SQL Interview Questions with Answers Part-1: ☑️

1. What is SQL? 
   SQL (Structured Query Language) is a standardized programming language designed to manage and manipulate relational databases. It allows you to query, insert, update, and delete data, as well as create and modify schema objects like tables and views.

2. Differentiate between SQL and NoSQL databases. 
   SQL databases are relational, table-based, and use structured query language with fixed schemas, ideal for complex queries and transactions. NoSQL databases are non-relational, can be document, key-value, graph, or column-oriented, and are schema-flexible, designed for scalability and handling unstructured data.

3. What are the different types of SQL commands?
⦁ DDL (Data Definition Language): CREATE, ALTER, DROP (define and modify structure)
⦁ DML (Data Manipulation Language): SELECT, INSERT, UPDATE, DELETE (data operations)
⦁ DCL (Data Control Language): GRANT, REVOKE (permission control)
⦁ TCL (Transaction Control Language): COMMIT, ROLLBACK, SAVEPOINT (transaction management)

4. Explain the difference between WHERE and HAVING clauses.
⦁ WHERE filters rows before grouping (used with SELECT, UPDATE).
⦁ HAVING filters groups after aggregation (used with GROUP BY), e.g., filtering aggregated results like sums or counts.

5. Write a SQL query to find the second highest salary in a table. 
   Using a subquery:

SELECT MAX(salary) FROM employees  
WHERE salary < (SELECT MAX(salary) FROM employees);

Or using DENSE_RANK():

SELECT salary FROM (  
  SELECT salary, DENSE_RANK() OVER (ORDER BY salary DESC) as rnk  
  FROM employees) t  
WHERE rnk = 2;

6. What is a JOIN? Explain different types of JOINs. 
   A JOIN combines rows from two or more tables based on a related column:
⦁ INNER JOIN: returns matching rows from both tables.
⦁ LEFT JOIN (LEFT OUTER JOIN): all rows from the left table, matched rows from right.
⦁ RIGHT JOIN (RIGHT OUTER JOIN): all rows from right table, matched rows from left.
⦁ FULL JOIN (FULL OUTER JOIN): all rows when there’s a match in either table.
⦁ CROSS JOIN: Cartesian product of both tables.

7. How do you optimize slow-performing SQL queries?
⦁ Use indexes appropriately to speed up lookups.
⦁ Avoid SELECT *; only select necessary columns.
⦁ Use joins carefully; filter early with WHERE clauses.
⦁ Analyze execution plans to identify bottlenecks.
⦁ Avoid unnecessary subqueries; use EXISTS or JOINs.
⦁ Limit result sets with pagination if dealing with large datasets.

8. What is a primary key? What is a foreign key?
⦁ Primary Key: A unique identifier for records in a table; it cannot be NULL.
⦁ Foreign Key: A field that creates a link between two tables by referring to the primary key in another table, enforcing referential integrity.

9. What are indexes? Explain clustered and non-clustered indexes.
⦁ Indexes speed up data retrieval by providing quick lookups.
⦁ Clustered Index: Sorts and stores the actual data rows in the table based on the key; a table can have only one clustered index.
⦁ Non-Clustered Index: Creates a separate structure that points to the data rows; tables can have multiple non-clustered indexes.

10. Write a SQL query to fetch the top 5 records from a table. 
    In SQL Server and PostgreSQL:

SELECT * FROM table_name  
ORDER BY some_column DESC  
LIMIT 5;  

In SQL Server (older syntax):

SELECT TOP 5 * FROM table_name  
ORDER BY some_column DESC;  

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SQL Interview Questions with Answers Part-2: ☑️

11. What is a subquery? Give an example. 
    A subquery is a query nested inside another query (SELECT, INSERT, UPDATE, DELETE). It helps filter or calculate values dynamically. 
    Example:

SELECT name FROM employees  
WHERE department_id = (SELECT id FROM departments WHERE name = 'Sales');

12. Explain the concept of normalization. 
    Normalization is organizing data to minimize redundancy by dividing tables and defining relationships using keys. It improves data integrity and reduces update anomalies. Common normal forms: 1NF, 2NF, 3NF.

13. What is denormalization? When is it used? 
    Denormalization is combining tables to reduce joins, improving read performance at the cost of redundancy. Used in data warehousing or OLAP scenarios requiring fast query responses.

14. Describe transactions and their properties (ACID). 
    A transaction is a set of SQL operations treated as a single unit. ACID properties:
⦁ Atomicity: all or nothing execution
⦁ Consistency: database moves from one valid state to another
⦁ Isolation: concurrent transactions don’t interfere
⦁ Durability: changes persist after commit

15. What is a stored procedure? 
    A stored procedure is a precompiled SQL program stored in the database, which can accept parameters and perform complex operations efficiently, improving performance and reusability.

16. How do you handle NULL values in SQL? 
    Use IS NULL or IS NOT NULL to check NULLs. Functions like COALESCE() or IFNULL() replace NULLs with specified values in queries.

17. Explain the difference between UNION and UNION ALL.
⦁ UNION combines results of two queries and removes duplicates.
⦁ UNION ALL combines results including duplicates, faster than UNION.

18. What are views? How are they useful? 
    A view is a virtual table based on a SELECT query. It simplifies complex queries, provides security by restricting access, and allows data abstraction.

19. What is a trigger? Give use cases. 
    Triggers are special procedures that automatically execute in response to certain events on a table (e.g., INSERT, UPDATE). Use cases: auditing changes, enforcing business rules, cascading changes.

20. How do you perform aggregate functions in SQL? 
    Aggregate functions process multiple rows to return a single value, e.g., COUNT(), SUM(), AVG(), MIN(), and MAX(). Often used with GROUP BY to group results.

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SQL interview questions Part-3 ✅

21. What is data partitioning? 
    Splitting large tables into smaller, manageable pieces (partitions) based on a key like date or region, improving query performance and maintenance.

22. How do you find duplicates in a table? 
    Use GROUP BY with HAVING:

SELECT column, COUNT(*)  
FROM table_name  
GROUP BY column  
HAVING COUNT(*) > 1;  

23. What is the difference between DELETE and TRUNCATE?
⦁ DELETE removes rows one by one, can have WHERE clause, logs each row, slower.
⦁ TRUNCATE removes all rows instantly, no WHERE, resets identity, faster but less flexible.

24. Explain window functions with examples. 
    Window functions perform calculations across sets of rows related to the current row without collapsing results. Example:

SELECT name, salary, RANK() OVER (ORDER BY salary DESC) AS salary_rank  
FROM employees;  

25. What is the difference between correlated and non-correlated subqueries?
⦁ Correlated subqueries depend on the outer query and execute for each row.
⦁ Non-correlated subqueries run independently once.

26. How do you enforce data integrity? 
    Using constraints (PRIMARY KEY, FOREIGN KEY, UNIQUE, CHECK, NOT NULL), triggers, and transactions.

27. What are CTEs (Common Table Expressions)? 
    Temporary named result sets within SQL statements to improve query readability and recursion:

WITH cte AS (SELECT * FROM employees WHERE salary > 5000)  
SELECT * FROM cte;  

28. Explain EXISTS and NOT EXISTS operators.
⦁ EXISTS returns TRUE if a subquery returns any rows.
⦁ NOT EXISTS returns TRUE if subquery returns no rows.

29. How do SQL constraints work? 
    Constraints enforce rules at the database level to ensure data validity and integrity during insert/update/delete operations.

30. What is an execution plan? How do you use it? 
    A detailed roadmap of how SQL Server executes a query. Used to analyze and optimize query performance by revealing bottlenecks.

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SQL interview questions Part-4 ✅

31. Describe how to handle errors in SQL. 
    Use TRY...CATCH blocks (in SQL Server) or exception handling constructs provided by the database to catch and manage runtime errors, ensuring graceful failure or rollback.

32. What are temporary tables? 
    Temporary tables store intermediate results temporarily during a session or procedure, usually with names prefixed by # (local) or ## (global) in SQL Server.

33. Explain the difference between CHAR and VARCHAR.
⦁ CHAR is fixed-length and pads unused spaces, faster for fixed-size data.
⦁ VARCHAR is variable-length, saves space for variable data but may be slightly slower.

34. How do you perform pagination in SQL? 
    Use LIMIT and OFFSET (MySQL/PostgreSQL):

SELECT * FROM table_name ORDER BY id LIMIT 10 OFFSET 20;

Or in SQL Server:

SELECT * FROM table_name ORDER BY id OFFSET 20 ROWS FETCH NEXT 10 ROWS ONLY;

35. What is a composite key? 
    A primary key made up of two or more columns that uniquely identify a record.

36. How do you convert data types in SQL? 
    Using CAST() or CONVERT() functions, e.g.,

SELECT CAST(column_name AS INT) FROM table_name;

37. Explain locking and isolation levels in SQL. 
    Locks control concurrent access to data. Isolation levels (READ UNCOMMITTED, READ COMMITTED, REPEATABLE READ, SERIALIZABLE) define visibility of changes between concurrent transactions, balancing consistency and performance.

38. How do you write recursive queries? 
    Using Recursive CTEs with WITH clause:

WITH RECURSIVE cte AS (  
  SELECT id, parent_id FROM table WHERE parent_id IS NULL  
  UNION ALL  
  SELECT t.id, t.parent_id FROM table t INNER JOIN cte ON t.parent_id = cte.id  
)  
SELECT * FROM cte;

39. What are the advantages of using prepared statements? 
    Improved performance (query plan reuse), security (prevents SQL injection), and ease of use with parameterized inputs.

40. How to debug SQL queries? 
    Analyze execution plans, check syntax errors, use denoscriptive aliases, test subqueries separately, and monitor performance metrics.

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Top 50 Python Interview Questions for Data Analysts (2025) ✅

1. What is Python and why is it popular for data analysis?
2. Differentiate between lists, tuples, and sets in Python.
3. How do you handle missing data in a dataset?
4. What are list comprehensions and how are they useful?
5. Explain Pandas DataFrame and Series.
6. How do you read data from different file formats (CSV, Excel, JSON) in Python?
7. What is the difference between Python’s append() and extend() methods?
8. How do you filter rows in a Pandas DataFrame?
9. Explain the use of groupby() in Pandas with an example.
10. What are lambda functions and how are they used?
11. How do you merge or join two DataFrames?
12. What is the difference between .loc[] and .iloc[] in Pandas?
13. How do you handle duplicates in a DataFrame?
14. Explain how to deal with outliers in data.
15. What is data normalization and how can it be done in Python?
16. Describe different data types in Python.
17. How do you convert data types in Pandas?
18. What are Python dictionaries and how are they useful?
19. How do you write efficient loops in Python?
20. Explain error handling in Python with try-except.
21. How do you perform basic statistical operations in Python?
22. What libraries do you use for data visualization?
23. How do you create plots using Matplotlib or Seaborn?
24. What is the difference between .apply() and .map() in Pandas?
25. How do you export Pandas DataFrames to CSV or Excel files?
26. What is the difference between Python’s range() and xrange()?
27. How can you profile and optimize Python code?
28. What are Python decorators and give a simple example?
29. How do you handle dates and times in Python?
30. Explain list slicing in Python.
31. What are the differences between Python 2 and Python 3?
32. How do you use regular expressions in Python?
33. What is the purpose of the with statement?
34. Explain how to use virtual environments.
35. How do you connect Python with SQL databases?
36. What is the role of the __init__.py file?
37. How do you handle JSON data in Python?
38. What are generator functions and why use them?
39. How do you perform feature engineering with Python?
40. What is the purpose of the Pandas .pivot_table() method?
41. How do you handle categorical data?
42. Explain the difference between deep copy and shallow copy.
43. What is the use of the enumerate() function?
44. How do you detect and handle multicollinearity?
45. How can you improve Python noscript performance?
46. What are Python’s built-in data structures?
47. How do you automate repetitive data tasks with Python?
48. Explain the use of Assertions in Python.
49. How do you write unit tests in Python?
50. How do you handle large datasets in Python?

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Python Interview Questions with Answers Part-1: ☑️

1. What is Python and why is it popular for data analysis? 
   Python is a high-level, interpreted programming language known for simplicity and readability. It’s popular in data analysis due to its rich ecosystem of libraries like Pandas, NumPy, and Matplotlib that simplify data manipulation, analysis, and visualization.

2. Differentiate between lists, tuples, and sets in Python.
⦁ List: Mutable, ordered, allows duplicates.
⦁ Tuple: Immutable, ordered, allows duplicates.
⦁ Set: Mutable, unordered, no duplicates.

3. How do you handle missing data in a dataset? 
   Common methods: removing rows/columns with missing values, filling with mean/median/mode, or using interpolation. Libraries like Pandas provide .dropna(), .fillna() functions to do this easily.

4. What are list comprehensions and how are they useful? 
   Concise syntax to create lists from iterables using a single readable line, often replacing loops for cleaner and faster code. 
   Example: [x**2 for x in range(5)] → ``

5. Explain Pandas DataFrame and Series.
⦁ Series: 1D labeled array, like a column.
⦁ DataFrame: 2D labeled data structure with rows and columns, like a spreadsheet.

6. How do you read data from different file formats (CSV, Excel, JSON) in Python? 
   Using Pandas:
⦁ CSV: pd.read_csv('file.csv')
⦁ Excel: pd.read_excel('file.xlsx')
⦁ JSON: pd.read_json('file.json')

7. What is the difference between Python’s append() and extend() methods?
⦁ append() adds its argument as a single element to the end of a list.
⦁ extend() iterates over its argument adding each element to the list.

8. How do you filter rows in a Pandas DataFrame? 
   Using boolean indexing: 
   df[df['column'] > value] filters rows where ‘column’ is greater than value.

9. Explain the use of groupby() in Pandas with an example. 
   groupby() splits data into groups based on column(s), then you can apply aggregation. 
   Example: df.groupby('category')['sales'].sum() gives total sales per category.

10. What are lambda functions and how are they used? 
    Anonymous, inline functions defined with lambda keyword. Used for quick, throwaway functions without formally defining with def. 
    Example: df['new'] = df['col'].apply(lambda x: x*2)

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Python Interview Questions with Answers Part-2: ☑️

11. How do you merge or join two DataFrames? 
    Use pd.merge(df1, df2, on='key_column', how='inner') with options:
⦁ how='inner' (default) for intersection,
⦁ left, right, or outer for other joins.

12. What is the difference between .loc[] and .iloc[] in Pandas?
⦁ .loc[] selects data by label (index names).
⦁ .iloc[] selects data by integer position (0-based).

13. How do you handle duplicates in a DataFrame? 
    Use df.duplicated() to find duplicates and df.drop_duplicates() to remove them.

14. Explain how to deal with outliers in data. 
    Detect outliers using statistical methods like IQR or Z-score, then either remove, cap, or transform them depending on context.

15. What is data normalization and how can it be done in Python? 
    Scaling data to a standard range (e.g., 0 to 1). Can be done using sklearn’s MinMaxScaler or manually using (x - min) / (max - min).

16. Describe different data types in Python. 
    Common types: int, float, str, bool, list, tuple, dict, set, NoneType.

17. How do you convert data types in Pandas? 
    Use df['col'].astype(new_type) to convert columns, e.g., astype('int') or astype('category').

18. What are Python dictionaries and how are they useful? 
    Unordered collections of key-value pairs useful for fast lookups, mapping, and structured data storage.

19. How do you write efficient loops in Python? 
    Use list comprehensions, generator expressions, and built-in functions instead of traditional loops, or leverage libraries like NumPy for vectorization.

20. Explain error handling in Python with try-except. 
    Wrap code that might cause errors in try: block and handle exceptions in except: blocks to prevent crashes and manage errors gracefully.

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Python Interview Questions with Answers Part-3: ☑️

21. How do you perform basic statistical operations in Python? 
    Use libraries like NumPy (np.mean(), np.median(), np.std()) and Pandas (df.describe()) for statistics like mean, median, variance, etc.

22. What libraries do you use for data visualization? 
    Common ones are Matplotlib, Seaborn, Plotly, and sometimes Bokeh for interactive plots.

23. How do you create plots using Matplotlib or Seaborn? 
    In Matplotlib:

import matplotlib.pyplot as plt
plt.plot(x, y)
plt.show()

In Seaborn:

import seaborn as sns
sns.barplot(x='col1', y='col2', data=df)

24. What is the difference between .apply() and .map() in Pandas?
⦁ .apply() can work on entire Series or DataFrames and accepts functions.
⦁ .map() maps values in a Series based on a dict, Series, or function.

25. How do you export Pandas DataFrames to CSV or Excel files? 
    Use df.to_csv('file.csv') or df.to_excel('file.xlsx').

26. What is the difference between Python’s range() and xrange()? 
    In Python 2, range() returns a list, xrange() returns an iterator for better memory usage. In Python 3, range() behaves like xrange().

27. How can you profile and optimize Python code? 
    Use modules like cProfile, timeit, or line profilers to find bottlenecks, then optimize with better algorithms or vectorization.

28. What are Python decorators and give a simple example? 
    Functions that modify other functions without changing their code. 
    Example:

def decorator(func):
    def wrapper():
        print("Before")
        func()
        print("After")
    return wrapper

@decorator
def say_hello():
    print("Hello")

29. How do you handle dates and times in Python? 
    Use datetime module and libraries like pandas.to_datetime() or dateutil to parse, manipulate, and format dates.

30. Explain list slicing in Python. 
    Get sublists using syntax list[start:stop:step]. Example: lst[1:5:2] picks items from index 1 to 4 skipping every other.

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Python Interview Questions with Answers Part-4: ✅

31. What are the differences between Python 2 and Python 3? 
    Python 3 introduced many improvements: print is a function (print()), better Unicode support, integer division changes, and removed deprecated features. Python 2 is now end-of-life.

32. How do you use regular expressions in Python? 
    With the re module, e.g., re.search(), re.findall(). They help match, search, or replace patterns in strings.

33. What is the purpose of the with statement? 
    Manages resources like file opening/closing automatically ensuring cleanup, e.g.,

with open('file.txt') as f:
    data = f.read()

34. Explain how to use virtual environments. 
    Isolate project dependencies using venv or virtualenv to avoid conflicts between package versions across projects.

35. How do you connect Python with SQL databases? 
    Using libraries like sqlite3, SQLAlchemy, or pymysql to execute SQL queries and fetch results into Python.

36. What is the role of the __init__.py file? 
    Marks a directory as a Python package and can initialize package-level code.

37. How do you handle JSON data in Python? 
    Use json module: json.load() to parse JSON files and json.dumps() to serialize Python objects to JSON.

38. What are generator functions and why use them? 
    Functions that yield values one at a time using yield, saving memory by lazy evaluation, ideal for large datasets.

39. How do you perform feature engineering with Python? 
    Create or transform variables using Pandas (e.g., creating dummy variables, extracting date parts), normalization, or combining features.

40. What is the purpose of the Pandas .pivot_table() method? 
    Creates spreadsheet-style pivot tables for summarizing data, allowing aggregation by multiple indices.

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