10 HP SQL Interview Questions (Updated 2024)
At HP, SQL is used all the damn time for analyzing product performance data for improvement, and managing customer databases for targeted marketing strategies. Because of this, HP often tests SQL questions during interviews for Data Science, Analytics, and & Data Engineering jobs.
As such, to help you prepare for the HP SQL interview, we've curated 10 HP SQL interview questions – can you solve them?
10 HP SQL Interview Questions
SQL Question 1: Compute Average Product Ratings Per Month
As an employee at HP, one of your responsibilities is to analyze the monthly performance of HP products based on customer reviews. You are asked to determine the average rating (stars) for each product on a monthly basis.
For this task, you have the table that records every review submitted for each HP product. The table has the following columns:
- (integer): The id of the review.
- (integer): The id of the user who submitted the review.
- (timestamp): The date and time when the review was submitted.
- (integer): The id of the product being reviewed.
- (integer): The star rating of the review from 1 to 5.
You need to write a PostgreSQL query that calculates the average star rating for each product per month. You need to use SQL window functions in your query.
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 6171 | 123 | 2022-06-08 00:00:00 | 50001 | 4 |
| 7802 | 265 | 2022-06-10 00:00:00 | 69852 | 4 |
| 5293 | 362 | 2022-06-18 00:00:00 | 50001 | 3 |
| 6352 | 192 | 2022-07-26 00:00:00 | 69852 | 3 |
| 4517 | 981 | 2022-07-05 00:00:00 | 69852 | 2 |
Example Output:
| mth | product_id | avg_stars |
|---|---|---|
| 6 | 50001 | 3.50 |
| 6 | 69852 | 4.00 |
| 7 | 69852 | 2.50 |
Answer:
Here, we used the function as a window function to calculate the average star rating of each product for each month. The function is used to extract the month from the timestamp. Then we used to separate the data into windows partitioned by the month and . The statement is used to group the records by month and product, and the statement is used to sort the records by month and product.
To practice a similar window function interview problem which uses RANK() on DataLemur's free online SQL code editor, try this Amazon BI Engineer interview question:
SQL Question 2: Software Sale Performance Analysis
HP produces numerous software products, and it's important for them to know how these products are performing in terms of sales. Suppose you are given two tables and , where contains information about each software product and records all the transactions made.
Design a database to model these aspects and define the relationships. After the design, write a PostgreSQL query to find out the total sales for each software product in the last quarter (October, November, December).
Example Input
| product_id | product_name | product_type |
|---|---|---|
| 101 | Photoshop | Design |
| 102 | Windows 10 | Operating System |
| 103 | Auto CAD | Engineering |
Example Input
| sale_id | product_id | sale_date | quantity |
|---|---|---|---|
| 4001 | 101 | 12/10/2022 | 6 |
| 4002 | 102 | 11/01/2022 | 9 |
| 4003 | 101 | 12/15/2022 | 10 |
| 4004 | 103 | 11/20/2022 | 7 |
| 4005 | 102 | 10/30/2022 | 8 |
Answer:
In PostgreSQL, you would get the total sales for each software product in the last quarter using below query:
This query first joins and tables using . It then filters out the records from last quarter using the function. Finally, it calculates the total sales for each product using and .
The relationships between the tables could be outlined as: A product can have multiple sales, but each sale is for one specific product. Therefore, a one-to-many relationship exists from products to sales.
SQL Question 3: Can you explain the purpose of the constraint and give an example of when you might use it?
The CHECK constraint is used to enforce rules on the data in a specific column. If a row is inserted or updated with data that does not follow the CHECK constraint's rule, the operation will fail. The CHECK constraint is often used with other constraints, such as NOT NULL or UNIQUE, to ensure that data meets certain conditions. You may want to use a CHECK constraint in your database if you want to maintain the quality and reliability of your data by ensuring that it meets specific criteria.
For example, you might use a CHECK constraint to ensure that a column contains only positive numbers, or that a date is within a certain range.
SQL Question 4: Filter Customers Based On Purchase And Support Tickets
HP wants to identify customers who have purchased printers in the last two years and have opened more than 5 support tickets. Use the following two tables to write a query that returns the customer ids of these customers.
Example Input:
| purchase_id | customer_id | product_id | purchase_date |
|---|---|---|---|
| 101 | 212 | HP001 | 2021-09-14 |
| 102 | 345 | HP002 | 2020-08-15 |
| 103 | 212 | HP002 | 2020-07-10 |
| 104 | 456 | HP001 | 2020-05-20 |
| 105 | 345 | HP003 | 2019-10-20 |
Example Input:
| ticket_id | customer_id | issue_date |
|---|---|---|
| 301 | 212 | 2022-06-01 |
| 302 | 345 | 2022-01-15 |
| 303 | 345 | 2022-07-01 |
| 304 | 345 | 2022-02-01 |
| 305 | 212 | 2021-12-15 |
| 306 | 212 | 2021-10-20 |
| 307 | 212 | 2021-07-01 |
| 308 | 212 | 2021-05-01 |
| 309 | 345 | 2021-02-10 |
| 310 | 345 | 2021-01-01 |
Answer:
This query first uses a subquery to find all customers who have opened more than 5 support tickets. Then it joins this with the purchases table to match based on the customer id. The WHERE clause is used to filter those purchases related to printers (product_id starts with 'HP') and the purchase happened in the last two years. Since the query might return multiple records for a customer due to multiple purchases, the GROUP BY clause is used to give a unique list of customer ids.
SQL Question 5: Can you describe the difference between a correlated and a non-correlated sub-query?
A correlated sub-query is one that is linked to the outer query and cannot be executed on its own. It uses the outer query to filter or transform data by referencing a column from the outer query, while the outer query uses the results of the inner query. On the other hand, a non-correlated sub-query is independent of the outer query and can be executed on its own. It does not reference any columns from the outer query and is used to retrieve additional data for the outer query.
Correlated sub-queries are slower to execute, as they have to be re-evaluated for each row of the outer query, while non-correlated sub-queries are faster, as they only have to be executed once.
SQL Question 6: Average Printing Pages on HP Printers
As an analyst at HP, you might be asked to find the average number of pages printed per month by each model of HP printers. This can help the company evaluate the utilization and efficiency of its different printers, and potentially offer insights for improvements. The question would be: "Find the average number of pages printed per month for each printer model for the year 2021".
Example Input:
| usage_id | printer_model | usage_date | pages_printed |
|---|---|---|---|
| 101 | LaserJet 401dn | 01/05/2021 | 500 |
| 102 | OfficeJet Pro 6978 | 01/10/2021 | 300 |
| 103 | LaserJet 401dn | 02/05/2021 | 550 |
| 104 | OfficeJet Pro 6978 | 02/14/2021 | 325 |
| 105 | LaserJet 401dn | 03/05/2021 | 520 |
| 106 | OfficeJet Pro 6978 | 03/12/2021 | 350 |
Example Output:
| month | model | avg_pages |
|---|---|---|
| 1 | LaserJet 401dn | 500 |
| 1 | OfficeJet Pro 6978 | 300 |
| 2 | LaserJet 401dn | 550 |
| 2 | OfficeJet Pro 6978 | 325 |
| 3 | LaserJet 401dn | 520 |
| 3 | OfficeJet Pro 6978 | 350 |
Answer:
A PostgreSQL query for this question might look like:
In this query, we are first extracting the month and year from the usage_date using the EXTRACT() function. We filter only the records from the year 2021. Then, we group the rows by the printer model and the month of usage, before calculating the average number of pages printed using the AVG() function. The results are ordered first by month, then by model.
To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for determining averages over a time period or this Facebook Active User Retention Question which is similar for analyzing usage data over time.
SQL Question 7: What sets relational and NoSQL databases apart?
While knowing this answer is beyond the scope of most Data Analyst & Data Science interviews, Data Engineers at HP should be at least aware of SQL vs. NoSQL databases.
Relational databases and non-relational (NoSQL) databases have some key differences, particularly in terms of how data is stored. Whereas relational databases have tables, made up of rows and columns, NoSQL databases use various data models like:
- Wide-Column Stores – this database uses tables, rows, and columns, but unlike a relational database, the names and format of the columns can vary from row to row within the same table
- Key-Value Stores – instead of rows and columns, you have keys, where each key is associated with only one value in a collection (similar to a Python dictionary data structure!)
- Graph Stores – instead of rows of data, you have nodes, and then can also have edges between entities (much like a Graph Data Structure for those who've taken a Computer Science data structures & algorithms class)
This added flexibility makes NoSQL databases well-suited for handling non-tabular data or data with a constantly changing format. However, this flexibility comes at the cost of ACID compliance, which is a set of properties (atomic, consistent, isolated, and durable) that ensure the reliability and integrity of data in a database. While most relational databases are ACID-compliant, NoSQL databases may not provide the same level of guarantees.
SQL Question 8: Computing Click-through Conversion Rates for HP Products
As a data analyst at HP, you have been given two tables: One tracking all the site visits and views of products () and another tracking all the digital product purchases ().
Your task is to write a SQL query that provides the click-through conversion rate, defined as the number of unique visitors who purchase every product after viewing it, as a proportion of the total number of unique visitors who viewed the product.
(create some sample data for the problem in markdown-formatted tables)
Example Input:
| view_id | user_id | view_date | product_id |
|---|---|---|---|
| 3412 | 765 | 07/25/2022 00:00:00 | 14567 |
| 2234 | 456 | 07/26/2022 00:00:00 | 14567 |
| 9056 | 987 | 07/26/2022 00:00:00 | 18792 |
| 5162 | 765 | 07/27/2022 00:00:00 | 14567 |
| 6195 | 456 | 07/28/2022 00:00:00 | 18792 |
Example Input:
| purchase_id | user_id | purchase_date | product_id |
|---|---|---|---|
| 5432 | 765 | 07/25/2022 00:00:00 | 14567 |
| 8835 | 987 | 07/27/2022 00:00:00 | 18792 |
Answer:
This query joins to on and to correlate each product view with a purchase (if it occurred). The click-though conversation rate is then calculated as the number of unique users who purchased the product after viewing it over the total number of unique users who viewed the product, grouped by .
The is used because we want to keep all the views, even those that did not result in a purchase. And we calculate the distinct count of user_id to only count each user once per product.
To solve another question about calculating rates, try this SQL interview question from TikTok on DataLemur's interactive coding environment:
SQL Question 9: Find the Average Selling Price per Laptop Model per Month
As an analyst at HP, you'd like to track the sales of HP laptops. You want to find out the average selling price per model per month for the year 2020. Write an SQL query to accomplish this.
The sales data is stored in a table with the following schema:
Example Input:
| sales_id | laptop_model | sale_date | sale_price |
|---|---|---|---|
| 89012 | Pavilion 15 | 01/05/2020 | 600 |
| 92382 | Spectre x360 | 01/09/2020 | 1200 |
| 86742 | Pavilion 15 | 02/17/2020 | 560 |
| 90287 | Spectre x360 | 02/19/2020 | 1220 |
| 96354 | Pavilion 15 | 02/26/2020 | 580 |
You are expected to output the average selling price per model per month.
Example Output:
| month | laptop_model | avg_sale_price |
|---|---|---|
| 01 | Pavilion 15 | 600.00 |
| 01 | Spectre x360 | 1200.00 |
| 02 | Pavilion 15 | 570.00 |
| 02 | Spectre x360 | 1220.00 |
Answer:
Here is a PostgreSQL query which solves the problem:
This query groups the laptop sales by month and model and calculates the average sale price for each group. It filters the sales for the year 2020 using the WHERE clause. The ORDER BY clause is used to sort the result by month and model.
SQL Question 10: What's the difference between the and operators?
The operator combines two or more results from multiple SELECT queries into a single result. If it encounters duplicate rows, the multiple copies are removed (so there's only one instance of each would-be duplicate in the result set). Here's an example of a operator which combines all rows from and (making sure each row is unique):
The operator is similar to the operator but it does NOT remove duplicate rows!
Preparing For The HP SQL Interview
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the HP SQL interview is to solve as many practice SQL interview questions as you can!
In addition to solving the earlier HP SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like FAANG (FB, Apple, Amazon, Netflix, Google).
Each interview question has multiple hints, fully explained answers along with a discussion board to see how others solved it and crucially, there is an interactive SQL code editor so you can instantly run your SQL query and have it checked.
To prep for the HP SQL interview it is also useful to practice SQL problems from other tech companies like:
In case your SQL foundations are weak, don't worry about jumping right into solving questions – strengthen your SQL foundations with this SQL interview tutorial.
This tutorial covers things like window functions and handling NULLs in SQL – both of these show up often in HP SQL interviews.
HP Data Science Interview Tips
What Do HP Data Science Interviews Cover?
Beyond writing SQL queries, the other topics to prepare for the HP Data Science Interview are:
- Probability & Statistics Questions
- Python or R Coding Questions
- Analytics and Product-Metrics Questions
- Machine Learning Questions
- Behavioral & Resume-Based Questions
How To Prepare for HP Data Science Interviews?
The best way to prepare for HP Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from FAANG tech companies
- A Crash Course covering SQL, AB Testing & ML
- Amazing Reviews (900+ 5-star reviews on Amazon)