Subex Data Scientist Interview Questions and Answers

Q1. What is Machine Learning, Difference among AI, ML and DL
Ans. 
Machine Learning is a subset of Artificial Intelligence that uses algorithms to learn from data and make predictions.
• AI (Artificial Intelligence) is the broader concept of machines being able to carry out tasks in a way that we would consider 'smart'.

• ML (Machine Learning) is a subset of AI that focuses on the development of computer programs that can access data and use it to learn for themselves.

• DL (Deep Learning) is ...read more
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Q2. Evaluation Metrics for Classification
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Evaluation metrics are used to assess the performance of classification models.
• Common evaluation metrics include accuracy, precision, recall, F1 score, and ROC-AUC.

• Accuracy measures the overall correctness of the model's predictions.

• Precision measures the proportion of true positive predictions out of all positive predictions.

• Recall measures the proportion of true positive predictions out of all actual positives.

• F1 sco...read more
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Q1. Window Function of SQL
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Window functions in SQL allow you to perform calculations across a set of table rows related to the current row.
• Window functions are used to perform calculations on a set of rows related to the current row.

• They do not reduce the number of rows returned by the query.

• Common window functions include ROW_NUMBER(), RANK(), DENSE_RANK(), and NTILE().
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Q2. Index and Procedure in SQL
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Indexes are used to improve the performance of SQL queries by allowing faster data retrieval. Procedures are reusable SQL code blocks.
• Indexes are used to quickly locate data without having to scan the entire table.

• Procedures are stored SQL code blocks that can be executed multiple times.

• Indexes can be created on columns to speed up search queries.

• Procedures can accept input parameters and return results.
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Q1. Salary Expectation
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Q2. Location Preferemce
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Q1. Difference between Precision and Recall? How to select features? What’s p-value?
Ans. 
Precision and Recall are evaluation metrics for classification models. Feature selection is important for model performance. P-value is a statistical measure.
• Precision is the ratio of true positives to the total predicted positives. Recall is the ratio of true positives to the total actual positives.

• Precision is important when false positives are costly, while recall is important when false negatives are costly.

• Feature...read more
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Q1. Questions on project life cycle: Challenges faced during Data Cleaning and Feature Engineering?
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6 mcqs and 2 sql questions

Q1. Difference between having and where in sql
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Q2. Sql joins and it types
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Q3. Window functions and subqueries
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Q4. Sql scenario question based on joins
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Q5. Kpi's and metrics
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Q1. Business problem related to case study
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Basic Hacker Rank SQL Questions 2 . and 10 MCQ based

Q1. Select customer, sum(amt ) as total_amt from t1 group by customer how many no. of rows it will return and explain this table t1 column name-> dept customer item amt
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Q2. Select * , sum(amt) over (partition by customer) as total_amt from t2
Ans. 
The query selects all columns and calculates the total amount for each customer in table t2.
• The query uses a window function to calculate the sum of 'amt' for each customer in the 't2' table.

• The 'partition by customer' clause divides the result set into partitions based on the 'customer' column.

• The 'select *' statement selects all columns from the table along with the calculated total amount.

• The 'sum(amt) over (partiti...read more
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Q3. How many Customers has purchased same item on the Same day more than onnce
Ans. 
To find customers who purchased the same item multiple times on the same day.
• Identify unique customers who purchased the same item multiple times on the same day

• Check for duplicate transactions by customer and item on the same day

• Aggregate the data to count the number of customers who made multiple purchases of the same item on the same day
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HackerEarth based assessment.
1. 8 qs were stats & prob based multiples choices
2. 2 were medium level sql questions.

Q1. Started with basic sql question like diff btw nosql & sql, join etc
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Q2. Table were given in google sheet & had to write basic level queries on them eg. cte's, agg function, lead & lag func
Ans. 
Basic SQL queries using CTEs, aggregate functions, and window functions like LEAD and LAG in Google Sheets.
• CTE (Common Table Expression) allows you to define a temporary result set. Example: WITH SalesCTE AS (SELECT * FROM Sales) SELECT * FROM SalesCTE;

• Aggregate functions like COUNT, SUM, AVG help summarize data. Example: SELECT COUNT(*) FROM Orders WHERE Status = 'Completed';

• LEAD function retrieves the next row's valu...read more
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Q1. The interviewer was not serious at all, asked silly quizzes instead of business case study, KPI & metrics
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Q2. Prepare for logical reasoning
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Consisted of 10 questions, 2 SQL medium level questions and remaining easy aptitude, profit loss, work time etc

Q1. SQL visualisation
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Q2. SQL Questions theory
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Q1. Questions on self join
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Knowledge. Creative. Innovative

Knowledge new idea reach target reach gaol
Idea screening

Q1. New innovative.... Profit earn.. Reach goal
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Mats knowledge ...creative... Idea