semester III is simple. For MCS 224 you need theory answers, which is what this guess paper is. For MCSL-228 you need a written lab record of your practical sessions. The second cannot be substituted by the first, and no guess paper of any kind will satisfy a practical examiner.
Who this MCS 224 Solved Guess Paper is written for
It suits three groups: MCA (New) Semester III students preparing on a short runway, back-paper candidates re-attempting this subject, and working learners who cannot get through 16 units of Self Learning Material before the exam. It is not a replacement for the blocks if you have three clear months.
If you are two or three weeks out, this is the honest use case. Fifteen answered questions covering the repeatedly examined theory ground gives you a defensible attempt at the compulsory Question 1 and at least two of the three long questions, provided you also practise the numerical methods separately.
Learners registered for Master of Science (Data Science and Analytics) under the same School of Computer and Information Sciences cover a great deal of the same AI and machine learning ground, and many of them use this file as revision support alongside their own reading. Material specific to that programme sits under our M.Sc. Data Science and Analytics guess Paper section.
Who should skip it. If you have already worked through all 16 units and solved four or five past papers end to end, you are past the point where a guess paper adds much. Buy the previous year papers instead and spend the time on timed writing practice.
How to get the complete MCS 224 guess paper with answers
Send a WhatsApp message on 9899436384 with the course code MCS 224 and we will confirm the current price and share the complete 16-page PDF with all 15 solved questions. Delivery is digital and usually the same day.
Five of the 15 answers are already on this page at no cost. What you are paying for is the remaining 10.
Those 10 are the Turing Test with the Chinese Room criticism, the three learning paradigms with the reinforcement learning diagram, BFS against DFS with complexity comparison, Dempster-Shafer with the four evidence types, and semantic nets with frames and scripts.
The rest of the set covers the machine learning cycle with its block diagram, the biological against artificial neuron comparison, classification against clustering, AI against ML against Deep Learning, and the confusion matrix with model evaluation methods.
Tell us your session when you message. If you are targeting December 2026 we will send the current edition; if you are appearing in a later session we will tell you plainly whether it still applies rather than selling you something stale.
WhatsApp 9899436384 for the complete MCS 224 guess paper
MCS 224 guess paper questions students ask us
How many questions are in the MCS 224 Solved Guess Paper and how many are shown here?
The file holds 15 descriptive questions with complete answers across 16 pages. Five of them, Q1, Q5, Q7, Q13 and Q14, are printed in full on this page so you can judge the quality yourself. That leaves 10 questions with answers in the paid file. Message us on WhatsApp 9899436384 with the code MCS 224 and we will share the complete PDF.
Which session is this MCS 224 guess paper prepared for?
It is prepared for the December 2026 Term-End Examination of MCS 224. The frequency analysis behind it covers six sessions from June 2023 to December 2025, with the December 2025 paper studied most closely. Because the MCS 224 syllabus of four blocks and 16 units is unchanged, the same file remains useful for the next session unless IGNOU revises the course structure.
Does the MCS 224 guess paper cover the numerical questions such as KNN and K-Means?
No, and that is a deliberate choice. KNN, K-Means, Apriori, PCA arithmetic and fuzzy set operations are examined regularly in MCS 224, but they are won by practising the method rather than by memorising a written answer. The file covers the descriptive theory questions. For numericals, work through the previous year papers with a timer.
Is the MCS 224 guess paper written from the official IGNOU book?
Yes. Every answer is written against the official MCS 224 Self Learning Material published by the School of Computer and Information Sciences, which is why the definitions carry the book's own wording and scholar attributions such as Turing 1950 and Jain et al. 1996. You can verify any definition against the four blocks hosted on eGyanKosh before you rely on it.
Will studying only this MCS 224 guess paper be enough to pass?
For most learners the honest answer is that it gets you close but not all the way. It gives strong coverage of the descriptive theory, which carries the bulk of the marks. You still need to practise the numerical questions separately and be able to draw the block diagrams from memory. Treat it as a focused revision file, not a substitute for the blocks.
Does MCS 224 need a practical file or lab record?
No. MCS 224 is a 4-credit theory course with no practical component. The lab work for this area sits in MCSL-228, the AI and Machine Learning Lab, which is a separate 2-credit practical course with its own lab record and its own 75 per cent attendance requirement. Do not order a guess paper expecting it to serve as your MCSL-228 record.
Which programme and semester does MCS 224 belong to?
MCS 224 Artificial Intelligence and Machine Learning is a Semester III theory course worth 4 credits in the Master of Computer Applications (MCA New) programme run by the School of Computer and Information Sciences. It sits alongside MCS-225 Accountancy and Financial Management, MCS-226, MCS-227 and the two lab courses of that semester in the official MCA programme structure.
How do I receive the MCS 224 file after paying?
Delivery is digital. Once payment is confirmed the 16-page PDF is sent on the same WhatsApp number you messaged from, usually within a few hours on working days. Nothing is posted and there is no login to set up. If anything fails to open, message 9899436384 again and we will resend the MCS 224 file straight away.
Programmes that include MCS 224
MCS 224 is studied in Master of Computer Applications (MCA_NEW) and Master of Science (M.Sc. Data Science and Analytics), both offered by the School of Computer and Information Sciences at IGNOU.
Unnati Educations is an independent academic support platform and is not affiliated with, endorsed by or connected to IGNOU or the Indira Gandhi National Open University in any way. This guess paper is our own prediction based on published previous year question papers and the official Self Learning Material, and is not an official IGNOU publication. IGNOU alone sets the actual question paper. Course structure details on this page are taken from the official MCA (New) Programme Guide, July 2024.