Assume that you are the Manager of the Loans department of a Finance House. To keep track of the loans you hav

Question 69 of 95

Q. Assume that you are the Manager of the Loans department of a Finance House. To keep track of the loans you have created two tables : CUSTOMERS and LOANS. The sample data in these tables is given below : Table : CUSTOMERS C_ID C_Name Phone 00001 Raj Malhotra 1234567890 00003 David Xavier 3456789012 00004 Damini Iyer 3156789012 00008 Abdul 2345678901 Table : LOANS SNo C_ID L_Amt L_Date Terms RoI 1 00003 200000 2025-12-06 60 7.80 2 00008 2500000 2023-08-09 60 9.00 3 00001 500000 2025-08-13 48 6.00 4 00003 300000 2026-12-07 36 8.00 5 00004 600000 2026-12-07 60 6.00 Note : The tables may contain more records than shown here. The management of the Finance House needs certain reports from you. Write the queries to extract the following data to create the reports :

(i) Number of records from LOANS table where Rate of Interest (RoI) is above 7.0.

(ii) Names of the customers whose loan amount (L_Amt) is above 1000000.

(iii) C_ID, C_Name and Terms of all those records where Loan Date (L_Date) is after 31st December, 2024.

(iv)

(a) Details of all the loans in the descending order of RoI.

(OR)

(b) C_ID and average term for each C_ID from the LOANS table.

Puducherry CbseCBSE Class XII Board 2026Subjective· 4mImportance★★★★★

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Part (a)Concept understanding — Database Querying

Database Querying: Asking Questions of a Data Collection

Think of a library. You walk in, and instead of wandering through endless shelves, you go to the librarian and say: "I need all books by R. K. Narayan published after 1980." The librarian knows exactly where to look, finds the relevant books, and hands you a short list. That act of asking — of specifying exactly what you want from a large, organised collection — is the essence of querying.

A database is like that library, but for digital information. It stores data in a structured way, typically in tables with rows and columns. A query is simply a question you ask of that database. You don't need to know where the data lives or how it's stored internally. You just need to state what you want.

The Precise Meaning

In formal terms, a database query is a request for data or information from a database. The request is written in a special language that the database understands. The most common such language is SQL (Structured Query Language), but the concept of querying exists in every database system.

When you query a database, you are doing one of four things:

Retrieving data (the most common — "show me the customers who live in Delhi")
Inserting new data ("add this new student record")
Updating existing data ("change this address")
Deleting data ("remove this old order")

For a commerce or humanities student, the retrieval part is where the real power lies. You are not just pulling out raw data — you are filtering, sorting, and combining it to get answers.

Why It Matters

A database without querying is like a library with the lights off. The data exists, but you cannot use it. Querying turns stored facts into actionable information.

Consider a small business owner who keeps customer records in a spreadsheet. Without querying, they might scroll through hundreds of rows to find customers who haven't purchased in six months. With a query, they type one request and get the answer instantly. That answer might lead to a targeted marketing campaign, which brings in revenue.

Note

Querying is not about memorising commands. It is about thinking in terms of conditions and relationships. The skill is learning to translate a real-world question — "Which products are out of stock?" — into a precise, unambiguous request the database can process.

The Intuition Behind It

Every query has a simple structure at its core:

What data do you want? (Which columns or fields?)
Where is it? (Which table or collection?)
Under what conditions? (Which rows match your criteria?)

If you can answer those three questions in plain English, you are already thinking like someone who queries databases. The technical language (SQL) is just a way to write those answers down so the computer understands.

A Real-World Example …

Part (b)Concept understanding — Group By Aggregation

Group By Aggregation: A First Look

Think of a pile of laundry. You have socks, shirts, towels — all mixed together. If someone asks "how many socks are there?", you first separate the socks from everything else, then count them. That act of separating first, then summarising — that is the core of Group By Aggregation.

In data, we often have rows of information about many different things mixed together. Group By Aggregation is the operation that says: "Take all these rows, sort them into meaningful groups based on some common property, and then for each group, compute a single summary value."

The Two Steps, Always

Every Group By Aggregation has exactly two parts, and they happen in order:

Grouping — You pick a column (or columns) whose values will define the groups. All rows that share the same value in that column become one group.
Aggregating — You pick a summary operation to apply to each group. Common operations include counting how many rows are in the group, finding the total or average of a numeric column within the group, or picking the smallest or largest value.

The result is a new table: one row per group, with the group's label and the computed summary.

Why It Matters

Without Group By, you can only summarise the entire dataset at once — one total, one average for everything. That hides almost all the interesting variation. Group By lets you ask questions like:

For each state, what is the total number of schools?
For each year, what was the average rainfall?
For each product category, how many items were sold?

Each of these questions picks a grouping column (state, year, category) and an aggregation (total, average, count). The answer reveals patterns that a single overall number would bury.

A Concrete Example (Without Numbers)

Imagine a table of exam scores for students from different cities. The table has columns: Student Name, City, Score.

If you want to know "how many students appeared from each city", you:

Group by the City column — all rows with the same city name become one group.
Apply the count aggregation — count how many rows are in each group.

The result is a table with two columns: City and Number of Students.

If instead you want "the highest score in each city", you:

Group by City again.
Apply the maximum aggregation to the Score column.

The result is a table with City and Highest Score.

Important

The grouping column and the aggregation column are often different. The grouping column defines who is in each group; the aggregation column defines what you summarise about that group. You can also aggregate the same column you grouped by — for example, counting how many rows are in each group — but that is a special case, not the general rule.

Common Aggregation Operations

These are the standard summaries you can compute for each group:

Count — How many rows are in the group.
Sum — Total of a numeric column across all rows in the group.
Average — Mean of a numeric column across the group.
Minimum — Smallest value in a numeric column within the group.
Maximum — Largest value in a numeric column within the group.

Each of these gives a different lens on the same grouped data.

What the NCERT Textbook Says …

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Part (a)

Reports (i)-(iii) are common; (iv) here is option (a).

(i) Count of loans with RoI above 7.0 (7.80, 9.00, 8.00 -> 3)

SELECT COUNT(*) FROM LOANS WHERE RoI > 7.0;

(ii) Names of customers whose loan amount exceeds 1000000 (needs a join; only Abdul, 2500000)

SELECT DISTINCT C.C_Name
FROM CUSTOMERS C JOIN LOANS L ON C.C_ID = L.C_ID
WHERE L.L_Amt > 1000000;

(iii) C_ID, C_Name, Terms where L_Date is after 31-Dec-2024

SELECT C.C_ID, C.C_Name, L.Terms
FROM CUSTOMERS C JOIN LOANS L ON C.C_ID = L.C_ID
WHERE L.L_Date > '2024-12-31';

(iv)(a) Details of all loans in descending order of RoI

SELECT * FROM LOANS ORDER BY RoI DESC; …

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Part (a): reports (i) COUNT RoI>7.0=3, (ii) join for names with L_Amt>1000000 (Abdul), (iii) join for C_ID/C_Name/Terms with L_Date>'2024-12-31', (iv)(a) all loans ORDER BY RoI DESC. Part (b): the (iv) alternative — C_ID and AVG(Terms) grouped by C_ID.

Two tables: CUSTOMERS(C_ID, C_Name, Phone) and LOANS(SNo, C_ID, L_Amt, L_Date, Terms, RoI). Reports (i), (ii) and (iii) are the same for both choices; task (iv) is where option (a) and option (b) differ.

Part (a)

(i) Count rows, not list them, so use COUNT(*). RoI values 7.80, 9.00 and 8.00 exceed 7.0.

SELECT COUNT(*) FROM LOANS WHERE RoI > 7.0;

Expected output:

(ii) The name lives in CUSTOMERS, the amount in LOANS, so join on C_ID. Only loan 2500000 (C_ID 00008) exceeds 1000000.

SELECT DISTINCT C.C_Name
FROM CUSTOMERS C JOIN LOANS L ON C.C_ID = L.C_ID
WHERE L.L_Amt > 1000000;

Expected output:

Abdul

(iii) Join again and filter dates after 31-Dec-2024 (i.e. 2025 onward).

SELECT C.C_ID, C.C_Name, L.Terms
FROM CUSTOMERS C JOIN LOANS L ON C.C_ID = L.C_ID
WHERE L.L_Date > '2024-12-31';

Expected output:

00003  David Xavier   60
00001  Raj Malhotra   48
00003  David Xavier   36
00004  Damini Iyer    60

(iv)(a) "Details" = all columns of LOANS, sorted highest interest first.

SELECT * FROM LOANS ORDER BY RoI DESC;
``` …

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