8 Altair SQL Interview Questions (Updated 2024)
At Altair Engineering, SQL is used frequently for analyzing and manipulating aerospace simulation data, and managing complex raw datasets for client-based projects, and as part of Altair's data analytics solutions group. So, it shouldn't surprise you that Altair asks SQL questions during interviews for Data Analytics, Data Science, and Data Engineering jobs.
As such, to help you prepare for the Altair SQL interview, we'll cover 8 Altair Engineering SQL interview questions – able to solve them?
8 Altair SQL Interview Questions
SQL Question 1: Calculate the Average Rating for Each Product Per Month
You are given a table, , that keeps track of all product reviews submitted by users on Altair's website. Each row contains a , , the of the review, the of the product being reviewed, and the number of given to the product (from 1 to 5).
Your task is to write a SQL query that will calculate the average rating for each product for each month. The result should be a table with the following columns: month (), product (), and average stars (). The column should contain the month number of the (i.e., for January, for February, etc.), and should be rounded to two decimal places.
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 6171 | 123 | 2022-06-08 | 50001 | 4 |
| 7802 | 265 | 2022-06-10 | 69852 | 4 |
| 5293 | 362 | 2022-06-18 | 50001 | 3 |
| 6352 | 192 | 2022-07-26 | 69852 | 3 |
| 4517 | 981 | 2022-07-05 | 69852 | 2 |
Answer:
In this query we're using the SQL function to separate the month from the . Then we're grouping by both the extracted month and . Finally, we calculate the average number of stars for each group using , rounding to two decimal places. The clause is used to sort the results first by month and then by .
Example Output:
| mth | product | avg_stars |
|---|---|---|
| 6 | 50001 | 3.50 |
| 6 | 69852 | 4.00 |
| 7 | 69852 | 2.50 |
For more window function practice, solve this Uber SQL Interview Question within DataLemur's online SQL coding environment:
SQL Question 2: Calculating Click-Through-Rates at Altair Inc.
Altair Inc. is a company that relies heavily on its ad campaigns for selling digital products. They are interested in understanding and improving their click-through rates on their digital ads, as well as product addition to cart rates. As a Data Analyst, your task is to calculate the click-through rate (CTR) and conversion rate (CR) for their ads and products respectively for each campaign.
Table 1:
| ad_id | user_id | click_date | campaign_id |
|---|---|---|---|
| 1001 | 1 | 01/10/2022 00:00:00 | 501 |
| 1002 | 2 | 01/10/2022 00:00:00 | 502 |
| 1003 | 3 | 01/10/2022 00:00:00 | 503 |
| 1004 | 4 | 01/11/2022 00:00:00 | 503 |
| 1005 | 5 | 01/11/2022 00:00:00 | 502 |
Table 2:
| add_id | user_id | add_date | product_id |
|---|---|---|---|
| 2001 | 1 | 01/10/2022 00:00:00 | 6001 |
| 2002 | 2 | 01/10/2022 00:00:00 | 6002 |
| 2003 | 6 | 01/11/2022 00:00:00 | 6003 |
| 2004 | 7 | 01/11/2022 00:00:00 | 6004 |
| 2005 | 8 | 01/11/2022 00:00:00 | 6005 |
Answer:
CTR is calculated as the number of clicks for a specific campaign over the total number of users who clicked ads and CR is calculated as the number of product additions to the cart for a specific product over the total number of users who added a product to their cart.
To practice a similar SQL interview question on DataLemur's free online SQL code editor, try this SQL interview question asked by Facebook:
SQL Question 3: Can you describe the difference between a correlated and a non-correlated sub-query?
hile a correlated subquery relies on columns in the main query's FROM clause and cannot function independently, a non-correlated subquery operates as a standalone query and its results are integrated into the main query.
An example correlated sub-query:
This correlated subquery retrieves the names and salaries of Altair employees who make more than the average salary for their department. The subquery references the department column in the main query's FROM clause (e1.department) and uses it to filter the rows of the subquery's FROM clause (e2.department).
An example non-correlated sub-query:
This non-correlated subquery retrieves the names and salaries of Altair employees who make more than the average salary for the Data Science department (which honestly should be very few people since Data Scientists are awesome and deserve to be paid well).The subquery is considered independent of the main query can stand alone. Its output (the average salary for the Data Science department) is then used in the main query to filter the rows of the Altair employees table.
SQL Question 4: Find the average revenue per month for each product
Altair is a company that sells technological products. Your task is to write a SQL query that provides the average revenue per month for each product sold by Altair.
Assume that you have access to a table that stores all transactions made and has the following structure:
Example Input:
| sale_id | product_id | sale_date | quantity | sale_price |
|---|---|---|---|---|
| 5291 | 41021 | 2022-05-23 | 3 | 150 |
| 7234 | 37519 | 2022-05-28 | 2 | 100 |
| 6538 | 41021 | 2022-06-10 | 1 | 150 |
| 7935 | 18437 | 2022-06-15 | 2 | 200 |
| 8617 | 37519 | 2022-07-05 | 4 | 100 |
The solution should look like this:
Example Output:
| month | product_id | avg_revenue |
|---|---|---|
| 5 | 41021 | 450 |
| 5 | 37519 | 200 |
| 6 | 41021 | 150 |
| 6 | 18437 | 400 |
| 7 | 37519 | 400 |
Answer:
This PostgreSQL query groups the sales by month and product_id, and then calculates the average revenue for each grouping. The function is used to get the month from the sale_date and the average revenue is calculated by multiplying the quantity by the sale_price and then applying the aggregate function. The results are ordered by month and product_id for easier reading.
SQL Question 5: What is database denormalization?
Denormalization is the process of modifying a database schema in a way that deviates from the typical rules of normalization (1NF, 2NF, 3NF, etc.).
Denormalization is often used to improve the performance of a database, particularly when it is being used for reporting and analytical purposes (rather than in an Online Transaction Processing (OLTP) manager).
By duplicating data, denormalization can reduce the number of expensive joins required to retrieve data, which can improve query performance. However, denormalization can also cause problems such as increased data redundancy and the need for more complex update and delete operations.
SQL Question 6: Analyzing customer and product performance
Altair is a company that sells multiple products, and it maintains a database to track its customers' details, the products they've purchased, and the reviews they've given for those products.
You are asked to write a SQL query to analyze the customer database and join this data with the product review information to provide a report on average product ratings for each month.
Weeks are numbered according to the ISO-8601 standard, i.e., the first week of the year is the one that contains the 4th of January.
The given tables are:
Example Input:
| customer_id | first_name | last_name | |
|---|---|---|---|
| 123 | John | Doe | john.doe@example.com |
| 265 | Jane | Smith | jane.smith@example.com |
| 362 | Mary | Johnson | mary.johnson@example.com |
| 192 | James | Williams | james.williams@example.com |
| 981 | Patricia | Brown | patricia.brown@example.com |
Example Input:
| review_id | customer_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 6171 | 123 | 2022-06-08 | 50001 | 4 |
| 7802 | 265 | 2022-06-10 | 69852 | 4 |
| 5293 | 362 | 2022-06-18 | 50001 | 3 |
| 6352 | 192 | 2022-07-26 | 69852 | 3 |
| 4517 | 981 | 2022-07-05 | 69852 | 2 |
Answer:
This PostgreSQL query first joins the customers table with the product_reviews table on the common field customer_id. Then, it groups by the month part of the submit_date and product_id, and calculates the average star rating for each group. The result will be a report on the average review stars for each product, broken down by month.
Because joins come up so often during SQL interviews, take a stab at this SQL join question from Spotify:
SQL Question 7: What are some similarities and differences between unique and non-unique indexes?
{#Question-7}
Unique indexes help ensure that there are no duplicate key values in a table, maintaining data integrity. They enforce uniqueness whenever keys are added or changed within the index.
To define a unique index in PostgreSQL, you can use the following syntax:
To define a non-unique index in PostgreSQL, you can use the following syntax:
Non-unique indexes on the other hand, are used to improve query performance by maintaining a sorted order of frequently used data values, but they do not enforce constraints on the associated table.
SQL Question 8: Calculate and Round off Employee Performance Score
The HR department at Altair wants to calculate the performance score for each employee. The performance scores are calculated as follows:
- Monthly sales made counts as five percent of the score.
- Quarterly customer reviews count as three percent of the score.
- Any leftover incidents from the month subtracts one percent of the score.
The performance score calculation needs to be rounded off to the nearest integer using the ROUND() function. If the score is 85.8, for example, it should be returned as 86.
Remember, you need to handle any negative number scores using the ABS() function. Also, you need to examine if the employee's final score is divisible by 6 using the MOD() function. If it is, the employee might receive an incremental bonus.
You have information about the monthly sales, quarterly reviews, and number of incidents from two tables.
Example Input:
| employee_id | month | sales_made |
|---|---|---|
| 1 | 1 | 250 |
| 2 | 1 | 300 |
| 3 | 1 | 400 |
| 1 | 2 | 350 |
| 2 | 2 | 275 |
| 3 | 2 | 325 |
Example Input:
| employee_id | quarter | reviews_received | incidents_left |
|---|---|---|---|
| 1 | 1 | 2 | 1 |
| 2 | 1 | 3 | 0 |
| 3 | 1 | 1 | 2 |
| 1 | 2 | 3 | 0 |
| 2 | 2 | 2 | 1 |
| 3 | 2 | 4 | 1 |
We want an output table as follows:
Example Output:
| employee_id | month | performance_score | is_bonus_eligible |
|---|---|---|---|
| 1 | 1 | 56 | No |
| 2 | 1 | 54 | Yes |
| 3 | 1 | 69 | No |
| 1 | 2 | 58 | No |
| 2 | 2 | 55 | Yes |
| 3 | 2 | 65 | No |
Answer:
This query first calculates the performance score using the specified arithmetic formula and rounds off the results using the ROUND() function. It then checks if the rounded performance score is divisible by 6 using the MOD() function and adds 'Yes' or 'No' to the new 'is_bonus_eligible' column. The sales and reviews made by an employee are retrieved by joining on the employee's ID from both the sales and reviews tables.
To practice a very similar question try this interactive Facebook App Click-through Rate (CTR) Question which is similar for calculating percentages or this Google Median Google Search Frequency Question which is similar for rounding off calculations.
Preparing For The Altair SQL Interview
The best way to prepare for a Altair SQL interview is to practice, practice, practice.
In addition to solving the earlier Altair SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Netflix, Airbnb, and Amazon.
Each SQL question has multiple hints, full answers and most importantly, there is an online SQL code editor so you can right online code up your SQL query answer and have it executed.
To prep for the Altair SQL interview it is also wise to solve interview questions from other tech companies like:
In case your SQL skills are weak, don't worry about diving straight into solving questions – refresh your SQL knowledge with this free SQL for Data Analytics course.
This tutorial covers SQL topics like filtering with LIKE and LEAD window function – both of which pop up routinely in Altair SQL interviews.
Altair Engineering Data Science Interview Tips
What Do Altair Data Science Interviews Cover?
Beyond writing SQL queries, the other question categories to practice for the Altair Data Science Interview are:
- Stats Interview Questions
- Coding Questions in Python or R
- Open-Ended Data Case Studies
- ML Interview Questions
- Behavioral Based Interview Questions
How To Prepare for Altair Data Science Interviews?
The best way to prepare for Altair Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from Google, Microsoft & tech startups
- A Crash Course on SQL, AB Testing & ML
- Great Reviews (900+ 5-star reviews on Amazon)