15 SQL interview questions for freshers

1) What is SQL and what is it used for?
Answer: SQL is a language for managing and querying relational databases. It’s used to retrieve, insert, update, delete data and to manage schema and permissions.

2) What are the different types of SQL statements?
Answer: DDL (Data Definition Language), DML (Data Manipulation Language), DCL (Data Control Language), and DTL (Transaction Control Language).

3) What is a primary key?
Answer: A unique identifier for each row in a table; cannot be NULL.

4) What is a foreign key?
Answer: A field that creates a link between two tables, enforcing referential integrity.

5) What is the difference between INNER JOIN and LEFT JOIN?
Answer: INNER JOIN returns matching rows from both tables; LEFT JOIN returns all rows from the left table and matched rows from the right table (NULL if no match).

6) What is normalization?
Answer: Organizing data to reduce redundancy by dividing into related tables and defining relationships.

7) What is a database index?
Answer: A data structure that improves the speed of data retrieval; can be on one or more columns.

8) What is GROUP BY and HAVING?
Answer: GROUP BY aggregates rows by column(s); HAVING filters groups after aggregation (unlike WHERE which filters rows before aggregation).

9) What is a subquery?
Answer: A query nested inside another query, used to perform operations that depend on another query’s result.

10) What is a view?
Answer: A saved query that presents data as a virtual table; does not store data itself.

11) What is transaction management?
Answer: Ensuring data integrity using ACID properties; COMMIT to save, ROLLBACK to undo, and SAVEPOINT to set a point to roll back to.

12) What are SQL constraints?
Answer: Rules like PRIMARY KEY, FOREIGN KEY, NOT NULL, UNIQUE, CHECK, and DEFAULT to enforce data integrity.

13) What is the difference between WHERE and HAVING?
Answer: WHERE filters rows before grouping; HAVING filters groups after aggregation.

14) What is a stored procedure?
Answer: A precompiled set of SQL statements stored in the database, can be executed with parameters.

15) What is the difference between UNION and UNION ALL?
Answer: UNION removes duplicates between results; UNION ALL keeps all rows, including duplicates.

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You already have the skills and expertise in Data Analytics tools like SQL, Power BI, Tableau, and Python. 𝐍𝐨𝐰, 𝐡𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐟𝐢𝐧𝐝 𝐚 𝐣𝐨𝐛?

1. Tailor your LinkedIn profile to highlight your Data Analyst skills and experience.

2. Make a list of companies that hire Data Analysts and follow them on LinkedIn to stay updated on job openings. (Ex- McKinsey & Company, BCG, Bain & Company, Google, Amazon, Microsoft, IBM, Goldman Sachs, JPMorgan Chase, Walmart, Target)

3. Follow HRs from your target companies on LinkedIn and reach out to them for job openings or whenever they post about job openings, send your resume to them within 2-3 hours via LinkedIn or email if available.

4. Connect with Managers or Senior Managers in Data Analyst roles at your target companies on LinkedIn and ask if they are hiring for their team or would be willing to refer you for any relevant Data analyst role.

5. Apply for jobs on LinkedIn, Naukri, and directly on the company's website.

📊 Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)

Aggregate functions are used to perform calculations on multiple rows of a table and return a single value. They're mostly used with GROUP BY, but also work standalone.

1. COUNT()
Returns the number of rows.

Example:

SELECT COUNT(*) FROM employees;

Counts all employees in the table.

You can also count only non-null values in a column:

SELECT COUNT(email) FROM customers;


2. SUM()
Adds up all the values in a numeric column.

Example:

SELECT SUM(salary) FROM employees;

Gives you the total salary payout.


3. AVG()
Calculates the average value of a numeric column.

Example:

SELECT AVG(price) FROM products;

Finds the average product price.


4. MIN()
Returns the lowest value.

Example:

SELECT MIN(salary) FROM employees;

Finds the smallest salary.


5. MAX()
Returns the highest value.

Example:

SELECT MAX(salary) FROM employees;

Finds the highest salary in the table.


Bonus Example:

SELECT
COUNT(*) AS total_orders,
SUM(amount) AS total_revenue,
AVG(amount) AS avg_order_value
FROM orders;

This gives you a quick business summary: number of orders, total revenue, and average order value.


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Key SQL Concepts for Data Analyst Interviews

1. Joins: Understand how to use INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN to combine data from different tables, ensuring you can retrieve the needed information from relational databases.

2. Group By and Aggregate Functions: Master GROUP BY along with aggregate functions like COUNT(), SUM(), AVG(), MAX(), and MIN() to summarize data and generate meaningful reports.

3. Data Filtering: Use WHERE, HAVING, and CASE statements to filter and manipulate data effectively, enabling precise data extraction based on specific conditions.

4. Subqueries: Employ subqueries to retrieve data nested within other queries, allowing for more complex data retrieval and analysis scenarios.

5. Window Functions: Leverage window functions such as ROW_NUMBER(), RANK(), DENSE_RANK(), and LAG() to perform calculations across a set of table rows, returning result sets with contextual calculations.

6. Data Types: Ensure proficiency in choosing and handling various SQL data types (VARCHAR, INT, DATE, etc.) to store and query data accurately.

7. Indexes: Learn how to create and manage indexes to speed up the retrieval of data from databases, particularly in tables with large volumes of records.

8. Normalization: Apply normalization principles to organize database tables efficiently, reducing redundancy and improving data integrity.

9. CTEs and Views: Utilize Common Table Expressions (CTEs) and Views to write modular, reusable, and readable queries, making complex data analysis tasks more manageable.

10. Data Import/Export: Know how to import and export data between SQL databases and other tools like BI tools to facilitate comprehensive data analysis workflows.

Here you can find SQL Interview Resources👇
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4 Career Paths In Data Analytics

1) Data Analyst:

Role: Data Analysts interpret data and provide actionable insights through reports and visualizations.

They focus on querying databases, analyzing trends, and creating dashboards to help businesses make data-driven decisions.

Skills: Proficiency in SQL, Excel, data visualization tools (like Tableau or Power BI), and a good grasp of statistics.

Typical Tasks: Generating reports, creating visualizations, identifying trends and patterns, and presenting findings to stakeholders.


2)Data Scientist:

Role: Data Scientists use advanced statistical techniques, machine learning algorithms, and programming to analyze and interpret complex data.

They develop models to predict future trends and solve intricate problems.
Skills: Strong programming skills (Python, R), knowledge of machine learning, statistical analysis, data manipulation, and data visualization.

Typical Tasks: Building predictive models, performing complex data analyses, developing machine learning algorithms, and working with big data technologies.


3)Business Intelligence (BI) Analyst:

Role: BI Analysts focus on leveraging data to help businesses make strategic decisions.

They create and manage BI tools and systems, analyze business performance, and provide strategic recommendations.

Skills: Experience with BI tools (such as Power BI, Tableau, or Qlik), strong analytical skills, and knowledge of business operations and strategy.

Typical Tasks: Designing and maintaining dashboards and reports, analyzing business performance metrics, and providing insights for strategic planning.

4)Data Engineer:

Role: Data Engineers build and maintain the infrastructure required for data generation, storage, and processing. They ensure that data pipelines are efficient and reliable, and they prepare data for analysis.

Skills: Proficiency in programming languages (such as Python, Java, or Scala), experience with database management systems (SQL and NoSQL), and knowledge of data warehousing and ETL (Extract, Transform, Load) processes.

Typical Tasks: Designing and building data pipelines, managing and optimizing databases, ensuring data quality, and collaborating with data scientists and analysts.

Hope this helps you 😊

📁 Data Analyst Resume Tips 💼📊

1️⃣ Start with a Strong Summary
– 2-3 lines summarizing your experience, tools, and impact.
– Example: “Detail-oriented Data Analyst with 2+ years of experience in SQL, Excel, and Power BI. Passionate about turning data into actionable insights.”

2️⃣ Highlight Technical Skills Clearly
– Tools: SQL, Excel, Python, Power BI, Tableau
– Concepts: Data Cleaning, EDA, Dashboarding, A/B Testing

3️⃣ Use Impact-Driven Bullet Points
✅ “Improved reporting speed by 40% using Power BI”
✅ “Analyzed customer churn with Python, improving retention by 12%”
❌ “Worked with data daily”

4️⃣ Include Projects with Business Context
– Name the project, tool used, and business outcome.
– Add GitHub or portfolio links.

5️⃣ Quantify Your Work
– Numbers catch attention!
– Use KPIs: revenue impact, time saved, accuracy improved

6️⃣ Education & Certifications
– Mention degrees, online courses, bootcamps
– Highlight certificates like Google Data Analytics, Excel for Business, etc.

7️⃣ Tailor Your Resume to the Job
– Use keywords from the job denoscription
– Rearrange skills/projects based on the role focus

📝 Keep it 1-page (for 0–3 yrs exp), well-formatted, and typo-free!

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📌 7 Steps to Ace Your Data Analyst Job Interview 💼✅

1️⃣ Know the Role Inside-Out
– Read the JD carefully
– Understand tools, responsibilities, and business domain
– Align your experience with their requirements

2️⃣ Revise Core Skills
– SQL: Joins, Window functions, Aggregations
– Excel: VLOOKUP, Pivot Tables, Charts
– Power BI/Tableau: Dashboarding, Filters, Slicers
– Python: Pandas, NumPy, basic EDA (if required)

3️⃣ Prepare for Case Studies & Scenarios
– Practice questions like:
“How would you find top-selling products?”
“How would you reduce churn?”
– Focus on business thinking + technical approach

4️⃣ Show Your Projects Smartly
– Pick 2-3 relevant projects
– Explain goal, tools, insights, and impact
– Keep it concise and results-driven

5️⃣ Practice Behavioral Questions
– “Tell me about a time you handled a data error”
– “Describe a challenging project”
– Use STAR method: Situation, Task, Action, Result

6️⃣ Ask Insightful Questions
– “How does your team use data to make decisions?”
– “What tools/tech stack do analysts use here?”

7️⃣ Follow-Up Professionally
– Send a short thank-you message/email
– Reaffirm your interest and value

🧠 Confidence = Preparation + Practice!

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Power BI DAX Cheatsheet 🚀

1️⃣ Basics of DAX (Data Analysis Expressions)

DAX is used to create custom calculations in Power BI.

It works with tables and columns, not individual cells.

Functions in DAX are similar to Excel but optimized for relational data.


2️⃣ Aggregation Functions

SUM(ColumnName): Adds all values in a column.

AVERAGE(ColumnName): Finds the mean of values.

MIN(ColumnName): Returns the smallest value.

MAX(ColumnName): Returns the largest value.

COUNT(ColumnName): Counts non-empty values.

COUNTROWS(TableName): Counts rows in a table.


3️⃣ Logical Functions

IF(condition, result_if_true, result_if_false): Conditional statement.

SWITCH(expression, value1, result1, value2, result2, default): Alternative to nested IF.

AND(condition1, condition2): Returns TRUE if both conditions are met.

OR(condition1, condition2): Returns TRUE if either condition is met.


4️⃣ Time Intelligence Functions

TODAY(): Returns the current date.

YEAR(TODAY()): Extracts the year from a date.

TOTALYTD(SUM(Sales[Amount]), Date[Date]): Year-to-date total.

SAMEPERIODLASTYEAR(Date[Date]): Returns values from the same period last year.

DATEADD(Date[Date], -1, MONTH): Shifts dates by a specified interval.


5️⃣ Filtering Functions

FILTER(Table, Condition): Returns a filtered table.

ALL(TableName): Removes all filters from a table.

ALLEXCEPT(TableName, Column1, Column2): Removes all filters except specified columns.

KEEPFILTERS(FilterExpression): Keeps filters applied while using other functions.


6️⃣ Ranking & Row Context Functions

RANKX(Table, Expression, [Value], [Order]): Ranks values in a column.

TOPN(N, Table, OrderByExpression): Returns the top N rows based on an expression.


7️⃣ Iterators (Row-by-Row Calculations)

SUMX(Table, Expression): Iterates over a table and sums calculated values.

AVERAGEX(Table, Expression): Iterates over a table and finds the average.

MAXX(Table, Expression): Finds the maximum value based on an expression.


8️⃣ Relationships & Lookup Functions

RELATED(ColumnName): Fetches a related column from another table.

LOOKUPVALUE(ColumnName, SearchColumn, SearchValue): Returns a value from a column where another column matches a value.


9️⃣ Variables in DAX

VAR variableName = Expression RETURN variableName

Improves performance by reducing redundant calculations.


🔟 Advanced DAX Concepts

Calculated Columns: Created at the column level, stored in the data model.

Measures: Dynamic calculations based on user interactions in Power BI visuals.

Row Context vs. Filter Context: Understanding how DAX applies calculations at different levels.

Free Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c

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Interview guide for Data Analyst Role

When interviewing for a Data Analyst role as a fresher, you’ll likely encounter questions that focus on your understanding of data analysis concepts, technical skills, and problem-solving abilities. Here’s a comprehensive list of commonly asked interview questions:

1. General and Behavioral Questions

• Tell me about yourself.
• Why do you want to become a Data Analyst?
• What do you know about our company and why do you want to work here?
• Describe a time when you solved a problem using data.
• How do you prioritize tasks and manage deadlines?
• Tell me about a time when you worked in a team to complete a project.

2. Technical Questions

• What are the different types of joins in SQL? (Expect variations of SQL questions)
• How would you handle missing or inconsistent data?
• What is normalization? Why is it important?
• Explain the difference between primary keys and foreign keys in a database.
• What are the most common data types in SQL?
• How do you perform data cleaning in Excel?

3. Analytical Skills and Problem-Solving

• How would you find outliers in a dataset?
• How would you approach analyzing a dataset with 1 million rows?
• If given two datasets, how would you combine them?
• What steps would you take if your results didn’t match stakeholders’ expectations?
• How would you identify trends or patterns in a dataset?

4. Excel-Related Questions

• What are pivot tables and how do you use them?
• Explain VLOOKUP and HLOOKUP.
• How would you handle large datasets in Excel?
• What is the use of conditional formatting?
• How would you create a dashboard in Excel?
• How can you create a custom formula in Excel?

5. SQL Questions

• Write a SQL query to find the second highest salary in a table.
• What is the difference between WHERE and HAVING clauses?
• How would you optimize a slow-running query?
• What is the difference between UNION and UNION ALL?
• What is a subquery, and when would you use it?

6. Statistics and Data Analysis

• Explain the difference between mean, median, and mode.
• What is standard deviation, and why is it important?
• What is regression analysis? Can you explain linear regression?
• What is correlation, and how is it different from causation?
• What are some key metrics you would track for a marketing campaign?

7. Data Visualization and Tools

• What tools have you used for data visualization?
• Explain a situation where you used charts to tell a story.
• What is your experience with tools like Tableau or Power BI?
• How would you decide which chart type to use for visualizing data?
• Have you ever created a dashboard? If yes, what were the key features?

8. Python/R (If mentioned on your resume)

• What libraries do you use in Python for data analysis?
• How would you import a dataset and perform basic analysis in Python?
• What are some common data manipulation functions in pandas?
• How do you handle missing values in Python?

9. Scenario-Based Questions

• Imagine you are given a dataset of customer purchases; how would you segment the customers?
• You are given sales data for the past five years. What steps would you take to forecast the next year’s sales?
• If you find conflicting data in a report, how would you handle the situation?
• Describe a project where you identified key insights using data.

10. Aptitude or Logical Questions

• Some companies also include questions testing your quantitative aptitude, logical reasoning, and pattern recognition to gauge problem-solving skills.

Tips to Prepare:

1. Strengthen your Basics: Brush up on SQL, Excel, and statistical concepts.
2. Mock Interviews: Practice explaining your thought process for data problems.
3. Projects: Be ready to discuss any projects or internships you’ve done.
4. Stay Current: Read about trends in data analysis and business intelligence.

Hope this helps you 😊