IGNOU MCS 207 Solved Assignment 2026-27 English Medium PDF Download Details
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| Course Code | MCS 207 |
| Course Title | Database Management Systems |
| Session | July 2026 – January 2027 |
| Medium | English |
| Assignment Number | PGDCA_NEW(II)/207/Assignment/2026-27 |
IGNOU MCS 207 Solved Assignment 2026-27 English Medium Question Paper
MCS 207 : Database Management Systems
Question 1: Foundations of Data-Centric Database Systems
(a) Data scientists often work with large, evolving datasets. Explain the limitations of traditional file-based systems in the context of analytics and machine learning workflows. How does a DBMS address these limitations?
(b) Explain the following concepts with respect to the relational data model, giving one data-science-oriented example for each:
– Candidate Key
– Functional Dependency
– Referential Integrity
– Selection Operation
– Projection Operation
(c) A health analytics platform maintains data about patients, diagnostic tests, doctors, and test results. Analysts want to query trends across diseases, age groups, and regions. Design an ER diagram for this system. Clearly identify entities, relationships, key attributes, and constraints. State assumptions made.
(d) Convert the ER diagram designed in part (c) into normalized relations up to 3NF, clearly indicating primary and foreign keys.
(e) Explain the importance of indexes in analytical databases. Differentiate between primary index, secondary index, and clustering index with suitable examples.
Question 2: Data Normalization, Dependencies, and SQL for Analytics
(a) Consider the relation:
Dataset(DatasetID, DatasetName, Source, CollectionDate, Domain, OwnerName, OwnerEmail, UpdateFrequency)
– Identify the primary key
– List meaningful functional dependencies
(b) Populate the relation with 8–10 sample records and highlight potential data redundancy and anomalies.
(c) Decompose the above relation into 2NF and 3NF, explaining each step clearly.
(d) Consider the following relations used in a research publication analytics system:
– Researcher(ResearcherID, Name, Email, Affiliation)
– Publication(PubID, Title, Year, Venue)
– Authorship(ResearcherID, PubID, AuthorOrder)
Perform the following using SQL:
– Create tables with appropriate primary and foreign keys
– Insert sample data (minimum 5 researchers, 6 publications)
– List publications by a given researcher
– Find researchers who have not authored any publication
– Find the publication with the highest number of authors
– List venues that have more than two publications
Question 3: Transactions, Concurrency, and Consistency in Data Systems
(a) Explain the ACID properties of transactions with a data science pipeline example, such as feature store updates or model versioning.
(b) Consider the following schedule involving two transactions T1 and T2 operating on a dataset table storing aggregate metrics:
Time | T1 | T2
t1 | READ(X) |
t2 | X = X + 50 |
t3 | | READ(X)
t4 | | X = X * 1.2
t5 | WRITE(X) |
t6 | | WRITE(X)
(i) Compute the final value of X (assume initial X = 100).
(ii) Determine whether the schedule is serializable.
(iii) Identify the concurrency issue involved and explain its impact on analytics accuracy.
(c) Explain the Two-Phase Locking (2PL) protocol. Discuss how it ensures consistency in multi-user analytical environments. Can deadlocks still occur? Explain with a suitable example.
Question 4: Advanced Topics and Case-Based Understanding
Write short notes on the following. Support your answers with examples relevant to data science or analytics.
a) Centralized vs Distributed Databases in Large-Scale Analytics
b) Star Schema and Snowflake Schema in Data Warehousing
c) NoSQL Databases for Data Science (Explain one type with use cases)
d) Query Optimization Techniques for Analytical Queries
e) Log-Based Recovery and Checkpointing in Data-Intensive Systems
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