Solve your IGNOU Doubts

Question:

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.

See Answer →

Question:

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).

11. Determine whether the schedule is serializable.

iii. Identify the concurrency issue involved and explain its impact on analytics accuracy.

See Answer →

Question:

Explain the ACID properties of transactions with a data science pipeline example, such as

feature store updates or model versioning.

See Answer →

Question:

Decompose the above relation into 2NF and 3NF, explaining each step clearly.

See Answer →

Question:

Populate the relation with 8-10 sample records and highlight potential data redundancy and anomalies.

See Answer →

Question:

Consider the relation:

Dataset(DatasetID, DatasetName, Source, CollectionDate, Domain, OwnerName, OwnerEmail, UpdateFrequency) Identify the primary key List meaningful functional dependencies

See Answer →

Question:

Explain the importance of indexes in analytical databases. Differentiate between primary index, secondary index, and clustering index with suitable examples.

See Answer →

Question:

Convert the ER diagram designed in part (c) into normalized relations up to 3NF, clearly indicating primary and foreign keys.

See Answer →

Question:

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.

See Answer →

Question:

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 Oeration

See Answer →

Question:

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?

See Answer →

Question:

Hypothesis

See Answer →

Question:

Research in Integrated Rural Development Programme (IRDP)

See Answer →

Question:

Evaluation Research

See Answer →

Question:

Data Types of Questionnaires