SQL interview questions Part-4 ✅
31. Describe how to handle errors in SQL.
Use
32. What are temporary tables?
Temporary tables store intermediate results temporarily during a session or procedure, usually with names prefixed by
33. Explain the difference between CHAR and VARCHAR.
⦁
⦁
34. How do you perform pagination in SQL?
Use
Or in SQL Server:
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
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
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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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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What is the purpose of a subquery in SQL?
Anonymous Quiz
9%
A) To join two tables
87%
B) To use the result of one query inside another
2%
C) To rename columns
3%
D) To insert data into a table
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Which SQL clause commonly uses subqueries to filter data?
Anonymous Quiz
21%
A) SELECT
8%
B) FROM
54%
C) WHERE
17%
D) GROUP BY
❤5😁2
Why are aliases useful in SQL?
Anonymous Quiz
10%
A) They speed up query execution
82%
B) They make queries easier to read and manage
5%
C) They prevent data loss
3%
D) They encrypt the data
❤4😁2
What will this query return?
SELECT employee_name, salary FROM employees WHERE salary > (SELECT AVG(salary) FROM employees);
SELECT employee_name, salary FROM employees WHERE salary > (SELECT AVG(salary) FROM employees);
Anonymous Quiz
12%
A) Employees with salary less than average
4%
B) All employees and their salaries
75%
C) Employees with salary greater than average
9%
D) Average salary of all employees
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SQL Interview Questions with Answers Part-5: ☑️
41. Differentiate between OLTP and OLAP databases.
⦁ OLTP (Online Transaction Processing) is optimized for transactional tasks—fast inserts, updates, and deletes with many users.
⦁ OLAP (Online Analytical Processing) is optimized for complex queries and data analysis, often dealing with large historical datasets.
42. What is schema in SQL?
A schema is a logical container that holds database objects like tables, views, and procedures, helping organize and manage database permissions.
43. How do you implement many-to-many relationships in SQL?
By creating a junction (or associative) table with foreign keys referencing the two related tables.
44. What is query optimization?
The process of improving query execution efficiency by rewriting queries, indexing, and analyzing execution plans to reduce resource consumption.
45. How do you handle large datasets in SQL?
Use partitioning, indexing, batch processing, query optimization, and sometimes materialized views or data archiving to manage performance.
46. Explain the difference between CROSS JOIN and INNER JOIN.
⦁ CROSS JOIN returns the Cartesian product (all combinations) of two tables.
⦁ INNER JOIN returns only matching rows based on join conditions.
47. What is a materialized view?
A stored physical copy of the result set of a query, which improves performance for complex queries by avoiding recomputation every time.
48. How do you backup and restore a database?
Use built-in commands/tools like
49. Explain how indexing can degrade performance.
Too many indexes slow down write operations (INSERT, UPDATE, DELETE) because indexes must also be updated; large indexes can consume extra storage and memory.
50. Can you write a query to find employees with no managers?
Example:
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41. Differentiate between OLTP and OLAP databases.
⦁ OLTP (Online Transaction Processing) is optimized for transactional tasks—fast inserts, updates, and deletes with many users.
⦁ OLAP (Online Analytical Processing) is optimized for complex queries and data analysis, often dealing with large historical datasets.
42. What is schema in SQL?
A schema is a logical container that holds database objects like tables, views, and procedures, helping organize and manage database permissions.
43. How do you implement many-to-many relationships in SQL?
By creating a junction (or associative) table with foreign keys referencing the two related tables.
44. What is query optimization?
The process of improving query execution efficiency by rewriting queries, indexing, and analyzing execution plans to reduce resource consumption.
45. How do you handle large datasets in SQL?
Use partitioning, indexing, batch processing, query optimization, and sometimes materialized views or data archiving to manage performance.
46. Explain the difference between CROSS JOIN and INNER JOIN.
⦁ CROSS JOIN returns the Cartesian product (all combinations) of two tables.
⦁ INNER JOIN returns only matching rows based on join conditions.
47. What is a materialized view?
A stored physical copy of the result set of a query, which improves performance for complex queries by avoiding recomputation every time.
48. How do you backup and restore a database?
Use built-in commands/tools like
BACKUP DATABASE and RESTORE DATABASE in SQL Server, or mysqldump in MySQL, often automating with noscripts for regular backups.49. Explain how indexing can degrade performance.
Too many indexes slow down write operations (INSERT, UPDATE, DELETE) because indexes must also be updated; large indexes can consume extra storage and memory.
50. Can you write a query to find employees with no managers?
Example:
SELECT * FROM employees e
WHERE NOT EXISTS (SELECT 1 FROM employees m WHERE m.id = e.manager_id);
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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
8. How do you filter rows in a Pandas DataFrame?
9. Explain the use of
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
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
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
25. How do you export Pandas DataFrames to CSV or Excel files?
26. What is the difference between Python’s
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
34. Explain how to use virtual environments.
35. How do you connect Python with SQL databases?
36. What is the role of the
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
41. How do you handle categorical data?
42. Explain the difference between deep copy and shallow copy.
43. What is the use of the
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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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
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:
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:
⦁ Excel:
⦁ JSON:
7. What is the difference between Python’s
⦁
⦁
8. How do you filter rows in a Pandas DataFrame?
Using boolean indexing:
9. Explain the use of
Example:
10. What are lambda functions and how are they used?
Anonymous, inline functions defined with
Example:
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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:
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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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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:
In Seaborn:
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:
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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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 (
32. How do you use regular expressions in Python?
With the
33. What is the purpose of the
Manages resources like file opening/closing automatically ensuring cleanup, e.g.,
34. Explain how to use virtual environments.
Isolate project dependencies using
35. How do you connect Python with SQL databases?
Using libraries like
36. What is the role of the
Marks a directory as a Python package and can initialize package-level code.
37. How do you handle JSON data in Python?
Use
38. What are generator functions and why use them?
Functions that yield values one at a time using
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
Creates spreadsheet-style pivot tables for summarizing data, allowing aggregation by multiple indices.
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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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Python Interview Questions with Answers Part-5: ☑️
41. How do you handle categorical data?
Use encoding techniques like one-hot encoding (
42. Explain the difference between deep copy and shallow copy.
⦁ Shallow copy copies an object but references nested objects.
⦁ Deep copy copies everything recursively, creating independent objects.
43. What is the use of the
Adds a counter to an iterable, yielding pairs
44. How do you detect and handle multicollinearity?
Use correlation matrix or Variance Inflation Factor (VIF). Handle by removing or combining correlated features.
45. How can you improve Python noscript performance?
Use efficient data structures, built-in functions, vectorized operations with NumPy/Pandas, and profile code to identify bottlenecks.
46. What are Python’s built-in data structures?
List, Tuple, Set, Dictionary, String.
47. How do you automate repetitive data tasks with Python?
Write noscripts or use task schedulers (like cron/Windows Task Scheduler) with libraries such as
48. Explain the use of Assertions in Python.
Used for debugging by asserting conditions that must be true, raising errors if violated:
49. How do you write unit tests in Python?
Use
50. How do you handle large datasets in Python?
Use chunking with Pandas
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41. How do you handle categorical data?
Use encoding techniques like one-hot encoding (
pd.get_dummies()), label encoding, or ordinal encoding to convert categories into numeric values.42. Explain the difference between deep copy and shallow copy.
⦁ Shallow copy copies an object but references nested objects.
⦁ Deep copy copies everything recursively, creating independent objects.
43. What is the use of the
enumerate() function? Adds a counter to an iterable, yielding pairs
(index, value) great for loops when you need the item index as well.44. How do you detect and handle multicollinearity?
Use correlation matrix or Variance Inflation Factor (VIF). Handle by removing or combining correlated features.
45. How can you improve Python noscript performance?
Use efficient data structures, built-in functions, vectorized operations with NumPy/Pandas, and profile code to identify bottlenecks.
46. What are Python’s built-in data structures?
List, Tuple, Set, Dictionary, String.
47. How do you automate repetitive data tasks with Python?
Write noscripts or use task schedulers (like cron/Windows Task Scheduler) with libraries such as
pandas, openpyxl, and automation tools.48. Explain the use of Assertions in Python.
Used for debugging by asserting conditions that must be true, raising errors if violated:
assert x > 0, "x must be positive"49. How do you write unit tests in Python?
Use
unittest or pytest frameworks to write test functions/classes that verify code behavior automatically.50. How do you handle large datasets in Python?
Use chunking with Pandas
read_csv(chunk_size=…), Dask for parallel computing, or databases to process data in parts rather than all at once.Python Interview Questions: https://news.1rj.ru/str/sqlspecialist/2220
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The Shift in Data Analyst Roles: What You Should Apply for in 2025
The traditional “Data Analyst” noscript is gradually declining in demand in 2025 not because data is any less important, but because companies are getting more specific in what they’re looking for.
Today, many roles that were once grouped under “Data Analyst” are now split into more domain-focused noscripts, depending on the team or function they support.
Here are some roles gaining traction:
* Business Analyst
* Product Analyst
* Growth Analyst
* Marketing Analyst
* Financial Analyst
* Operations Analyst
* Risk Analyst
* Fraud Analyst
* Healthcare Analyst
* Technical Analyst
* Business Intelligence Analyst
* Decision Support Analyst
* Power BI Developer
* Tableau Developer
Focus on the skillsets and business context these roles demand.
Whether you're starting out or transitioning, look beyond "Data Analyst" and align your profile with industry-specific roles. It’s not about the noscript—it’s about the value you bring to a team.
The traditional “Data Analyst” noscript is gradually declining in demand in 2025 not because data is any less important, but because companies are getting more specific in what they’re looking for.
Today, many roles that were once grouped under “Data Analyst” are now split into more domain-focused noscripts, depending on the team or function they support.
Here are some roles gaining traction:
* Business Analyst
* Product Analyst
* Growth Analyst
* Marketing Analyst
* Financial Analyst
* Operations Analyst
* Risk Analyst
* Fraud Analyst
* Healthcare Analyst
* Technical Analyst
* Business Intelligence Analyst
* Decision Support Analyst
* Power BI Developer
* Tableau Developer
Focus on the skillsets and business context these roles demand.
Whether you're starting out or transitioning, look beyond "Data Analyst" and align your profile with industry-specific roles. It’s not about the noscript—it’s about the value you bring to a team.
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Guys, Big Announcement!
We’ve officially hit 2.5 Million followers on WhatsApp — and it’s time to level up together! ❤️
I’m launching a Python Projects Series — designed for beginners to those preparing for technical interviews or building real-world projects.
This will be a step-by-step, hands-on journey — where you’ll build useful Python projects with clear code, explanations, and mini-quizzes!
Here’s what we’ll cover:
🔹 Week 1: Python Mini Projects (Daily Practice)
⦁ Calculator
⦁ To-Do List (CLI)
⦁ Number Guessing Game
⦁ Unit Converter
⦁ Digital Clock
🔹 Week 2: Data Handling & APIs
⦁ Read/Write CSV & Excel files
⦁ JSON parsing
⦁ API Calls using Requests
⦁ Weather App using OpenWeather API
⦁ Currency Converter using Real-time API
🔹 Week 3: Automation with Python
⦁ File Organizer Script
⦁ Email Sender
⦁ WhatsApp Automation
⦁ PDF Merger
⦁ Excel Report Generator
🔹 Week 4: Data Analysis with Pandas & Matplotlib
⦁ Load & Clean CSV
⦁ Data Aggregation
⦁ Data Visualization
⦁ Trend Analysis
⦁ Dashboard Basics
🔹 Week 5: AI & ML Projects (Beginner Friendly)
⦁ Predict House Prices
⦁ Email Spam Classifier
⦁ Sentiment Analysis
⦁ Image Classification (Intro)
⦁ Basic Chatbot
📌 Each project includes:
✅ Problem Statement
✅ Code with explanation
✅ Sample input/output
✅ Learning outcome
✅ Mini quiz
💬 React ❤️ if you're ready to build some projects together!
You can access it for free here
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https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Let’s Build. Let’s Grow. 💻🙌
We’ve officially hit 2.5 Million followers on WhatsApp — and it’s time to level up together! ❤️
I’m launching a Python Projects Series — designed for beginners to those preparing for technical interviews or building real-world projects.
This will be a step-by-step, hands-on journey — where you’ll build useful Python projects with clear code, explanations, and mini-quizzes!
Here’s what we’ll cover:
🔹 Week 1: Python Mini Projects (Daily Practice)
⦁ Calculator
⦁ To-Do List (CLI)
⦁ Number Guessing Game
⦁ Unit Converter
⦁ Digital Clock
🔹 Week 2: Data Handling & APIs
⦁ Read/Write CSV & Excel files
⦁ JSON parsing
⦁ API Calls using Requests
⦁ Weather App using OpenWeather API
⦁ Currency Converter using Real-time API
🔹 Week 3: Automation with Python
⦁ File Organizer Script
⦁ Email Sender
⦁ WhatsApp Automation
⦁ PDF Merger
⦁ Excel Report Generator
🔹 Week 4: Data Analysis with Pandas & Matplotlib
⦁ Load & Clean CSV
⦁ Data Aggregation
⦁ Data Visualization
⦁ Trend Analysis
⦁ Dashboard Basics
🔹 Week 5: AI & ML Projects (Beginner Friendly)
⦁ Predict House Prices
⦁ Email Spam Classifier
⦁ Sentiment Analysis
⦁ Image Classification (Intro)
⦁ Basic Chatbot
📌 Each project includes:
✅ Problem Statement
✅ Code with explanation
✅ Sample input/output
✅ Learning outcome
✅ Mini quiz
💬 React ❤️ if you're ready to build some projects together!
You can access it for free here
👇👇
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Let’s Build. Let’s Grow. 💻🙌
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Top 50 Power BI Interview Questions (2025) ✅
1. What is Power BI?
2. Explain the key components of Power BI.
3. Differentiate between Power BI Desktop, Service, and Mobile.
4. What are the different types of data sources in Power BI?
5. Explain the Get Data process in Power BI.
6. What is Power Query Editor?
7. How do you clean and transform data in Power Query?
8. What are the different data transformations available in Power Query?
9. What is M language in Power BI?
10. Explain the concept of data modeling in Power BI.
11. What are relationships in Power BI?
12. What are the different types of relationships in Power BI?
13. What is cardinality in Power BI?
14. What is cross-filter direction in Power BI?
15. How do you create calculated columns and measures?
16. What is DAX?
17. Explain the difference between calculated columns and measures.
18. List some common DAX functions.
19. What is the CALCULATE function in DAX?
20. How do you use variables in DAX?
21. What are the different types of visuals in Power BI?
22. How do you create interactive dashboards in Power BI?
23. Explain the use of slicers in Power BI.
24. What are filters in Power BI?
25. How do you use bookmarks in Power BI?
26. What is the Power BI Service?
27. How do you publish reports to the Power BI Service?
28. How do you create dashboards in the Power BI Service?
29. How do you share reports and dashboards in Power BI?
30. What are workspaces in Power BI?
31. Explain the role of gateways in Power BI.
32. How do you schedule data refresh in Power BI?
33. What is Row-Level Security (RLS) in Power BI?
34. How do you implement RLS in Power BI?
35. What are Power BI apps?
36. What are dataflows in Power BI?
37. How do you use parameters in Power BI?
38. What are custom visuals in Power BI?
39. How do you import custom visuals into Power BI?
40. Explain performance optimization techniques in Power BI.
41. What is the difference between import and direct query mode?
42. When should you use direct query mode?
43. How do you connect to cloud data sources in Power BI?
44. What are the advantages of using Power BI?
45. How do you handle errors in Power BI?
46. What are the limitations of Power BI?
47. Explain Power BI Embedded.
48. What is Power BI Report Server?
49. How do you use Power BI with Azure?
50. What are the latest features of Power BI?
Double tap ❤️ for detailed answers!
1. What is Power BI?
2. Explain the key components of Power BI.
3. Differentiate between Power BI Desktop, Service, and Mobile.
4. What are the different types of data sources in Power BI?
5. Explain the Get Data process in Power BI.
6. What is Power Query Editor?
7. How do you clean and transform data in Power Query?
8. What are the different data transformations available in Power Query?
9. What is M language in Power BI?
10. Explain the concept of data modeling in Power BI.
11. What are relationships in Power BI?
12. What are the different types of relationships in Power BI?
13. What is cardinality in Power BI?
14. What is cross-filter direction in Power BI?
15. How do you create calculated columns and measures?
16. What is DAX?
17. Explain the difference between calculated columns and measures.
18. List some common DAX functions.
19. What is the CALCULATE function in DAX?
20. How do you use variables in DAX?
21. What are the different types of visuals in Power BI?
22. How do you create interactive dashboards in Power BI?
23. Explain the use of slicers in Power BI.
24. What are filters in Power BI?
25. How do you use bookmarks in Power BI?
26. What is the Power BI Service?
27. How do you publish reports to the Power BI Service?
28. How do you create dashboards in the Power BI Service?
29. How do you share reports and dashboards in Power BI?
30. What are workspaces in Power BI?
31. Explain the role of gateways in Power BI.
32. How do you schedule data refresh in Power BI?
33. What is Row-Level Security (RLS) in Power BI?
34. How do you implement RLS in Power BI?
35. What are Power BI apps?
36. What are dataflows in Power BI?
37. How do you use parameters in Power BI?
38. What are custom visuals in Power BI?
39. How do you import custom visuals into Power BI?
40. Explain performance optimization techniques in Power BI.
41. What is the difference between import and direct query mode?
42. When should you use direct query mode?
43. How do you connect to cloud data sources in Power BI?
44. What are the advantages of using Power BI?
45. How do you handle errors in Power BI?
46. What are the limitations of Power BI?
47. Explain Power BI Embedded.
48. What is Power BI Report Server?
49. How do you use Power BI with Azure?
50. What are the latest features of Power BI?
Double tap ❤️ for detailed answers!
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