At Certara, SQL is used day-to-day for analyzing complex bioinformatics data sets, and managing clinical trial data for drug development projects. So, it shouldn't surprise you that Certara LOVES to ask SQL coding questions in interviews for Data Science, Analytics, and & Data Engineering jobs.
So, to help you practice for the Certara SQL interview, here’s 9 Certara SQL interview questions – able to answer them all?
Certara is a company that offers medicines and software that simulate and predict drug behavior. For Certara, a power user can be defined someone who uses the software simulators frequently and successfully. This question assumes we have two tables, that holds the info about the users, and that records the specifics of each use.
We'd like to identify power users who have more than 100 successful uses in the last month. A successful use is where .
Write an SQL query to identify such users.
user_id | user_name |
---|---|
1 | Jordan |
2 | Alex |
3 | Taylor |
4 | Jay |
5 | Kelly |
record_id | user_id | usage_date | status |
---|---|---|---|
10001 | 1 | 2022-06-08 | Success |
10002 | 1 | 2022-06-09 | Failed |
10003 | 2 | 2022-06-18 | Success |
10004 | 1 | 2022-06-20 | Success |
10005 | 3 | 2022-06-21 | Success |
10006 | 4 | 2022-06-22 | Success |
10007 | 5 | 2022-06-23 | Failed |
10008 | 3 | 2022-06-24 | Success |
10009 | 2 | 2022-06-25 | Success |
10010 | 1 | 2022-06-26 | Success |
This query joins the and tables on , filters for rows where is 'Success', and is within the last month. It then groups the rows by and counts the number of rows for each user, only retaining those with more than 100. The output thus gives us the names of the power users, and their counts of successful uses in the last month.
To work on a similar customer analytics SQL question where you can solve it interactively and have your SQL query instantly graded, try this Walmart Labs SQL Interview Question:
Certara specializes in biosimulation software for pharmaceutical research. As an employee, you're given a dataset that contains information about drug efficacy testing sessions. Each session has an ID, a drug being used, the start date of the session, the end date, and an efficacy score (a percentage between 0-100).
Write a SQL query that calculates a running average efficacy score for each drug. The running average should be between the start of the data and the current row's end date. Rows should be ordered by the in ascending order.
Here is a sample of the table:
session_id | drug | start_date | end_date | efficacy_score |
---|---|---|---|---|
1001 | Drug A | 01/01/2021 | 01/31/2021 | 75 |
1002 | Drug B | 02/01/2021 | 02/28/2021 | 80 |
1003 | Drug A | 03/01/2021 | 03/31/2021 | 85 |
1004 | Drug B | 04/01/2021 | 04/30/2021 | 70 |
1005 | Drug A | 05/01/2021 | 05/31/2021 | 90 |
Here is a PostgreSQL query to get the running average efficacy_score for each drug:
This query uses a window function to calculate the running average efficacy score for each drug. The clause is used to divide the data into partitions based on . Within each partition, the clause orders the rows by , and the constructs the window frame to include all the rows from the start of the partition to the current row, inclusive. The function then calculates the average efficacy score over this window frame. Finally, it returns the sessions in an ascending order of the end date.
Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur
The function can take in multiple paramaters, and returns the first input paramater that is not null. If all arguments are null, the COALESCE function will return null too.
Suppose you have a table of Certara salespeople and the number of deals they closed. If a salesperson didn't close any deals, the data from the 3rd-party CRM system exports a NULL value.
sales_person | closed_deals |
---|---|
Jason Wright | NULL |
Drew Jackson | 3 |
Chris Ho | NULL |
Adam Cohen | 2 |
Samantha Perez | 4 |
To change these NULLs to zeros, you can use the function in the following way:
This would result in the following data:
sales_person | closed_deals |
---|---|
Jason Wright | 0 |
Drew Jackson | 3 |
Chris Ho | 0 |
Adam Cohen | 2 |
Samantha Perez | 4 |
Certara, a global health solutions company, is running various digital ad campaigns to promote their software and services. As a Data Analyst, you're tasked with determining the click-through rates for these ads.
The table records each time a user clicks on one of these ads and the table records each time an ad is viewed. Each entry in both tables is time stamped and associated with a unique ad ID.
You're asked to calculate the Click-through-rate (CTR) = (total ad clicks/total ad views) * 100% for the last month. Assume today's date is September 30, 2021.
view_id | ad_id | view_date |
---|---|---|
1001 | 001 | 08/21/2021 00:00:00 |
1002 | 002 | 08/30/2021 00:00:00 |
1003 | 001 | 09/01/2021 00:00:00 |
1004 | 003 | 09/10/2021 00:00:00 |
1005 | 002 | 09/20/2021 00:00:00 |
click_id | ad_id | click_date |
---|---|---|
2001 | 002 | 08/30/2021 00:00:00 |
2002 | 001 | 09/05/2021 00:00:00 |
2003 | 003 | 09/12/2021 00:00:00 |
2004 | 001 | 09/21/2021 00:00:00 |
2005 | 003 | 09/25/2021 00:00:00 |
This query calculates the Click-Through-Rate (CTR) for each ad by dividing the total number of clicks by the total number of views for the last month. The typecast is used to get a decimal result from integer division. The use of a ensures that we include all ads that were viewed, even if they did not get clicked (in such cases, CTR will be 0). We filter views for the last month by including dates between the first day of the last month and the first day of the current month.
To practice a similar SQL interview question on DataLemur's free interactive SQL code editor, solve this SQL interview question asked by Facebook:
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.
For example, say you had a marketing analytics database that stores ad campaign data from Certara's Google Analytics account.
Here's what some constraints could look like:
The CHECK constraint is used in the above example to make sure that the "budget" and "cost_per_click" fields contain only positive values. This helps to maintain the validity and integrity of the data in the context of ad campaigns.
The CHECK constraint can also be used to enforce other specific conditions on data. For example, you can use it to ensure that the "start_date" for each ad campaign is earlier than the "end_date."
Certara is a global biotech company and you are a data analyst. They maintain records of their customers' information and their interactions with the company. One of your tasks is to filter customer records to find customers who work in the biotech industry in the San Francisco Bay Area.
The specific task is to write a SQL query to find all customers in the table whose includes the word "Biotech" and whose includes "San Francisco".
The table is structured as follows:
customer_id | first_name | last_name | adress | job_title | |
---|---|---|---|---|---|
123 | John | Doe | john.doe@example.com | 1234 Market St, San Francisco, CA | Biotech Engineer |
456 | Jane | Smith | jane.smith@example.com | 5678 Bay St, Oakland, CA | Software Engineer |
789 | Bob | Johnson | bob.johnson@example.com | 91011 Pine St, San Francisco, CA | Biotech Analyst |
321 | Alice | Williams | alice.williams@example.com | 1213 Elm St, San Francisco, CA | Civil Engineer |
654 | Charlie | Brown | charlie.brown@example.com | 1415 Oak St, Oakland, CA | Biotech Researcher |
You can assume that the job title and address are stored as text in the database.
A query to solve this task may look like this:
This will return all records for customers whose job title includes "Biotech" and whose address includes "San Francisco".
The condition matches any job titles that include the word "Biotech". The '%' is a wildcard character in SQL that matches any sequence of characters, so can appear anywhere in the .
Similarly, the condition matches any addresses that include the string "San Francisco".
So given the example data above, this will return the records for customers with ID 123 and 789, as they work in the biotech industry and live in San Francisco.
The clause is used to filter the groups created by the clause. It's similar to the clause, but it is used to specify conditions on the groups created by the clause, rather than on the individual rows of the table.
For example, say you were analyzing salaries for analytics employees at Certara:
This query retrieves the total salary for each Analytics department at Certara and groups the rows by the specific department (i.e. "Marketing Analytics", "Business Analytics", "Sales Analytics" teams).
The clause then filters the groups to include only Certara departments where the total salary is greater than $1 million
Given two tables, and :
The table contains information about each customer, and the table contains information about each order that was made. Assume that the table contains a to link it to the table.
Write a SQL query to find the total amount spent by each customer, along with the customer's name.
The tables are structured as following:
customer_id | customer_name |
---|---|
001 | John Smith |
002 | Jane Doe |
003 | Alice Johnson |
order_id | customer_id | order_value |
---|---|---|
1001 | 001 | 200 |
1002 | 002 | 300 |
1003 | 003 | 150 |
1004 | 001 | 100 |
1005 | 002 | 120 |
customer_name | total_spent |
---|---|
John Smith | 300 |
Jane Doe | 420 |
Alice Johnson | 150 |
In this query, we join the and tables on . We then group by to find the total order value for each customer. The use of calculates the total amount spent by each customer across all orders.
Because join questions come up so often during SQL interviews, take a stab at this interactive Snapchat Join SQL question:
Certara is a pharmaceutical company that also deals with customers' reviews and ratings for their products. They would like to analyze the rating performance of their products on a scale of 1 to 5. They are interested in knowing how much a product deviates from a perfect 5-star rating. They would want the rounded average, the absolute deviation from 5 and the square root of this deviation, for each product.
Assume the company has a table that tracks each review submitted by the customers.
review_id | user_id | submit_date | product_id | stars |
---|---|---|---|---|
6171 | 123 | 06/08/2022 00:00:00 | 1 | 4 |
7802 | 265 | 06/10/2022 00:00:00 | 2 | 4 |
5293 | 362 | 06/18/2022 00:00:00 | 1 | 3 |
6352 | 192 | 07/26/2022 00:00:00 | 2 | 3 |
4517 | 981 | 07/05/2022 00:00:00 | 2 | 2 |
This query calculates the average rating for each product first and rounds it to 2 decimals. Then it calculates the absolute deviation from the maximum star rating of 5. Next, it calculates the square root of this deviation. These calculations give Certara a detailed mathematical analysis of how much a product is deviating from a perfect rating and that can help them improve their products. The ABS function is used for finding the absolute value and the SQRT function is used to calculate the square root. Rounding is done using the ROUND function. All computations are grouped by the product_id.
The 2 most similar questions from the question bank are:
"Average Review Ratings" from Amazon. Reason: The problem involves calculating average ratings, similar to the original Certara question.
"Median Google Search Frequency" from Google. Reason: The problem also involves calculations on a rating-like scale and rounding.
So, the recommendation in markdown would be:
To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for calculating averages or this Google Median Google Search Frequency Question which is similar for calculations on a rating-like scale.
The key to acing a Certara SQL interview is to practice, practice, and then practice some more! Besides solving the earlier Certara SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Microsoft, Google, Amazon, and tech startups.
Each DataLemur SQL question has hints to guide you, detailed solutions and most importantly, there is an online SQL code editor so you can right online code up your query and have it graded.
To prep for the Certara SQL interview it is also wise to practice SQL problems from other tech companies like:
In case your SQL coding skills are weak, forget about diving straight into solving questions – go learn SQL with this DataLemur SQL Tutorial.
This tutorial covers SQL topics like using wildcards with LIKE and math functions like ROUND()/CEIL() – both of these come up frequently in SQL job interviews at Certara.
Besides SQL interview questions, the other question categories to practice for the Certara Data Science Interview are:
The best way to prepare for Certara Data Science interviews is by reading Ace the Data Science Interview. The book's got: