10 Silicon Labs SQL Interview Questions (Updated 2024)
At Silicon Labs, SQL is used across the company for analyzing large data sets for hardware optimization and running complex queries for predictive analytics in semiconductor manufacturing. Unsurprisingly this is why Silicon Labs often tests SQL query questions in interviews for Data Science, Analytics, and & Data Engineering jobs.
As such, to help you prepare for the Silicon Labs SQL interview, we've curated 10 Silicon Labs SQL interview questions – can you answer each one?
10 Silicon Labs SQL Interview Questions
SQL Question 1: Analyze Monthly Average Review Score
Suppose that you were asked to analyze the average review rating for various products sold by Silicon Labs each month. Given a table containing review ratings submitted by users, your task is to write a PostgreSQL query to calculate the monthly average rating for each product. Assume the dataset only spans for the year 2022.
The table is structured as follows:
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 6171 | 123 | 06/08/2022 00:00:00 | 50001 | 4 |
| 7802 | 265 | 06/10/2022 00:00:00 | 69852 | 4 |
| 5293 | 362 | 06/18/2022 00:00:00 | 50001 | 3 |
| 6352 | 192 | 07/26/2022 00:00:00 | 69852 | 3 |
| 4517 | 981 | 07/05/2022 00:00:00 | 69852 | 2 |
Your output should reflect the average rating per product for each month it was reviewed. For the purpose of this example, your output should look similar to the following:
Example Output:
| mth | product_id | avg_stars |
|---|---|---|
| 6 | 50001 | 3.5 |
| 6 | 69852 | 4.0 |
| 7 | 69852 | 2.5 |
Answer:
This query uses the aggregate function in combination with an clause to partition the data by and the month of the , effectively giving us the average star rating for each product on a per-month basis. We EXTRACT the month from submit_date to group by months. Then, we use GROUP BY clause to group the result by product_id, and month.
To solve another window function question on DataLemur's free online SQL code editor, solve this Google SQL Interview Question:
SQL Question 2: Design and Query for Silicon Labs production
Given the nature of Silicon Labs as a semiconductor manufacturer, you are tasked to design an efficient database that records product data. The database should keep track of different products made, date of manufacture, and the manufacturing cost.
Sample Tables:
Example Input:
| product_id | product_name | manufacture_date | manufacturing_cost |
|---|---|---|---|
| 100 | Chip A | 08/10/2020 | 120 |
| 200 | Chip B | 10/12/2020 | 150 |
| 300 | Chip C | 12/15/2020 | 100 |
| 400 | Chip A | 01/20/2021 | 130 |
| 500 | Chip D | 03/25/2021 | 140 |
Your task is to create an SQL Query that can:
- Calculate the total manufacturing cost per product for a given year (2020)
- And sort the output by total manufacturing cost in descending order.
Answer:
In this query, the main operation is aggregation via the GROUP BY clause on product_name, allowing us to perform a calculation on each group (in this case, the sum of the manufacturing costs). The WHERE clause is used to limit the data to only include products manufactured in 2020. Finally, the ORDER BY clause sorts the results by total cost, in descending order.
SQL Question 3: How does a cross join differ from a natural join?
A cross join is a JOIN operation in SQL that creates a new table by pairing each row from the first table with every row from the second table. It is also referred to as a cartesian join. In contrast, a natural join combines rows from two or more tables based on their common columns, forming a new table. Natural joins are called "natural" because they rely on the natural relationship between the common columns in the joined tables.
Here's an example of a cross join:
Here's a natural join example using two tables, Silicon Labs employees and Silicon Labs managers:
This natural join returns all rows from Silicon Labs employees where there is no matching row in managers based on the column.
One significant difference between cross joins and natural joins is that the former do not require common columns between the tables being joined, while the latter do. Another distinction is that cross joins can generate very large tables if the input tables have a large number of rows, while natural joins only produce a table with the number of rows equal to the number of matching rows in the input tables.
SQL Question 4: Filter Employees Based on Designation and Status
Given a table named "employees", write a SQL query to return all Engineers that are currently active in the company. The "employees" table has the following columns: employee_id (integer), first_name (string), last_name (string), designation (string), status (string).
Example Input:
| employee_id | first_name | last_name | designation | status |
|---|---|---|---|---|
| 1 | John | Doe | Engineer | Active |
| 2 | Jane | Doe | Manager | Inactive |
| 3 | Dave | Smith | Engineer | Active |
| 4 | Sara | Johnson | Director | Active |
| 5 | Robert | Miller | Engineer | Inactive |
Example Output:
| employee_id | first_name | last_name | designation | status |
|---|---|---|---|---|
| 1 | John | Doe | Engineer | Active |
| 3 | Dave | Smith | Engineer | Active |
Answer:
Here is the SQL query that performs the given task.
This query filters the table to only include rows where the column is 'Engineer' and the column is 'Active'. The use of the logical operator ensures that both conditions must be met. The statement returns all columns for the filtered rows.
SQL Question 5: How does an inner join differ from a full outer join?
An inner join only includes rows from both tables that satisfy the join condition, whereas a full outer join includes all rows from both tables, regardless of whether they match the join condition or not.
For a concrete example, imagine you had two database tables, an advertising campaigns table which had information on Google Ads keywords and how much was bid for each keyword, and a Silicon Labs sales table, which has data on how many products were sold and which Google Ads keyword drove that sale.
: retrieves rows from both tables where there is a match in the shared key or keys. For example, an INNER JOIN between the table and the table could be performed using the keyword column as the shared key. This would retrieve only the rows where the keyword in the table matches the keyword in the table.
: retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, values will be returned for the columns of the non-matching table.
SQL Question 6: Click-through-rate Analysis
Silicon Labs has been running a digital ad campaign for their new IoT product which includes a display ad directing users to their product webpage. On this webpage, users have the option to add the product to their digital cart.
The marketing team would love to have an understanding of the clickthrough conversion rate. This rate essentially represents the percentage of users who added the product to the cart after viewing the ad.
In the tables below, the 'page_impressions' table records each instance that the ad was displayed, and the 'add_to_cart' table records each instance that a product was added to a customer's cart.
Example Input:
| impression_id | user_id | view_date | product_id |
|---|---|---|---|
| 2134 | 567 | 09/12/2021 00:00:00 | 2356 |
| 6926 | 908 | 09/13/2021 00:00:00 | 2356 |
| 8425 | 362 | 09/15/2021 00:00:00 | 2356 |
| 3847 | 825 | 09/11/2021 00:00:00 | 2356 |
| 2546 | 673 | 09/17/2021 00:00:00 | 2356 |
Example Input:
| transaction_id | user_id | add_date | product_id |
|---|---|---|---|
| 1055 | 567 | 09/12/2021 00:00:00 | 2356 |
| 7689 | 908 | 09/13/2021 00:00:00 | 2356 |
| 6235 | 982 | 09/16/2021 00:00:00 | 2356 |
| 5447 | 673 | 09/14/2021 00:00:00 | 2356 |
| 4547 | 921 | 09/12/2021 00:00:00 | 2356 |
Answer:
This query calculates the click-through conversion rate for product '2356'. It does this by joining the 'page_impressions' table and the 'add_to_cart' table based on user_id, product_id and date. We use a 'LEFT JOIN' to ensure that every impression is included in the calculation. The click-through rate is then calculated by dividing the count of unique ids in 'add_to_cart' by the count of unique ids in 'page_impressions' for each product. The 'WHERE' clause filters for product '2356'.
To practice a related SQL problem on DataLemur's free online SQL coding environment, solve this Meta SQL interview question:
SQL Question 7: How can you select records without duplicates from a table?
The clause in SQL allows you to select records that are unique, eliminating duplicates.
For example, if you had a table of Silicon Labs employees, and wanted to see what are all the unique job titles that currently worked at the company, you could write the following query:
SQL Question 8: Customer Order Analysis
Given two tables and , where contains customer data and contains their order data, write a SQL query to find the total amount spent by each customer on "Computer Hardware".
Furthermore, give the customer's name, their total expenditure, and sort the output by the expenditure in descending order. Assume that table has a column with as a possible value.
Example Input:
| customer_id | first_name | last_name |
|---|---|---|
| 1 | John | Doe |
| 2 | Sarah | Smith |
| 3 | Adam | Johnson |
| 4 | Cindy | Morgan |
Example Input:
| order_id | customer_id | product_category | price |
|---|---|---|---|
| 1 | 1 | Phone Accessories | 59.99 |
| 2 | 1 | Computer Hardware | 599.99 |
| 3 | 2 | Computer Hardware | 999.99 |
| 4 | 1 | Computer Hardware | 129.99 |
| 5 | 3 | Headphones | 49.99 |
| 6 | 2 | Computer Hardware | 199.99 |
| 7 | 4 | Computer Hardware | 699.99 |
Answer:
This query first joins the customers table with the orders table on the "customer_id" column. Then it filters out the orders that are not from the "Computer Hardware" category. The result is grouped by customer and the total expenditure (sum of prices) per customer is calculated. Finally, the results are sorted by the total expenditure in descending order. The query will return a list of customers along with their total expenditures on "Computer Hardware".
Because join questions come up so often during SQL interviews, practice an interactive SQL join question from Spotify:
SQL Question 9: Calculate Statistics for Product Sales
Suppose Silicon Labs wants to get an understanding of their quarterly sales for each product. They are interested in calculating the total sales, the mean sale, the standard deviation of the sales, and the maximum sale for each product for each quarter of the year.
Assume there is a sales table with the following structure:
Example Input
| sale_id | product_id | sale_date | sale_amount |
|---|---|---|---|
| 101 | P1001 | 02/01/2022 | 200 |
| 102 | P1001 | 03/10/2022 | 300 |
| 103 | P1002 | 01/15/2022 | 150 |
| 104 | P1002 | 01/20/2022 | 50 |
| 105 | P1002 | 03/25/2022 | 100 |
| 106 | P1001 | 04/02/2022 | 250 |
| 107 | P1002 | 06/05/2022 | 75 |
| 108 | P1001 | 07/01/2022 | 300 |
The company wants the result for each product id for each quarter of the year. They define Q1 as January to March, Q2 as April to June, and Q3 as July to September.
Write a SQL query to get the desired result.
Answer:
The query uses the statement to categorize records into quarters based on the . It then groups by both and to calculate the required aggregations: total sales, mean sale, standard deviation of the sales, and maximum sale. Since SQL's standard deviation function will return null if there is only one sale for a product in a quarter, it might be useful to handle such cases separately or keep this in mind while interpreting the results.
To practice a very similar question try this interactive Wayfair Y-on-Y Growth Rate Question which is similar for calculating year-on-year growth rate in sales>or this Amazon Highest-Grossing Items Question which is similar for identifying the highest-grossing products.
SQL Question 10: Can you describe the meaning of database normalization in layman's terms?
Normalizing a database involves dividing a large table into smaller and more specialized ones, and establishing relationships between them using foreign keys. This reduces repetition, resulting in a database that is more adaptable, scalable, and easy to maintain. Additionally, it helps to preserve the accuracy of the data by reducing the likelihood of inconsistencies and problems.
How To Prepare for the Silicon Labs SQL Interview
The best way to prepare for a SQL interview, besides making sure you have strong SQL fundamentals, is to practice a ton of real SQL questions that were asked in recent job interviews.
Beyond just solving the above Silicon Labs SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Microsoft, Google, and Facebook.
Each interview question has multiple hints, full answers and most importantly, there's an online SQL code editor so you can right in the browser run your SQL query and have it checked.
To prep for the Silicon Labs SQL interview it is also useful to solve SQL problems from other tech companies like:
But if your SQL skills are weak, don't worry about diving straight into solving questions – strengthen your SQL foundations with this DataLemur SQL Tutorial.
This tutorial covers things like UNION and creating pairs via SELF-JOINs – both of which come up frequently in Silicon Labs SQL interviews.
Silicon Labs Data Science Interview Tips
What Do Silicon Labs Data Science Interviews Cover?
In addition to SQL interview questions, the other topics to practice for the Silicon Labs Data Science Interview are:
- Probability & Stats Questions
- Python or R Programming Questions
- Open-Ended Data Case Studies
- ML Modelling Questions
- Behavioral & Resume-Based Questions
How To Prepare for Silicon Labs Data Science Interviews?
The best way to prepare for Silicon Labs Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from Facebook, Google & startups
- A Refresher on Python, SQL & ML
- Amazing Reviews (900+ reviews, 4.5-star rating)