Why database testing ensures data integrity in Testing Fundamentals - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to check if a database record exists.

Testing Fundamentals

record = db.query("SELECT * FROM users WHERE id = [1]")

Drag options to blanks, or click blank then click option'

Aemail

Busername

Cuser_id

Dpassword

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-unique fields like username or email can return multiple records.

The query should filter by the unique user ID to find the correct record.

2fill in blank

medium

Complete the code to assert that the retrieved data matches expected values.

Testing Fundamentals

assert user_data['email'] == [1]

Drag options to blanks, or click blank then click option'

A'[e-mail]'

Bemail

[e-mail]

Duser_email

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting quotes around string literals in assertions.

The expected email should be a string literal in quotes for the assertion to work.

3fill in blank

hard

Fix the error in the SQL query to ensure data integrity by checking for null values.

Testing Fundamentals

SELECT * FROM orders WHERE order_date IS [1]

Drag options to blanks, or click blank then click option'

ANULL

BNOT NULL

CEMPTY

DNONE

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'IS NULL' when you want to find records with valid dates.

To ensure data integrity, we check that order_date is NOT NULL, meaning it has a valid value.

4fill in blank

hard

Fill both blanks to create a test that verifies the number of records matches the expected count.

Testing Fundamentals

assert len(db.query("SELECT * FROM products WHERE category = '[1]'")) [2] expected_count

Drag options to blanks, or click blank then click option'

Aelectronics

B==

C>=

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using comparison operators other than '==' when exact match is needed.

The test checks that the number of products in the 'electronics' category equals the expected count.

5fill in blank

hard

Fill all three blanks to write a test that filters records and checks a condition on a field.

Testing Fundamentals

results = db.query("SELECT * FROM sales WHERE region = '[1]'")
filtered = [r for r in results if r['amount'] [2] [3]]
assert len(filtered) > 0

Drag options to blanks, or click blank then click option'

Anorth

B>

C1000

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong comparison operators or numeric values as strings.

This test selects sales from the 'north' region and filters those with amount greater than 1000, asserting there is at least one such record.

Practice

1. Why is database testing important for ensuring data integrity?

easy

A. It verifies that data is accurate and consistent in the database.

B. It improves the speed of the database server.

C. It changes the database schema automatically.

D. It deletes duplicate data without user approval.

Solution

1. Step 1: Understand the purpose of database testing

   Database testing checks if data stored is correct and consistent.

2. Step 2: Identify the role of data integrity

   Data integrity means data is accurate and reliable over its lifecycle.

3. Final Answer:

   It verifies that data is accurate and consistent in the database. -> Option A

4. Quick Check:

   Data integrity = Accurate and consistent data [OK]

Hint: Data integrity means data is correct and consistent [OK]

Common Mistakes:

• Confusing data integrity with performance
• Thinking testing changes data automatically
• Assuming testing deletes data

2. Which SQL query is best to check if a column has any NULL values to ensure data integrity?

easy

A. SELECT * FROM table WHERE column IS NULL;

B. SELECT * FROM table WHERE column = 0;

C. SELECT * FROM table WHERE column LIKE '%NULL%';

D. SELECT * FROM table WHERE column IS NOT NULL;

Solution

1. Step 1: Identify how to find NULL values in SQL

   SQL uses 'IS NULL' to check for NULL values, not '=' or LIKE.

2. Step 2: Match the correct query

   SELECT * FROM table WHERE column IS NULL; uses 'WHERE column IS NULL' which correctly finds NULLs.

3. Final Answer:

   SELECT * FROM table WHERE column IS NULL; -> Option A

4. Quick Check:

   Check NULL with IS NULL [OK]

Hint: Use IS NULL to find missing data in SQL [OK]

Common Mistakes:

• Using '=' to check NULL values
• Using LIKE to find NULLs
• Confusing IS NULL with IS NOT NULL

3. Consider this SQL query to check data integrity:

SELECT COUNT(*) FROM users WHERE email = '' OR email IS NULL;

What does this query return?

medium

A. The number of users with valid email addresses.

B. The number of users missing an email address.

C. The total number of users in the database.

D. The number of users with duplicate emails.

Solution

1. Step 1: Analyze the WHERE clause conditions

   The query counts rows where email is empty string or NULL, meaning missing emails.

2. Step 2: Understand the COUNT(*) function

   COUNT(*) returns the number of rows matching the condition.

3. Final Answer:

   The number of users missing an email address. -> Option B

4. Quick Check:

   Empty or NULL emails counted = missing emails [OK]

Hint: Empty or NULL means missing data [OK]

Common Mistakes:

• Thinking it counts valid emails
• Confusing COUNT(*) with COUNT(column)
• Assuming it finds duplicates

4. You wrote this SQL to check for duplicate user IDs:

SELECT user_id, COUNT(*) FROM users GROUP BY user_id;

Why might this query fail to find duplicates?

medium

A. COUNT(*) cannot be used with GROUP BY.

B. It uses GROUP BY incorrectly on user_id.

C. It should use WHERE instead of GROUP BY.

D. It does not filter groups having more than one record.

Solution

1. Step 1: Understand GROUP BY with COUNT(*)

   The query groups rows by user_id and counts each group size.

2. Step 2: Identify missing filter for duplicates

   Without filtering COUNT(*) > 1, it shows all groups, not just duplicates.

3. Final Answer:

   It does not filter groups having more than one record. -> Option D

4. Quick Check:

   Filter COUNT(*) > 1 to find duplicates [OK]

Hint: Add HAVING COUNT(*) > 1 to find duplicates [OK]

Common Mistakes:

• Assuming GROUP BY alone finds duplicates
• Thinking COUNT(*) is invalid with GROUP BY
• Using WHERE instead of HAVING for group filters

5. A database tester wants to ensure no invalid dates are stored in the orders table's order_date column. Which approach best ensures data integrity?

hard

A. Ignore date checks because the application handles it.

B. Delete all records with dates older than one year without checking.

C. Run a query to find dates outside the allowed range and report errors.

D. Update all dates to the current date automatically.

Solution

1. Step 1: Identify how to check invalid dates

   Querying for dates outside allowed range finds invalid data without changing it.

2. Step 2: Understand importance of reporting errors

   Reporting errors helps fix data issues without data loss or wrong updates.

3. Final Answer:

   Run a query to find dates outside the allowed range and report errors. -> Option C

4. Quick Check:

   Find and report invalid data to keep integrity [OK]

Hint: Find errors first, don't delete or change data blindly [OK]

Common Mistakes:

• Deleting data without validation
• Trusting application without testing
• Automatically changing data without review