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ICAR AICE-JRF/SRF Databases, Big Data & Agricultural Information Systems: practice questions

ICAR AICE-JRF/SRF - Agricultural Statistics, Computer Application, Bioinformatics (PhD admission and fellowship) · India · National Testing Agency for ICAR

Original practice questions written to the published syllabus · not past papers
10worked here, free
34written 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)

Where the 34 questions fall

How our questions for ICAR AICE-JRF/SRF spread across the units of Databases, Big Data & Agricultural Information Systems. Difficulty: 0 easy, 28 medium, 6 hard.

Relational model, ER modelling and normalisation5
SQL and PL/SQL5
Transactions, concurrency, indexing and recovery5
Distributed databases4
Data warehousing: star and snowflake schemas, OLAP4
Big-data tools: distributed storage and processing3
Sensor, IoT and drone data pipelines3
Agricultural information and decision-support systems3
APIs, open-data portals and data standards (e.g. FAOSTAT, national portals)1
FAIR principles, metadata and data governance1

10 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 · medium

A many-to-many relationship between farmers and traders is implemented in a relational schema with:

  1. A foreign key placed in the farmer table only
  2. A single combined table with repeated groups
  3. Two tables with no link between them
  4. A junction table holding both keys
Answer and working

D. A junction table holding both keys

The junction table's key combines farmer and trader keys, giving three tables.

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 · Relational model, ER modelling and normalisation · medium

Boyce–Codd normal form requires that:

  1. No attribute is ever null in the table
  2. Every determinant is a candidate key
  3. All attributes are numeric values
  4. Each table has exactly two columns
Answer and working

B. Every determinant is a candidate key

BCNF is stricter than 3NF.

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)

3 · SQL and PL/SQL · medium

A PL/SQL block consists of which sections?

  1. DECLARE, BEGIN, EXCEPTION, END
  2. SELECT, FROM, WHERE, GROUP
  3. CREATE, ALTER, DROP, TRUNCATE
  4. COMMIT, ROLLBACK, SAVEPOINT, END
Answer and working

A. DECLARE, BEGIN, EXCEPTION, END

Variables are declared, statements executed and errors handled.

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)

4 · Transactions, concurrency, indexing and recovery · medium

Reading data written by a transaction that later rolls back is a:

  1. Phantom read of new rows
  2. Non-repeatable read of changed rows
  3. Lost update of a value
  4. Dirty read
Answer and working

D. Dirty read

Read committed isolation prevents it.

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)

5 · Distributed databases · hard

Survey responses whose structure changes between questionnaire versions are conveniently stored in:

  1. A fixed relational table without changes
  2. A hash index
  3. A star schema fact table only
  4. A document store
Answer and working

D. A document store

Self-describing JSON documents accommodate varying structure.

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)

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

Disk-based MapReduce is slow for iterative statistical algorithms mainly because it:

  1. Cannot read numbers, only text records in plain files
  2. Runs on only one machine
  3. Writes intermediate results to disk at each step
  4. Needs no shuffle
Answer and working

C. Writes intermediate results to disk at each step

In-memory frameworks avoid this.

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)

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

A data lake without metadata and governance risks becoming:

  1. A normalised relational database
  2. A faster OLTP system
  3. A star schema
  4. A 'data swamp' of undocumented files
Answer and working

D. A 'data swamp' of undocumented files

Raw data are useless without documentation.

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)

8 · APIs, open-data portals and data standards (e.g. FAOSTAT, national portals) · medium

AGROVOC is:

  1. FAO's multilingual agricultural thesaurus
  2. A crop simulation model
  3. A database engine
  4. A sensor protocol
Answer and working

A. FAO's multilingual agricultural thesaurus

It standardises agricultural terms across languages.

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)

9 · FAIR principles, metadata and data governance · medium

The CARE principles for Indigenous data governance emphasise:

  1. Compression and replication
  2. Collective benefit and authority to control
  3. Caching and encryption
  4. Coding and reporting
Answer and working

B. Collective benefit and authority to control

They complement FAIR with Indigenous data sovereignty.

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)

10 · Agricultural information and decision-support systems · medium

Management information systems mainly provide:

  1. Routine summary reports
  2. What-if simulation
  3. Inference by rules
  4. Raw sensor streams only
Answer and working

A. Routine summary reports

DSS add models for semi-structured decisions.

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)

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Read the subject

Databases, Big Data & Agricultural Information SystemsThe full study manual, free ICAR AICE-JRF/SRFFormat, sections and how it has changed

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