SQL Interview Questions with Answers

1. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name
RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.

2. How to use LIKE in SQL?
The LIKE operator checks if an attribute value matches a given string pattern. Here is an example of LIKE operator
SELECT * FROM employees WHERE first_name like ‘Steven’;
With this command, we will be able to extract all the records where the first name is like “Steven”.

3. If we drop a table, does it also drop related objects like constraints, indexes, columns, default, views and sorted procedures?
Yes, SQL server drops all related objects, which exists inside a table like constraints, indexes, columns, defaults etc. But dropping a table will not drop views and sorted procedures as they exist outside the table.

4. Explain SQL Constraints.
SQL Constraints are used to specify the rules of data type in a table. They can be specified while creating and altering the table. The following are the constraints in SQL: NOT NULL CHECK DEFAULT UNIQUE PRIMARY KEY FOREIGN KEY

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Roadmap to become Data Scientist

Data Science Roadmap: 🗺

📂 Math & Stats
 ∟📂 Python/R
  ∟📂 Data Wrangling
   ∟📂 Visualization
    ∟📂 ML
     ∟📂 DL & NLP
      ∟📂 Projects
       ∟ ✅ Apply For Job

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Learning DSA wasn’t just about acing interviews, --- it was about thinking better, building faster, and debugging smarter.

🎯 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝟵 𝗰𝗼𝗿𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝘁𝗵𝗮𝘁 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗱 𝗵𝗼𝘄 𝗜 𝘀𝗼𝗹𝘃𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀:
• Sliding Windows
• Two Pointers
• Stack Based Patterns
• Dynamic Programing
• BFS/DFS (Trees & Graphs)
• Merge Intervals
• Backtracking & Subsets
• top-k Elements (Heaps)
• Greedy Techniques


🛤️ 𝗠𝘆 𝗣𝗮𝘁𝗵 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗗𝗦𝗔:
• Started with basic problems on arrays & strings
• Solved 1-2 problems a day, consistently for 3 months
• Focused more on patterns than individual questions
• Made my own notes, revisited problems I struggled with
• Used visual tools to understand recursion & DP
• Practiced explaining my solutions out loud (like system design reviews)
• Applied patterns in real-world projects (DevOps automation, log parsing, infra tools)


💡 𝗟𝗼𝗼𝗸𝗶𝗻𝗴 𝗯𝗮𝗰𝗸, 𝗼𝗻𝗲 𝘁𝗵𝗶𝗻𝗴 𝗶𝘀 𝗰𝗹𝗲𝗮𝗿:
> It's not how many problems you solve, it's how well you can recognize the pattern hiding in each one.

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Leetcode is a tool to learn

Neetcode is a tool to learn

CodeChef is a tool to learn

Codeforces is a tool to learn

HackerRank is a tool to learn

GeeksForGeeks is a tool to learn

It doesn't matter:

- which platform you are using

- or how many problems you solve

All that matters is how strong you grasp concepts and adapt to problems.

Don't chase the status of 500 or 1000 problems solved.

Chase proper learning & training your mind for problem-solving.

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SQL can be simple—if you learn it the smart way..



If you’re aiming to become a data analyst, mastering SQL is non-negotiable.
Here’s a smart roadmap to ace it:

1. Basics First: Understand data types, simple queries (SELECT, FROM, WHERE). Master basic filtering.

2. Joins & Relationships: Dive into INNER, LEFT, RIGHT joins. Practice combining tables to extract meaningful insights.

3. Aggregations & Functions: Get comfortable with COUNT, SUM, AVG, MAX, GROUP BY, and HAVING clauses. These are essential for summarizing data.

4. Subqueries & Nested Queries: Learn how to query within queries. This is powerful for handling complex datasets.

5. Window Functions: Explore ranking, cumulative sums, and sliding windows to work with running totals and moving averages.

6. Optimization: Study indexing and query optimization for faster, more efficient queries.

7. Real-World Scenarios: Apply your SQL knowledge to solve real-world business problems.

The journey may seem tough, but each step sharpens your skills and brings you closer to data analysis excellence. Stay consistent, practice regularly, and let SQL become your superpower! 💪

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18 Most common used Java List methods

1. add(E element) - Adds the specified element to the end of the list.
2. addAll(Collection<? extends E> c) - Adds all elements of the specified collection to the end of the list.
3. remove(Object o) - Removes the first occurrence of the specified element from the list.
4. remove(int index) - Removes the element at the specified position in the list.
5. get(int index) - Returns the element at the specified position in the list.
6. set(int index, E element) - Replaces the element at the specified position in the list with the specified element.
7. indexOf(Object o) - Returns the index of the first occurrence of the specified element in the list.
8. contains(Object o) - Returns true if the list contains the specified element.
9. size() - Returns the number of elements in the list.
10. isEmpty() - Returns true if the list contains no elements.
11. clear() - Removes all elements from the list.
12. toArray() - Returns an array containing all the elements in the list.
13. subList(int fromIndex, int toIndex) - Returns a view of the portion of the list between the specified fromIndex, inclusive, and toIndex, exclusive.
14. addAll(int index, Collection<? extends E> c) - Inserts all elements of the specified collection into the list, starting at the specified position.
15. iterator() - Returns an iterator over the elements in the list.
16. sort(Comparator<? super E> c) - Sorts the elements of the list according to the specified comparator.
17. replaceAll(UnaryOperator<E> operator) - Replaces each element of the list with the result of applying the given operator.
18. forEach(Consumer<? super E> action) - Performs the given action for each element of the list until all elements have been processed or the action throws an exception.

Beginner’s Roadmap to Learn Data Structures & Algorithms

1. Foundations: Start with the basics of programming and mathematical concepts to build a strong foundation.

2. Data Structure: Dive into essential data structures like arrays, linked lists, stacks, and queues to organise and store data efficiently.

3. Searching & Sorting: Learn various search and sort techniques to optimise data retrieval and organisation.

4. Trees & Graphs: Understand the concepts of binary trees and graph representation to tackle complex hierarchical data.

5. Recursion: Grasp the principles of recursion and how to implement recursive algorithms for problem-solving.

6. Advanced Data Structures: Explore advanced structures like hashing, heaps, and hash maps to enhance data manipulation.

7. Algorithms: Master algorithms such as greedy, divide and conquer, and dynamic programming to solve intricate problems.

8. Advanced Topics: Delve into backtracking, string algorithms, and bit manipulation for a deeper understanding.

9. Problem Solving: Practice on coding platforms like LeetCode to sharpen your skills and solve real-world algorithmic challenges.

10. Projects & Portfolio: Build real-world projects and showcase your skills on GitHub to create an impressive portfolio.

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API

When preparing for a Power BI interview, you should be ready to answer questions that assess your practical experience, understanding of Power BI’s features, and ability to solve real-world business problems using Power BI. Here are some key questions you might encounter, along with tips on how to answer them:

1. Can you describe a Power BI project you worked on? What was your role?
- Tip: Provide a detailed overview of the project, including the business problem, your role in the project, the data sources used, key metrics tracked, and the overall impact of the project. Focus on how you contributed to the project’s success.

2. How do you approach designing a dashboard in Power BI?
- Tip: Explain your process, from understanding the user’s requirements to planning the layout, choosing appropriate visuals, ensuring data accuracy, and focusing on user experience. Mention how you ensure the dashboard is both insightful and easy to use.

3. What are the challenges you’ve faced while working on Power BI projects, and how did you overcome them?
- Tip: Discuss specific challenges like data integration issues, performance optimization, or dealing with complex DAX calculations. Emphasize how you identified the issue and the steps you took to resolve it.

4. How do you manage large datasets in Power BI to ensure optimal performance?
- Tip: Talk about techniques like using DirectQuery, aggregations, optimizing data models, using measures instead of calculated columns, and leveraging Power BI’s performance analyzer to optimize the performance of reports.

5. How do you handle data security in Power BI?
- Tip: Discuss your experience with implementing row-level security (RLS), managing permissions, and ensuring sensitive data is protected. Mention any experience you have with setting up role-based access controls.

6. Can you explain how you use DAX in Power BI to create complex calculations?
- Tip: Provide examples of DAX formulas you’ve written to solve specific business problems. Discuss the logic behind the calculations and how they were used in your reports or dashboards.

7. How do you integrate Power BI with other tools or systems?
- Tip: Talk about your experience integrating Power BI with databases (like SQL Server), Excel, SharePoint, or using APIs to pull in data. Also, mention how you might export data or reports to other tools like Excel or PowerPoint.

8. Describe a situation where you used Power BI to provide insights that led to a significant business decision.
- Tip: Share a specific example where your Power BI report or dashboard uncovered insights that impacted the business. Focus on the outcome and how your analysis influenced the decision-making process.

9. How do you stay updated with new features and updates in Power BI?
- Tip: Mention resources you use like Microsoft’s Power BI blog, community forums, attending webinars, or taking courses. Emphasize the importance of continuous learning in your role.

10. What is your approach to troubleshooting a Power BI report that isn’t working as expected?
- Tip: Describe a systematic approach to identifying the root cause, whether it’s related to data refresh issues, incorrect DAX formulas, or visualization problems.

11. Can you walk us through how you set up and manage Power BI dataflows?
   - Tip: Explain the process of creating dataflows, how you configure them to transform and clean data, and how they help in centralizing and reusing data across multiple reports.

13. How do you handle version control and collaboration in Power BI?
   - Tip: Discuss how you use tools like OneDrive, SharePoint, or Power BI Service for version control, and how you collaborate with other team members on reports and dashboards.

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