ICAR-IASRI MSc and PhD Databases, Big Data & Agricultural Information Systems: practice questions

ICAR-IASRI MSc and PhD (Agricultural Statistics, Computer Application, Bioinformatics) · India · ICAR-Indian Agricultural Statistics Research Institute with the IARI Graduate School

Original practice questions written to the published syllabus · not past papers

6worked here, free

22written to this exam in this subject

10syllabus units

What this subject covers

Storing and serving agricultural data at scale: relational design and SQL, data warehouses and multidimensional models, big-data tools, APIs and open-data portals, sensor and IoT streams, FAIR data and data governance, and the information systems that hold trial, survey and market data.

In the exams: AICE-JRF Computer Application Unit 9 DBMS (heavy); GATE DA 'Database Management and Warehousing' (heavy); AIEEA-PG Group 07 MS Access databases (present)

Relational model, ER modelling and normalisation
SQL and PL/SQL
Transactions, concurrency, indexing and recovery
Distributed databases
Data warehousing: star and snowflake schemas, OLAP
Big-data tools: distributed storage and processing
APIs, open-data portals and data standards (e.g. FAOSTAT, national portals)
Sensor, IoT and drone data pipelines
FAIR principles, metadata and data governance
Agricultural information and decision-support systems

Where the 22 questions fall

How our questions for ICAR-IASRI MSc and PhD spread across the units of Databases, Big Data & Agricultural Information Systems. Difficulty: 15 easy, 7 medium, 0 hard.

Big-data tools: distributed storage and processing	3
Sensor, IoT and drone data pipelines	3
Relational model, ER modelling and normalisation	2
SQL and PL/SQL	2
Transactions, concurrency, indexing and recovery	2
Distributed databases	2
Data warehousing: star and snowflake schemas, OLAP	2
APIs, open-data portals and data standards (e.g. FAOSTAT, national portals)	2
FAIR principles, metadata and data governance	2
Agricultural information and decision-support systems	2

6 practice questions, worked

Choose an answer, then open the working. Each explanation says why the right answer is right and why the others are not.

1 · Relational model, ER modelling and normalisation · easy

A minimal set of attributes that uniquely identifies each row of a relation is a:

Foreign key that refers to another table
Candidate key
Superkey with extra redundant attributes
Composite index on non-key columns

Answer and working

B. Candidate key

A superkey may contain unnecessary attributes; a candidate key is minimal.

Further reading: Elmasri and Navathe, Fundamentals of Database Systems (2016); Silberschatz, Korth and Sudarshan, Database System Concepts (2019); Kimball and Ross, The Data Warehouse Toolkit (2013); Wilkinson et al., FAIR principles (2016)

2 · Transactions, concurrency, indexing and recovery · easy

The 'A' in ACID stands for:

Atomicity of each transaction
Availability of the system
Accuracy of the stored values
Authentication of users

Answer and working

A. Atomicity of each transaction

Consistency, isolation and durability complete the set.

3 · Distributed databases · medium

Replication of data across sites mainly improves:

Write speed, since copies never need to be kept consistent
Availability and read performance
Normal form of the tables
Accuracy of each measurement

Answer and working

B. Availability and read performance

Keeping copies consistent is the cost.

4 · Data warehousing: star and snowflake schemas, OLAP · easy

In a star schema, the central table of numeric measures is the:

Dimension table describing context
Junction table of a many-to-many link
Fact table
Lookup table of error codes

Answer and working

C. Fact table

Dimensions such as time, region and crop surround it.

5 · Data warehousing: star and snowflake schemas, OLAP · medium

Stock levels of grain in stores are a semi-additive measure because they can be summed over:

Time but not over stores, as monthly stocks accumulate
Nothing at all
Stores and regions but not over time
Only the time dimension

Answer and working

C. Stores and regions but not over time

Summing monthly stocks over a year is meaningless.

6 · Big-data tools: distributed storage and processing · medium

The 'variety' characteristic of big data refers to:

The number of crop varieties recorded in the data set
Rapid arrival of data
The usefulness of data
Many formats, from tables to images and text

Answer and working

D. Many formats, from tables to images and text

Velocity is speed; value is usefulness.

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