10 Cadence Design Systems SQL Interview Questions (Updated 2024)
At Cadence Design Systems, SQL does the heavy lifting for analyzing vast semiconductor design data and managing customer information within the integrated circuit design industry. Because of this, Cadence Design Systems LOVES to ask SQL query questions in interviews for Data Analytics, Data Science, and Data Engineering jobs.
So, if you're trying to prepare for the SQL Assessment, we've curated 10 Cadence Design Systems SQL interview questions to practice, which are similar to recently asked questions at Cadence Design Systems – how many can you solve?
10 Cadence Design Systems SQL Interview Questions
SQL Question 1: Identify Frequent High-Value Users
Cadence Design Systems offers a multitude of services and products, catering to different types of users. In order to better serve and retain our highest-value users, we need to identify them. For this exercise, write a SQL query that identifies the users who have made the most frequent purchases costing over $10,000 in the past month.
Let's consider two tables, and .
Example Input:
| user_id | name |
|---|---|
| 101 | John Smith |
| 102 | Sara Doe |
| 103 | Mark Johnson |
| 104 | Amy Williams |
| 105 | Kevin Brown |
Example Input:
| purchase_id | user_id | purchase_date | amount |
|---|---|---|---|
| 1000 | 101 | 2022-04-13 00:00:00 | 10050 |
| 1001 | 101 | 2022-04-19 00:00:00 | 20000 |
| 1002 | 103 | 2022-05-10 00:00:00 | 15000 |
| 1003 | 104 | 2022-05-02 00:00:00 | 12000 |
| 1004 | 105 | 2022-05-25 00:00:00 | 9000 |
| 1005 | 105 | 2022-05-29 00:00:00 | 11000 |
Answer:
The above SQL query joins the users and purchases tables and filters for records where the purchase amount exceeds $10,000 and the purchase was made within the past month. It then groups the results by the user's ID and name, while also counting the number of purchases made by each user. The results are then returned in descending order of the count of purchases. Using this query, we can successfully identify users who frequently make high-value purchases.
To practice a related SQL interview question on DataLemur's free interactive SQL code editor, attempt this Meta SQL interview question:
SQL Question 2: Analyze Design Product Performance
Given a table named which records the usage and performance metrics of various Cadence design products used by different clients, write an SQL query to calculate the rolling average of the product performance score by each client over a given period of time.
Consider the table is as shown below:
Example Input:
| performance_id | client_id | record_date | product_id | performance_score |
|---|---|---|---|---|
| 101 | 987 | 2022-06-20 | 40001 | 87 |
| 102 | 654 | 2022-06-18 | 40001 | 90 |
| 103 | 654 | 2022-06-20 | 69852 | 85 |
| 104 | 987 | 2022-07-15 | 40001 | 82 |
| 105 | 321 | 2022-07-17 | 40001 | 96 |
| 106 | 321 | 2022-07-20 | 69852 | 94 |
You are required to write a SQL query that would provide the rolling average of the performance scores for each client_id for the specified product_id with a window frame of 3 days.
Expected Output
| client_id | product_id | average_score |
|---|---|---|
| 987 | 40001 | 84.5 |
| 654 | 40001 | 90.0 |
| 654 | 69852 | 85.0 |
| 321 | 40001 | 96.0 |
| 321 | 69852 | 94.0 |
Answer:
This query leverages the window function . It partitions the data by and , then orders the records within each partition by . The part of the query is defining the window frame for our calculation: in this case, the current row and the two preceding it. This gives us a rolling average of the last 3 recorded usage performance scores per client per product. Finally, clause has been added at the end to organize our resulting data in a convenient order to check.
To solve another question about calculating rates, try this TikTok SQL Interview Question on DataLemur's interactive coding environment:
SQL Question 3: What's database denormalization?
Denormalization is a technique used to improve the read performance of a database, typically at the expense of some write performance.
By adding redundant copies of data or grouping data together in a way that does not follow normalization rules, denormalization improves the performance and scalability of a database by eliminating costly join operations, which is important for OLAP use cases that are read-heavy and have minimal updates/inserts.
SQL Question 4: Filter Customers Who Use Specific Cadence Design System Products
You are an analyst at Cadence Design Systems and you are asked to pull out a list of all customers who are actively using either OrCAD, Allegro or Sigrity software products. A customer is considered actively using the software if they have logged in during the past 7 days.
Example Input:
| customer_id | first_name | last_name | |
|---|---|---|---|
| 001 | John | Doe | johndoe@gmail.com |
| 002 | Mary | Smith | marysmith@gmail.com |
| 003 | Robert | Johnson | rJohnson@gmail.com |
| 004 | Mike | Brown | mike_brown@gmail.com |
| 005 | Emma | Davis | emmaD@gmail.com |
Example Input:
| usage_id | software_name | customer_id | last_login_date |
|---|---|---|---|
| 500 | OrCAD | 001 | 06/13/2022 00:00:00 |
| 501 | Allegro | 002 | 06/05/2022 00:00:00 |
| 502 | Sigrity | 003 | 06/11/2022 00:00:00 |
| 503 | Incisive | 004 | 06/10/2022 00:00:00 |
| 504 | Virtuoso | 005 | 06/17/2022 00:00:00 |
Answer:
This SQL query first joins the and tables on the field. Then, it filters for those customers using OrCAD, Allegro, or Sigrity. Lastly, it checks if the is within the past 7 days, indicating active users. The result set will contain the customer ids, first names, last names and email addresses of all such actively using customers.
To solve a similar SQL interview question on DataLemur's free interactive SQL code editor, try this Meta SQL interview question:
SQL Question 5: Can you explain the concept of database normalization?
Database normalization is the process of breaking down a table into smaller and more specific tables and defining relationships between them via foreign keys. This minimizes redundancy, and creates a database that's more flexible, scalable, and easier to maintain. It also helps to ensure the integrity of the data by minimizing the risk of data inconsistencies and anomalies.
SQL Question 6: Average usage hours of software product per client
Given a database containing usage logs of Cadence's software products by their clients, find the average usage hours of each software product by each client over the specified period.
Sample Input:
| log_id | client_id | product_id | usage_date | hours_used |
|---|---|---|---|---|
| 1 | 100 | 561 | 01/08/2022 | 5 |
| 2 | 200 | 561 | 01/09/2022 | 8 |
| 3 | 100 | 561 | 02/08/2022 | 7 |
| 4 | 200 | 741 | 01/10/2022 | 6 |
| 5 | 100 | 741 | 01/11/2022 | 4 |
Answer:
The above query groups the data by both client_id and product_id and applies the AVG function on the hours_used column to find the average usage of each product by each client. It should be noted that usage logs for each product by each client over the entire period are being considered. If the period needs to be filtered, you can add an additional WHERE clause to filter dates based on the requirement.
Example Output:
| client_id | product_id | avg_usage |
|---|---|---|
| 100 | 561 | 6 |
| 200 | 561 | 8 |
| 200 | 741 | 6 |
| 100 | 741 | 4 |
To practice another question about calculating rates, try this TikTok SQL question on DataLemur's interactive coding environment:
SQL Question 7: Can you describe the difference between a correlated and a non-correlated sub-query?
A non-correlated sub-query is not linked to the outer query and can be run on its own. It does not reference any columns from the outer query and is used to retrieve additional data for the outer query. On the other hand, a correlated sub-query is one that relies on the outer query and cannot be run independently. It uses the outer query to filter or transform data by referencing a column from it, while the outer query uses the results of the inner 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.
Here is an example of a correlated sub-query:
This query selects the and total sales of all Cadence Design Systems 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 ().
Non-correlated sub-queries are typically faster, as they only have to be executed once, wheareas correlated sub-queries are slower since they have to be re-evaluated for each row of the outer query.
SQL Question 8: Analyzing Ad Click-Through Rates
As a data analyst at Cadence Design Systems, you've been asked to analyze the click-through rates (CTR) of different digital ads that the company is running. Cadence designs and sells electronic design automation (EDA) software and hardware, and they run a series of ads to drive leads to trial product downloads. Your manager would like to know how many users are clicking through from viewing the ad to actually starting a software trial.
Please calculate the click-through rate, determined as the number of trials started by a user divided by the number of times that user viewed the ad, all multiplied by 100. Provide results table for all users.
Now, let's say we have two tables: and .
Example Input:
| view_id | user_id | ad_id | view_date |
|---|---|---|---|
| 101 | 1001 | 201 | 2022-10-10 |
| 102 | 1002 | 202 | 2022-10-11 |
| 103 | 1001 | 201 | 2022-10-12 |
| 104 | 1003 | 203 | 2022-10-13 |
| 105 | 1001 | 201 | 2022-10-14 |
Example Input:
| trial_id | user_id | product_id | start_date |
|---|---|---|---|
| 901 | 1001 | 301 | 2022-10-12 |
| 902 | 1003 | 302 | 2022-10-14 |
| 903 | 1001 | 301 | 2022-10-15 |
| 904 | 1002 | 303 | 2022-10-16 |
| 905 | 1001 | 301 | 2022-10-18 |
Example Output:
| user_id | ctr |
|---|---|
| 1001 | 66.67 |
| 1002 | 50.00 |
| 1003 | 100.00 |
Answer:
This query first joins the and tables on the . Then it counts the number of distinct s and s for each user, calculates the ratio of trials to views, and multiplies it by 100 to convert it to a percentage. Finally, it returns this click-through rate for each user.
To practice a similar problem about calculating rates, try this SQL interview question from TikTok on DataLemur's online SQL coding environment:
SQL Question 9: Average Review Ratings for Cadence Design Products
As a data analyst at Cadence Design Systems, your responsibility is to regularly prepare reports on user feedback to inform product development and improvement strategies. You have access to a database table, , containing review data for all Cadence Design System's products.
Each record in the table represents a unique product review submitted by a user, with the following columns:
- : An identifier for the review.
- : An identifier for the user who submitted the review.
- : The date and time the review was submitted.
- : An identifier for the reviewed product.
- : The star rating given in the review, ranging from 1 to 5.
Write a SQL query that for each month and each product, calculates the average rating it received across all reviews submitted that month. If no reviews were submitted for a product in a particular month, that product-month should not appear in your report.
Example Input
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 7123 | 145 | 2022-02-03 08:45:29 | 9001 | 5 |
| 8931 | 327 | 2022-02-16 19:30:22 | 9001 | 4 |
| 5634 | 216 | 2022-02-23 10:30:00 | 9002 | 4 |
| 5478 | 458 | 2022-03-10 15:00:00 | 9002 | 3 |
| 6413 | 689 | 2022-03-18 17:45:00 | 9001 | 2 |
Example Output
| month | product_id | avg_rating |
|---|---|---|
| 2 | 9001 | 4.50 |
| 2 | 9002 | 4.00 |
| 3 | 9001 | 2.00 |
| 3 | 9002 | 3.00 |
Answer
This SQL query first extracts the month from the of each review record. Then it groups the data by the extracted month and , averaging the (ratings) within each product-month group. The resulting data is a monthly breakdown of average review ratings for each Cadence Design System product ordered by month and then by the average rating in descending order.
To solve a related SQL interview question on DataLemur's free online SQL coding environment, try this Meta SQL interview question:
SQL Question 10: What's the difference between an inner and 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 Cadence Design Systems 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.
How To Prepare for the Cadence Design Systems SQL Interview
The key to acing a Cadence Design Systems SQL interview is to practice, practice, and then practice some more!
In addition to solving the above Cadence Design Systems SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like Amazon, Microsoft, Meta, and smaller tech companies.
Each SQL question has multiple hints, step-by-step solutions and crucially, there is an interactive SQL code editor so you can right in the browser run your SQL query answer and have it checked.
To prep for the Cadence Design Systems SQL interview you can also be useful to solve interview questions from other tech companies like:
However, 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 topics including functions like SUM()/COUNT()/AVG() and turning a subquery into a CTE – both of which show up often in SQL job interviews at Cadence Design Systems.
Cadence Design Systems Data Science Interview Tips
What Do Cadence Design Systems Data Science Interviews Cover?
For the Cadence Design Systems Data Science Interview, beyond writing SQL queries, the other types of questions to practice:
- Probability & Statistics Questions
- Python or R Programming Questions
- Open-Ended Data Case Studies
- ML Modelling Questions
- Behavioral & Resume-Based Questions
How To Prepare for Cadence Design Systems Data Science Interviews?
To prepare for Cadence Design Systems Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from Facebook, Google & startups
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