--- title: "Altimetrik Interview Questions: Python, SQL, PySpark Practice" description: "Prepare for Altimetrik interviews with these 10 essential Python, SQL, and PySpark questions and solutions. Covers coding, data transformation, and real-world problem-solving for 2025" url: https://srinimf.com/2025/09/11/altimetrik-interview-questions-2025-python-sql-and-pyspark-explained/ date: 2025-09-11 modified: 2026-10-11 author: "Srini" image: https://i0.wp.com/srinimf.com/wp-content/uploads/2025/09/pexels-photo-7731360.jpeg?fit=1782%2C1300&ssl=1 categories: ["PySpark"] tags: ["AltimetrikInterview", "DataEngineering", "PIVOT", "PySparkInterview", "PythonInterview", "SQLInterview", "UNPIVOT"] type: post lang: en --- # Altimetrik Interview Questions: Python, SQL, PySpark Practice ### Introduction Are you preparing for an [interview at Altimetrik](https://srinimf.com/2025/09/24/top-pyspark-interview-questions/) or a similar data engineering/analytics company? πŸš€ Altimetrik interviews often focus on **problem-solving skills** in **Python, SQL, and PySpark**, with a mix of coding challenges and conceptual questions. Related[Top PySpark & AWS Glue Q&A for Big Data Practitioners](https://srinimf.com/2025/09/24/top-pyspark-aws-glue-qa-for-big-data-practitioners/) In this blog, we’ll cover **10 real-world interview-style questions with detailed solutions**β€”ranging from Python coding (strings, decorators, arrays), SQL queries (joins, aggregation, [window functions](https://www.postgresql.org/docs/current/functions-window.html)), and PySpark transformations (pivot, explode, deduplication). By the end of this [guide](https://srinimf.com/about/), you’ll have a **solid practice set** to boost your confidence for technical interviews. ### πŸ”Ÿ Questions Covered 1. Python – First non-repetitive character in a string 2. Python – Rearrange an array so negatives come first 3. Python – Decorator to log function execution time 4. Python – Flatten a nested list 5. SQL – Latest transaction and total transactions per credit card 6. SQL – Departments with more than 5 employees 7. SQL – Match results summary (played, won, lost, draw per team) 8. PySpark – Remove duplicates by keeping the latest row 9. PySpark – Pivot sales data by year 10. PySpark – Explode comma-separated values into rows # Altimetrik-Style Interview Practice Questions ## 🐍 Python (4 Questions) ### **Q1. [First non-repetitive character](https://docs.python.org/3/library/collections.html) in a string** **Problem:** Given a string, return the first character that does not repeat. **Solution:** ``` from collections import Counterdef first_non_repeating(s: str): freq = Counter(s) for c in s: if freq[c] == 1: return c return Noneprint(first_non_repeating("swiss")) # w ``` ### **Q2. Rearrange an array so that negatives come first** **Problem:** Input `[3, -2, -1, 5, 4, -3]` β†’ Output `[-2, -1, -3, 3, 5, 4]` (order within groups not important). **Solution:** ``` def segregate(arr): i, j = 0, len(arr)-1 while i < j: if arr[i] < 0: i += 1 elif arr[j] >= 0: j -= 1 else: arr[i], arr[j] = arr[j], arr[i] return arrprint(segregate([3, -2, -1, 5, 4, -3])) ``` ### **Q3. Write a [decorator](https://peps.python.org/pep-0318/) that logs function execution time** **Solution:** ``` import timedef log_time(func): def wrapper(*args, **kwargs): start = time.time() result = func(*args, **kwargs) print(f"{func.__name__} took {time.time() - start:.4f}s") return result return wrapper@log_timedef add_numbers(a, b): return a + bprint(add_numbers(5, 7)) ``` ### **Q4. Flatten a nested list** **Problem:** `[1, [2, [3, 4], 5], 6] β†’ [1,2,3,4,5,6]` **Solution:** ``` def flatten(lst): out = [] for x in lst: if isinstance(x, list): out.extend(flatten(x)) else: out.append(x) return outprint(flatten([1, [2, [3, 4], 5], 6])) ``` ## πŸ—„ SQL (3 Questions) ### **Q5. Latest transaction and total transactions per credit card** **Table:** `transactions(credit_card_id, transaction_date, amount)` **Solution:** ``` %sqlCREATE TABLE transactions ( credit_card_id INT, transaction_date DATE, amount DECIMAL(10,2));%sqlINSERT INTO transactions (credit_card_id, transaction_date, amount) VALUES(101, '2025-01-05', 250.00),(101, '2025-01-10', 300.00),(101, '2025-02-02', 150.00),(102, '2025-01-15', 500.00),(102, '2025-01-20', 200.00),(103, '2025-02-01', 450.00),(103, '2025-02-10', 600.00),(103, '2025-03-05', 350.00); ``` Related[Mastering SQL Date Extraction and Monthly Trends Using LAG()](https://srinimf.com/2025/10/14/mastering-sql-date-extraction-and-monthly-trends-using-lag/) ``` SELECT credit_card_id, COUNT(*) AS total_txns, MAX(transaction_date) AS latest_txnFROM transactionsGROUP BY credit_card_id; ``` ### **Q6. Departments with > 5 employees** **Tables:** `employees(emp_id, dept_id)` and `departments(dept_id, dept_name)` **Solution:** ``` WITH dept_counts AS ( SELECT dept_id, COUNT(*) AS emp_count FROM employees GROUP BY dept_id)SELECT d.dept_name, dc.emp_countFROM departments dJOIN dept_counts dc ON d.dept_id = dc.dept_idWHERE dc.emp_count > 5; ``` ### **Q7. Match results summary** **Table:** `matches(team_a, team_b, score_a, score_b)` β†’ find total played, won, lost, draw per team. **Solution:** ``` %sqlCREATE TABLE matches ( team_a VARCHAR(50), team_b VARCHAR(50), score_a INT, score_b INT);%sqlINSERT INTO matches (team_a, team_b, score_a, score_b) VALUES('India', 'Australia', 250, 200), -- India wins('India', 'England', 180, 180), -- Draw('Australia','England', 220, 230), -- England wins('India', 'Australia', 210, 220), -- Australia wins('England', 'India', 190, 200), -- India wins('England', 'Australia', 300, 300); -- Draw ``` ``` %sqlwith cte as ( select team_a as team, score_a as score, score_b as opponent from matches union all select team_b as team, score_b as score , score_a as opponent from matches)select team, count(*) as all_played, sum(case when score > opponent then 1 else 0 end) as wins, sum(case when score < opponent then 1 else 0 end ) as lost, sum(case when score = opponent then 1 else 0 end) as draw from cte group by team order by team ``` ## πŸ”₯ PySpark (3 Questions) ### **Q8. Remove duplicates by keeping latest row** **Data:** `(id, value, updated_at)` β†’ [keep only the latest record per id by removing duplicates](https://spark.apache.org/docs/3.5.6/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.dropDuplicates.html). **Solution:** ``` from pyspark.sql import functions as F, Windoww = Window.partitionBy("id").orderBy(F.col("updated_at").desc())df_latest = df.withColumn("rn", F.row_number().over(w)) \ .filter("rn = 1") \ .drop("rn") ``` ### **Q9. Pivot sales data by year** **Data:** `(region, year, sales)` β†’ [pivot years as columns](https://spark.apache.org/docs/latest/api/python/reference/pyspark.pandas/api/pyspark.pandas.DataFrame.pivot.html). **Solution:** ``` from pyspark.sql import SparkSessionfrom pyspark.sql.functions import *spark = SparkSession.builder.appName("PivotExample").getOrCreate()#PIVOTdata = (["East", 1000, 2022], ["West", 2000, 2022], ["East", 3000, 2022], ["West", 4000, 2022], ["East", 5000, 2023], ["West", 6000, 2024])schema = ["Region", "Sales", "Year"]df = spark.createDataFrame(data, schema)df = df.groupBy("Region").pivot("Year").sum("Sales")df.show()#UNPIVOTdf1 = df.selectExpr("Region", stack(3, '2022','2022', '2022', '2022','2023','2024')as(Year,Sales)))df1.show() ``` [Learn more about pivot and unpivot in PySpark](https://srinimf.com/pivot-vs-unpivot-in-pyspark/). ### **Q10. Explode comma-separated values into rows** **Data:** `(Name, Sports)` β†’ `"Alice", "Badminton,Tennis"` β†’ 2 rows using [explode to expand comma-separated values into rows](https://spark.apache.org/docs/latest/api/python/reference/pyspark.pandas/api/pyspark.pandas.DataFrame.explode.html). **Solution:** ``` from pyspark.sql.functions import split, explode, trimdf2 = df.withColumn("Sport", explode(split("Sports", ",")))df2 = df2.withColumn("Sport", trim(df2["Sport"]))df2.show() ``` ## Extra Questions ### **Q1. Write a SQL query to find the most recent order date for each customer from the `orders1` table, but only include orders placed within the last 30 days.** ``` -- 1. Create tableCREATE TABLE orders1 ( id INT AUTO_INCREMENT PRIMARY KEY, name VARCHAR(100), amount DECIMAL(10,2), ordered_date DATE);-- 2.Insert valuesINSERT INTO orders1 (name, amount, ordered_date) VALUES('Alice', 250.00, '2025-09-10'),('Bob', 180.50, '2025-09-05'),('Charlie', 320.75, '2025-08-30'),('David', 150.00, '2025-08-01'), -- older than 30 days('Eva', 500.00, '2025-09-13'),('Frank', 275.00, '2025-09-01'),('Grace', 420.00, '2025-09-07');-- 3. Solutionselect name, max(ordered_date) as recent_from_orderswhere datediff(CURDATE(), ordered_date) < 30group by name; ``` ### Q2. Write a SQL query to find, for each month, the customer who spent the **highest total amount**. If a customer has multiple orders in the same month, sum them first before comparing with others. ``` -- Create tableCREATE TABLE orders ( id INT AUTO_INCREMENT PRIMARY KEY, name VARCHAR(100), amount DECIMAL(10,2), ordered_date DATE);-- Insert sample data across different monthsINSERT INTO orders (name, amount, ordered_date) VALUES('Alice', 200.00, '2025-01-05'),('Bob', 450.00, '2025-01-12'),('Charlie', 300.00, '2025-01-20'),('Alice', 500.00, '2025-02-03'),('David', 350.00, '2025-02-15'),('Eva', 700.00, '2025-02-25'),-- March('Frank', 600.00, '2025-03-08'),('Grace', 750.00, '2025-03-16'),('Helen', 400.00, '2025-03-30'),-- April('Alice', 900.00, '2025-04-02'),('Bob', 650.00, '2025-04-18'),('Charlie', 800.00, '2025-04-25'),-- May('David', 1000.00, '2025-05-05'),('Eva', 950.00, '2025-05-10'),('Frank', 720.00, '2025-05-20');WITH CTE1 AS ( SELECT NAME, SUM(AMOUNT) as totamt, DATE_FORMAT(ordered_date, '%Y-%m') as month from orders group by name,DATE_FORMAT(ordered_date, '%Y-%m')),CTE2 as( select name, totamt, month, row_number()over(PARTITION BY month order by totamt desc) as rn from CTE1)select name, totamt, month from CTE2 where rn =1; ``` ### Share this: - - [Tweet](https://twitter.com/share) - [![](https://i0.wp.com/assets.pinterest.com/images/pidgets/pinit_fg_en_rect_gray_20.png)](https://www.pinterest.com/pin/create/link/?url=https%3A%2F%2Fsrinimf.com%2F2025%2F09%2F11%2Faltimetrik-interview-questions-2025-python-sql-and-pyspark-explained%2F&media=https%3A%2F%2Fi0.wp.com%2Fsrinimf.com%2Fwp-content%2Fuploads%2F2025%2F09%2Fpexels-photo-7731360.jpeg%3Ffit%3D1200%252C875%26ssl%3D1&description=Altimetrik%20Interview%20Questions%3A%20Python%2C%20SQL%2C%20PySpark%20Practice) - - [Share on Tumblr](https://www.tumblr.com/share) - [Share on WhatsApp (Opens in new window) WhatsApp](https://srinimf.com/2025/09/11/altimetrik-interview-questions-2025-python-sql-and-pyspark-explained/?share=jetpack-whatsapp) - [More](#) - - [Print (Opens in new window) Print](https://srinimf.com/2025/09/11/altimetrik-interview-questions-2025-python-sql-and-pyspark-explained/?share=print) - [Share on Reddit (Opens in new window) Reddit](https://srinimf.com/2025/09/11/altimetrik-interview-questions-2025-python-sql-and-pyspark-explained/?share=reddit) - [Share on Telegram (Opens in new window) Telegram](https://srinimf.com/2025/09/11/altimetrik-interview-questions-2025-python-sql-and-pyspark-explained/?share=telegram) - [Email a link to a friend (Opens in new window) Email](mailto:?subject=%5BShared%20Post%5D%20Altimetrik%20Interview%20Questions%3A%20Python%2C%20SQL%2C%20PySpark%20Practice&body=https%3A%2F%2Fsrinimf.com%2F2025%2F09%2F11%2Faltimetrik-interview-questions-2025-python-sql-and-pyspark-explained%2F&share=email) - ### Like this: Like Loading…