At Snowflake, SQL is used quite frequently for querying and analyzing large datasets in the cloud and managing data warehouse infrastructure for optimization and scalability. Because of this, Snowflake almost always asks SQL problems during interviews for Data Science, Data Engineering and Data Analytics jobs.
So, if you want to ace the SQL Assessment, we've collected 10 Snowflake SQL interview questions to practice, which are similar to commonly asked questions at Snowflake – able to answer them all?
Assume that Snowflake, a cloud-based data-warehousing company, wants to identify its power users. A power user is defined as any user who runs at least 500 query requests per month. The aim is to identify these power users so that special care can be taken to ensure their needs are being met and their experience with Snowflake is as smooth as possible.
Given the following data, write a SQL query to get the usernames of all power users and the number of their monthly queries for the last 12 months.
This SQL query groups all queries by user and month-year, counts the queries for each user for each month, and then filters out only those users who have run 500 or more queries in a month. The result will provide a list of VIP users along with the number of their monthly queries for the last 12 months.
To practice a related SQL problem on DataLemur's free interactive coding environment, solve this Facebook SQL Interview question:
You are an analyst at an e-commerce company. The company wants to track the average monthly rating of each product to assess performance trends. Write a SQL query that calculates the average rating (stars) for each product on a monthly basis, ordered by the month and then product id.
We will use the table that consists of the following columns:
The above SQL query extracts the month from the and calculates the average for each within that month using a window function. The clause is used to specify the partitioning and ordering of rows for the window function (in this case, based on and month of ). The resulting dataset provides the month, product id, and average product rating for each month.
To solve a similar problem about calculating rates, try this TikTok SQL question on DataLemur's online SQL coding environment:
A cross-join, also known as a cartesian join, is a type of JOIN operation in SQL that creates a new table by combining each row from the first table with every row from the second table. This results in a table with a row for every possible combination of rows from the two input tables.
An example of when this might be useful is if you wanted to first make a dataset containing all possible pairs of customers and products data, in order to later build a Machine Learning model to predict the probability of a customer purchasing a particular product.
However, it is important to note that cross-joins can create very large tables, especially if the input tables have a large number of rows. For example, if you had 10,000 customers and 5,000 different product SKUs, the resulting cross-join would have 50 million rows.
Snowflake Inc is a cloud-based data warehousing platform that enables data storage, processing, and analytic solutions compatible with various cloud platforms. As a Data Analyst at Snowflake, your task is to analyze product usage over some time. Each product use is logged in a table.
The table contains information on which user () used what product () at what date () and time ().
Your task is to create an SQL query that gives an output consisting of each product, the number of times it has been used, and the distinct number of users who used it.
This PostgreSQL query will group the table by and for each product, it will count the total number of usage logs (representing the number of times the product has been used) and the number of distinct associated with it (representing the distinct number of users who used the product).
To solve a similar problem about calculating rates, solve this SQL interview question from TikTok within DataLemur's interactive SQL code editor:
In SQL, a join is used to combine rows from different tables based on a shared key or set of keys, resulting in a single merged table of data.
There are four distinct types of JOINs: , , , and .
(INNER) JOIN: Retrieves records that have matching values in both tables involved in the join.
LEFT (OUTER) JOIN: Retrieves all the records/rows from the left and the matched records/rows from the right table.
RIGHT (OUTER) JOIN: Retrieves all the records/rows from the right and the matched records/rows from the left table.
FULL (OUTER) JOIN: Retrieves all the records where there is a match in either the left or right table.
Company XYZ has a customer record database and you are required to filter out the customers who made purchases last month. The purchases need to be above $500 and the customer needs to be from the United States. You also have to filter customers who have not logged into their accounts in the past year.
Attached below is a sample set of data that comes from the and tables.
The SQL query above first joins the table with the table on the column. Next, it filters customers who made purchases in the last month by , specifying that the purchase amount should be greater than $500 using . It also filters by country (United States), and customers who have not logged into their accounts in the past year by comparing the with the date one year ago from the current date.
To practice a related problem on DataLemur's free interactive coding environment, solve this SQL interview question asked by Facebook:
The operator merges the output of two or more statements into a single result set. The two SELECT statements within the UNION must have the same number of columns and the data types of the columns are all compatible.
For example, if you were a Data Analyst on the marketing analytics team at Snowflake, this statement would return a combined result set of both Snowflake's Google and Facebook ads that have more than 300 impressions:
You are given two tables, and . The table has information about all the digital ads displayed to users. It includes a unique identifier for each ad (ad_id), the date the ad was shown (ad_date), and the unique identifier of the user (user_id) to whom the ad was shown. The table contains logs of whenever a user clicked on an ad. It also includes a unique identifier for each click (click_id), the date of the click (click_date), and the user's identifier who clicked the ad.
The task is to write a SQL query to calculate the click-through rate (CTR) for all ads shown in the month of October 2022. The CTR is calculated as the number of clicks that an ad gets divided by the number of times the ad was shown, expressed as a percentage.
This SQL query first joins the table and table on the ad_id and user_id column by using a left join. Hence, for every ad, if it has been clicked, the click record and display record would be matched. Then, the query will filter out the ads displayed in October 2022 with the WHERE clause. After getting all the appropriate records, the query will calculate the click-through rate (CTR) and present them along with the ad id, number of clicks and the number of times the ad was shown. The count of click_id would result in the number of clicks an ad has, and the count of ad_id gives the number of times the ad is shown. The CTR is calculated based on these counts.
To practice a related SQL problem on DataLemur's free online SQL coding environment, solve this Facebook SQL Interview question:
Snowflake, an organization with numerous users, wants to filter down customer records to find all customers with an email domain of "@snowflake.com". As a data engineer, write a SQL query that filters the user database to return only users with this specific email domain.
This PostgreSQL query uses the LIKE operator to compare the 'email' attribute with the pattern '%@snowflake.com'. The percentage sign is a wildcard that matches any sequence of characters in the string. Thus, it returns all user records where the email contains '@snowflake.com'.
To solve another question about calculating rates, try this TikTok SQL question within DataLemur's interactive SQL code editor:
The / operator is used to remove to return all rows from the first SELECT statement that are not returned by the second SELECT statement.
Note that is available in PostgreSQL and SQL Server, while MINUS is available in MySQL and Oracle (but don't worry about knowing which DBMS supports which exact commands since Snowflake interviewers aren't trying to trip you up on memorizing SQL syntax).
For a tangible example of in PostgreSQL, suppose you were doing an HR Analytics project for Snowflake, and had access to Snowflake's contractors and employees data. Assume that some employees were previously contractors, and vice versa, and thus would show up in both tables.
You could use operator to find all contractors who never were a employee using this query:
The best way to prepare for a Snowflake SQL interview is to practice, practice, practice. In addition to solving the above Snowflake 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 problem on DataLemur has hints to guide you, fully explained answers along with a discussion board to see how others solved it and most importantly, there is an online SQL code editor so you can right online code up your SQL query answer and have it executed.
To prep for the Snowflake SQL interview it is also useful to practice interview questions from other tech companies like:
In case your SQL query skills are weak, don't worry about going right into solving questions – improve your SQL foundations with this DataLemur SQL Tutorial.
For the Snowflake Data Science Interview, beyond writing SQL queries, the other types of questions which are covered:
The best way to prepare for Snowflake Data Science interviews is by reading Ace the Data Science Interview. The book's got: