11 Power Integrations SQL Interview Questions (Updated 2024)
At Power Integrations, SQL is used frequently for extracting and analyzing performance data from product lifecycle databases, and for analyzing data generated from their IoT products. That's why Power Integrations LOVES to ask SQL questions during interviews for Data Science, Data Engineering and Data Analytics jobs.
As such, to help you prepare for the Power Integrations SQL interview, here’s 11 Power Integrations SQL interview questions – able to answer them all?
11 Power Integrations SQL Interview Questions
SQL Question 1: Identify Most Active Customers
Power Integrations would like to identify their most active customers, who are individuals that place a high number of chip orders. This information will help the team identify their most valuable clients, better align their resources, and refine their marketing and sales strategies. Assume we are analyzing for a specific year. The SQL query will need to find customers who have placed more than 50 orders in a single year.
Example Input:
Answer:
The query first filters out orders placed in the year 2022. It then groups these orders by the user_id and counts the number of unique orders (order_id) each user has placed during this period. Finally, it filters out users who have placed more than 50 orders. The output will be the user_id along with the total number of orders they have placed in the year 2022, for users who have placed more than 50 orders.
To practice a super-customer analysis question on DataLemur's free interactive coding environment, try this Microsoft SQL Interview problem:
SQL Question 2: Analyzing Product Sales Performance
Power Integrations is interested in understanding how their products are performing with respect to sales across different regions. As an interviewee, write a SQL query that lists each product, the total sales for each product, and the rank of each product based on sales within each sales region.
| sale_id | product_id | region_id | sales_amount |
|---|---|---|---|
| 101 | 501 | 1 | 50000 |
| 102 | 502 | 1 | 60000 |
| 103 | 502 | 2 | 45000 |
| 104 | 501 | 2 | 39000 |
| 105 | 503 | 3 | 30000 |
| 106 | 501 | 3 | 25000 |
| 107 | 502 | 3 | 41000 |
| 108 | 503 | 1 | 55000 |
We're interested in finding out the sales rank per region, so the output should look something like:
Expected Output
| region_id | product_id | total_sales | sales_rank |
|---|---|---|---|
| 1 | 502 | 60000 | 1 |
| 1 | 501 | 50000 | 2 |
| 1 | 503 | 55000 | 3 |
| 2 | 502 | 45000 | 1 |
| 2 | 501 | 39000 | 2 |
| 3 | 502 | 41000 | 1 |
| 3 | 503 | 30000 | 2 |
| 3 | 501 | 25000 | 3 |
Answer:
In the above query, we first calculate the total sales for each product in each region. We then calculate the rank of each product within each region, using the window function. We partition the data by in order to calculate the rank within each region, and we order by in descending order because we're ranking sales (so higher sales get a lower rank).
p.s. Window functions show up pretty frequently during SQL interviews, so practice the 27+ window function questions on DataLemur
SQL Question 3: DBMS transactions are expected to follow the ACID properties. What are they, and what does each property mean?
A DBMS (database management system), in order to ensure transactions are relaible and correct, tries to mantain the following ACID properties: Atomicity, Consistency, Isolation, and Durability
Here is what each of the ACID properties stands for:
Atomicity: ensures that a transaction is either completed in its entirety, or not completed at all. If a transaction fails halfway, the database does a rollback on the commit.
Consistency: ensures that a transaction will only be completed if adheres to the constraints defined in the database of the DB.
Isolation: ensures that concurrent transactions are isolated from each one another, so that the changes made by one transaction cannot be seen by a 2nd transaction until the 1st transaction is done.
**Durability: ** ensures that once a transaction has been committed, the database permanently stores the results in the DB.
As you can see, it's pretty important for Power Integrations's data systems to be ACID compliant, else they'll be a big problem for their customers!
SQL Question 4: Determine Most Efficient Power Supply Unit
As a PQ engineer in Power Integrations, your role involves the determination of the most efficient power supply units over a given period. Consider that the company produces different power supply unit models with various serial numbers that require testing. You are tasked to maintain a database of these power supply units which includes columns like serial number, model, test date, and efficiency score.
Given the database, you need to come up with an SQL query that would obtain the highest efficiency score for each product model by month.
Example Input:
| serial_no | model | test_date | efficiency_score |
|---|---|---|---|
| SN001 | ModelA | 01/05/2022 | 95 |
| SN002 | ModelB | 02/05/2022 | 88 |
| SN003 | ModelA | 01/06/2022 | 92 |
| SN004 | ModelB | 02/06/2022 | 93 |
| SN005 | ModelC | 03/05/2022 | 90 |
| SN006 | ModelA | 01/07/2022 | 97 |
| SN007 | ModelB | 01/06/2022 | 95 |
Answer:
This query retrieves the maximum efficiency score of each power supply unit model per month by creating a subquery () that groups by the and (extracted from ) and calculates the maximum . The main SELECT statement then retrieves and orders the result in ascending order based on the month and descending order based on the .
SQL Question 5: What does the SQL keyword do?
The keyword removes duplicates from a query.
Suppose you had a table of Power Integrations customers, and wanted to figure out which cities the customers lived in, but didn't want duplicate results.
table:
| name | city |
|---|---|
| Akash | SF |
| Brittany | NYC |
| Carlos | NYC |
| Diego | Seattle |
| Eva | SF |
| Faye | Seattle |
You could write a query like this to filter out the repeated cities:
Your result would be:
| city |
|---|
| SF |
| NYC |
| Seattle |
SQL Question 6: Filtering Customer Orders by Region and Status
Power Integrations wants to analyze their order data. They would like to filter out the orders from the 'Americas' and 'Europe' regions that have either been 'Completed' or are 'In Process'. Create a SQL query that provides this information.
Example Input Table:
| order_id | customer_id | region | order_status | order_date | product_id |
|---|---|---|---|---|---|
| 3731 | 877 | Americas | Completed | 06/10/2022 | 4001 |
| 4578 | 999 | Asia | In Process | 06/12/2022 | 4500 |
| 6234 | 098 | Europe | Completed | 07/15/2022 | 6001 |
| 5225 | 332 | Americas | In Process | 07/20/2022 | 5500 |
| 4923 | 625 | Africa | Completed | 08/05/2022 | 5000 |
Example Output Table:
| order_id | customer_id | region | order_status | order_date | product_id |
|---|---|---|---|---|---|
| 3731 | 877 | Americas | Completed | 06/10/2022 | 4001 |
| 6234 | 098 | Europe | Completed | 07/15/2022 | 6001 |
| 5225 | 332 | Americas | In Process | 07/20/2022 | 5500 |
Answer:
Here is the PostgreSQL query that solves the problem:
This statement filters down to records where is either 'Americas' or 'Europe', and is either 'Completed' or 'In Process'. The result set includes all fields from the original records, as indicated by the wildcard SELECT clause (). The output includes all matching orders, including the relevant , , , , , and values.
SQL Question 7: What's a correlated sub-query? How does it differ from a non-correlated sub-query?
A correlated sub-query is a sub-query that depends on the outer query and cannot be executed separately. It uses the outer query to filter or transform data by referencing a column from it, and the outer query uses the results of the inner query. In contrast, 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 needed by the outer query.
Here is an example of a correlated sub-query:
This query selects the and total sales of all Power Integrations customers in the sales table whose are greater than the average of their own customer group. The sub-query in this case is correlated with the outer query, as it references the column from the outer query ().
Here is an example of a non-correlated sub-query:
The sub-query in this case is non-correlated, as it does not reference any columns from the outer query.
Performance-wise, correlated sub-queries are generally 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 8: Analyzing Clickthrough Rates for Product Ads
Power Integrations would like to assess the efficiency of their product ads. Specifically, they are interested in understanding which products have the highest click-through to conversion rates - that is, the percentage of users who click on an ad and eventually add the product to their cart.
Example Input:
| ad_id | product_id | user_id | click_time |
|---|---|---|---|
| 84513 | 1001 | 5432 | '2022-06-30 09:00:00' |
| 37449 | 1001 | 8272 | '2022-07-01 09:00:00' |
| 93288 | 2002 | 5432 | '2022-06-30 09:00:00' |
| 73829 | 3003 | 8272 | '2022-07-01 09:00:00' |
| 62840 | 4004 | 5432 | '2022-06-30 09:00:00' |
Example Input:
| user_id | product_id | add_time |
|---|---|---|
| 5432 | 1001 | '2022-06-30 10:00:00' |
| 8272 | 1001 | '2022-07-01 10:00:00' |
| 5432 | 2002 | NULL |
| 8272 | 3003 | '2022-07-05 10:00:00' |
| 5432 | 4004 | NULL |
Answer:
In the provided query, first, two temporary tables (ad_clicks_count and cart_additions_count) are created to count the number of ad clicks and cart additions for each product. These tables are joined in the main query to compute the conversion rate, which is defined as the ratio of successful conversions (click to add to cart) to the total ad clicks for each product. Products are ordered in descending order by the conversion rate to return the products with the highest conversion rate at the top.
To practice a similar problem about calculating rates, try this TikTok SQL question within DataLemur's online SQL coding environment:
SQL Question 9: Tracking Employee Sales for Power Integrations Customers
An integral part of analyzing a company's sales records is being able to filter and analyze data based on specific patterns or strings. We need you to identify the employees who serviced customers with the name containing the string 'elect' (from 'electronics') in the last quarter.
Consider the below examples of and tables.
Example Input:
| employee_id | firstname | lastname |
|---|---|---|
| 101 | John | Doe |
| 102 | Jane | Smith |
| 103 | Derek | Jones |
| 104 | Emily | Johnson |
Example Input:
| customer_id | name | employee_id | last_contact_date |
|---|---|---|---|
| 201 | ElectroPartz | 101 | 2022-07-15 |
| 202 | GlobalTech | 101 | 2022-08-20 |
| 203 | ResistoElectronics | 103 | 2022-11-05 |
| 204 | AdvanzeElectCorp | 104 | 2022-09-30 |
| 205 | ConglomoEnterprises | 102 | 2022-08-15 |
Your task is to write a SQL query that returns a list of employees who have worked with customers whose names contain the string 'elect', and the date of their last contact was within the last quarter.
Answer:
This query joins the and tables on the field. It then filters for customers whose names contain the string 'elect' and who have been contacted within the last quarter. The result is a list of employee IDs, first names, and last names matching these criteria. The results are ordered by employee ID.
SQL Question 10: Can you give an example of a one-to-one relationship between two entities, vs. a one-to-many relationship?
In database schema design, a one-to-one relationship between two entities means that each entity is linked to a single instance of the other. For example, the relationship between a car and a license plate is one-to-one because each car has only one license plate, and each license plate belongs to one car.
In contrast, a one-to-many relationship is when one entity can be associated with multiple instances of the other entity. An example of this is the relationship between a teacher and their classes - a teacher can teach multiple classes, but each class is only associated with one teacher.
SQL Question 11: Calculate Weighted Average Power Consumption
Power Integrations, a company manufacturing power conversion products, wants to conduct testing on their varying models. Each model is tested multiple times and assigned a power-efficiency score ranging between 0 and 100, where 100 is the most energy efficient. Each test has a varying degree of importance, weighted between 1 and 5. You are given the task to calculate the average power efficiency score for each model, taking into account the weightage of each test.
Here are the and tables.
Example Input:
| test_id | model_id | test_date | power_efficiency |
|---|---|---|---|
| 101 | 7001 | 08/12/2022 00:00:00 | 85 |
| 202 | 6002 | 08/11/2022 00:00:00 | 92 |
| 303 | 8003 | 08/14/2022 00:00:00 | 88 |
| 404 | 6002 | 08/13/2022 00:00:00 | 95 |
| 505 | 7001 | 08/11/2022 00:00:00 | 90 |
Example Input:
| test_id | weight |
|---|---|
| 101 | 4 |
| 202 | 2 |
| 303 | 1 |
| 404 | 5 |
| 505 | 3 |
The task is to calculate the weighted power-efficiency score for each model. Order the results by the weighted power-efficiency score in descending order.
Answer:
You can answer this question by executing the following SQL command:
In this query, we first join the and tables on the column. Then, for each model (), we compute its weighted power-efficiency score by summing the products of the and for each of its tests and dividing by the total of all its tests. This gives us the weighted average power-efficiency score.
The function is used to limit the precision of the computed averages to two decimal places. Finally, the models are ordered by their weighted power-efficiency scores in descending order.
To practice a very similar question try this interactive Alibaba Compressed Mean Question which is similar for requiring calculation of a weighted average or this Amazon Average Review Ratings Question which is similar for dealing with averages in grouped data.
Preparing For The Power Integrations SQL Interview
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Power Integrations SQL interview is to solve as many practice SQL interview questions as you can!
Besides solving the earlier Power Integrations SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like Netflix, Airbnb, and Amazon.
Each exercise has multiple hints, full answers and crucially, there is an online SQL code editor so you can instantly run your query and have it checked.
To prep for the Power Integrations SQL interview you can also be wise to practice SQL questions from other tech companies like:
However, if your SQL foundations are weak, forget about going right into solving questions – strengthen your SQL foundations with this SQL tutorial for Data Scientists & Analysts.
This tutorial covers topics including LEAD window function and handling timestamps – both of which pop up routinely during Power Integrations SQL interviews.
Power Integrations Data Science Interview Tips
What Do Power Integrations Data Science Interviews Cover?
Besides SQL interview questions, the other types of questions covered in the Power Integrations Data Science Interview are:
- Statistics and Probability Questions
- Coding Questions in Python or R
- Product Data Science Interview Questions
- Machine Learning Questions
- Behavioral Based Interview Questions
How To Prepare for Power Integrations Data Science Interviews?
To prepare for Power Integrations Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from Facebook, Google & startups
- a crash course on Python, SQL & ML
- over 900+ reviews on Amazon & 4.5-star rating