Essential TCS Data Analyst Interview Questions (2025) Quiz

Discover fundamental concepts and best practices commonly assessed in TCS Data Analyst interviews, including SQL operations, Power BI functions, data analysis steps, and problem-solving approaches.

1. INNER JOIN vs LEFT JOIN in SQL

   Which statement best describes the difference between INNER JOIN and LEFT JOIN in SQL when querying two tables?

   1. INNER JOIN returns all rows from both tables; LEFT JOIN returns only matching rows.
   2. INNER JOIN and LEFT JOIN always return the same results.
   3. INNER JOIN returns all rows from the right table; LEFT JOIN returns only matching rows.
   4. INNER JOIN returns only matching rows; LEFT JOIN returns all rows from the left table and matching rows from the right table.

   Explanation: The correct answer summarizes the key difference: INNER JOIN gives only rows present in both tables, while LEFT JOIN includes all left table rows regardless of matches on the right. Option B reverses the behavior, C is incorrect about right table focus, and D is false since the joins typically yield different results.

2. Handling Missing Data

   If you find missing values in a dataset, which of the following is NOT a standard method to handle them?

   1. Randomly filling in with extreme values
   2. Predicting missing values using regression or KNN
   3. Imputing using mean, median, or mode
   4. Dropping rows or columns with missing data

   Explanation: Randomly filling with extreme values is not a recommended method, as it can distort the analysis. The other options—dropping, imputing, and using predictive methods—are common and appropriate ways to handle missing data.

3. Data Analysis Process Steps

   Which of the following steps comes FIRST in the data analysis process?

   1. Interpret results and present insights
   2. Visualize with Power BI or Excel
   3. Analyze with SQL or Python
   4. Define the problem

   Explanation: Data analysis should begin by clearly defining the problem to be solved. Visualization, analysis, and interpretation occur after data collection and cleaning. Starting with visualization or analysis without a defined problem is inappropriate.

4. OLAP vs OLTP

   What is the main purpose of OLTP systems as compared to OLAP systems?

   1. Transactional processing with fast writes
   2. Visualization of business dashboards
   3. Data mining with deep learning
   4. Analytical processing with fast reads

   Explanation: OLTP is designed for fast transactional (write) operations, while OLAP focuses on analytical (read) queries. Options C and D mix unrelated concepts; option B describes OLAP, not OLTP.

5. Power BI CALCULATE() Function

   What is the primary use of the CALCULATE() function in Power BI?

   1. To remove duplicates from a dataset
   2. To visualize data in a chart
   3. To modify the context of calculations using filters
   4. To combine multiple tables

   Explanation: CALCULATE() changes the context by applying filters to calculations. It does not visualize, combine tables, or de-duplicate data based on the context provided.

6. Identifying Outliers

   Which method would NOT help in identifying outliers in a dataset?

   1. Checking for values using the IQR method
   2. Relying solely on the alphabetical order of data
   3. Using boxplots or scatter plots
   4. Calculating Z-scores for data points

   Explanation: Alphabetical order provides no information about data outliers. The IQR method, Z-scores, and visualizations like boxplots or scatter plots are common outlier identification techniques.

7. Normalization in SQL Databases

   What is the primary goal of normalization in SQL databases?

   1. To enhance graphical reports
   2. To increase hardware storage requirements
   3. To merge unrelated tables
   4. To reduce data redundancy and organize data efficiently

   Explanation: Normalization aims to reduce redundancy and logically organize data. It does not focus on graphical reports, increasing storage, or merging unrelated tables.

8. WHERE vs HAVING in SQL

   In an SQL query, how does the HAVING clause differ from the WHERE clause?

   1. WHERE filters only aggregated values, HAVING filters raw rows
   2. HAVING filters groups after aggregation, while WHERE filters rows before grouping
   3. HAVING and WHERE are interchangeable
   4. HAVING is not a valid SQL keyword

   Explanation: The main difference is that WHERE filters rows before grouping and aggregation, while HAVING applies conditions to the aggregated groups. They are not interchangeable, and both are valid SQL clauses.

9. Measures and Dimensions in Power BI

   In Power BI, which best describes a 'dimension'?

   1. A measure used to filter queries
   2. A numeric calculation such as Total Sales
   3. A Python script used in analysis
   4. Descriptive data such as Product or Region

   Explanation: Dimensions are descriptive fields like Product or Region. Numeric calculations are measures; Python scripts are tools, not dimensions; filters can use both measures and dimensions.

10. Analyzing Sales Drop Scenario

    If sales have dropped significantly in one region, what is a good first step in your analysis?

    1. Compare year-over-year or month-over-month sales trends using visuals
    2. Remove all data for that region
    3. Immediately adjust prices for all products
    4. Ignore external factors like holidays

    Explanation: Comparing trends helps identify when and how the sales drop occurred. Options B and C jump to solutions without analysis, and D ignores potentially important external factors.