9 Nikon SQL Interview Questions (Updated 2024)
At Nikon, SQL is used all the damn time for analyzing complex imaging data sets, and managing supply-chain databases for better inventory control. So, it shouldn't surprise you that Nikon LOVES to ask SQL query questions in interviews for Data Science, Data Engineering and Data Analytics jobs.
As such, to help you ace the Nikon SQL interview, we'll cover 9 Nikon SQL interview questions – can you solve them?
9 Nikon SQL Interview Questions
SQL Question 1: Analyze Average Monthly Sales of Nikon Camera Models
Nikon, the global manufacturer of digital imaging products, needs to analyze its camera sales trend to understand customer preferences better. As a data analyst, your task is to compute the average monthly sales of each camera model for the year 2022. Nikon is specifically interested in getting these metrics for their high-demand dSLR camera models: D780, Z50, and D3500.
To answer this question, consider the following tables with the appropriate data:
Example Input:
| sale_id | sale_date | camera_model | unit_price | quantity |
|---|---|---|---|---|
| 5432 | 01/08/2022 | D780 | 2399.95 | 12 |
| 2613 | 01/08/2022 | Z50 | 859.95 | 15 |
| 4725 | 02/04/2022 | D780 | 2399.95 | 7 |
| 8715 | 02/18/2022 | D3500 | 496.95 | 25 |
| 7523 | 03/21/2022 | Z50 | 859.95 | 14 |
Example Output:
| mth | model | avg_sale |
|---|---|---|
| 1 | D780 | 28799.40 |
| 1 | Z50 | 12899.25 |
| 2 | D780 | 16799.65 |
| 2 | D3500 | 12423.75 |
| 3 | Z50 | 12039.30 |
Answer:
This query extracts the month and the year from the and filters for the year 2022, and only for the requested Nikon Camera models ('D780', 'Z50', 'D3500'). The average sales per month for each Camera model are calculated by multiplying the by the , this is then averaged using the AVG SQL function. The results are finally grouped by and and ordered descendingly by .
For more window function practice, try this Uber SQL Interview Question within DataLemur's online SQL coding environment:
SQL Question 2: Database Design for Nikon's Product Sales
At Nikon, there are many types of camera products and they are sold in various countries. Each product has different attributes such as product category (DSLR, Mirrorless, Compact), launching year and price. Nikon wants to keep track of the sales data by product and by country. They're interested in understanding which product category is performing well in each country and what the average selling price is.
Design a database for this system, including any necessary tables and relationships. Show how you would model this data in SQL, and write a query to find the current average selling price for each product category by country.
Sample data:
table:
| product_id | product_category | launching_year | initial_price |
|---|---|---|---|
| 1001 | DSLR | 2015 | 1500 |
| 1002 | Mirrorless | 2018 | 2000 |
| 1003 | Compact | 2020 | 500 |
table:
| sales_id | product_id | country | selling_price | selling_date |
|---|---|---|---|---|
| 1 | 1001 | USA | 1100 | 2022-07-03 |
| 2 | 1001 | UK | 1200 | 2022-07-10 |
| 3 | 1002 | USA | 1800 | 2022-07-15 |
| 4 | 1003 | Japan | 450 | 2022-07-20 |
Answer:
The SQL query to get the current average selling price for each product category by country could look something like this:
This query retrieves the product category information from the table and the country and selling price information from the table. It uses an average aggregate function () to get the average selling price for each product category by country.
The result can help Nikon identify which products perform well in different countries, and provide insight into pricing strategies.
SQL Question 3: Are the results of a UNION ALL and a FULL OUTER JOIN usually the same?
No, in 99% of normal cases a and do NOT produce the same result.
You can think of more as set addition when it combines the results of two tables.
You can think of a more as set multiplication, producing all combos of table 1 with combos of table 2.
SQL Question 4: Average Sale Prices of Nikon Cameras by Year
Given a database of camera sales transactions, calculate the average sale price per model for each year that a Nikon model was sold. The database includes the tables and . The table includes transaction details such as the camera model, selling price, and date of sale. The table includes camera model details such as the model name and the company (in this case, Nikon).
Example Input:
| sale_id | model_id | sale_date | sale_price |
|---|---|---|---|
| 001 | 101 | 01/22/2020 | 500 |
| 002 | 102 | 03/15/2020 | 700 |
| 003 | 101 | 07/20/2020 | 550 |
| 004 | 103 | 10/25/2020 | 800 |
| 005 | 101 | 12/30/2020 | 520 |
| 006 | 102 | 02/14/2021 | 720 |
| 007 | 101 | 04/28/2021 | 540 |
| 008 | 101 | 06/10/2021 | 530 |
| 009 | 103 | 08/23/2021 | 790 |
| 010 | 102 | 12/15/2021 | 710 |
Example Input:
| model_id | model_name | company |
|---|---|---|
| 101 | Nikon_model_X | Nikon |
| 102 | Nikon_model_Y | Nikon |
| 103 | Nikon_model_Z | Nikon |
| 104 | Other_model_A | Othercompany |
Answer:
In this scenario, an is used to connect the table with the table based on the . The field is used to perform a grouping by year and the function is used to calculate the average sale price. The clause ensures only Nikon cameras are included in the analysis. The result is ordered by and in descending order.
To practice a very similar question try this interactive Wayfair Y-on-Y Growth Rate Question which is similar for calculating yearly aggregate sales metrics or this Amazon Average Review Ratings Question which is similar for calculating average metrics over specific products.
SQL Question 5: What's a cross-join, and why are they used?
A cross-join, also known as a cartesian join, is like a mad scientist's laboratory experiment gone wild. It takes two tables and mixes them together to create a crazy new table with every possible combination of rows from the original tables.
Here's an example:
If you have 20 products and 10 colors, that's 200 rows right there! Cross-joins are great for generating all possible combinations, but they can also create really big tables if you're not careful. Just like a mad scientist, use your powers wisely!
SQL Question 6: Analyze Nikon's Click-Through-Rates
As a Data Analyst at Nikon, you have been tasked with evaluating the click-through rates of the company's digital camera product line. Specifically, the marketing team wants to understand the conversion rate of footsteps from viewing a camera product to adding it to the shopping cart.
Analyze the data for the month of August, comparing click-through rates of different camera models.
Your additionally tasks are:
- Calculate the click-through rate for each product for the month of August.
- Rank the products based on the calculated click-through rates.
Sample tables with data are provided.
Table:
| view_id | user_id | view_date | product_id |
|---|---|---|---|
| 121 | 456 | 08/01/2022 00:00:00 | 70001 |
| 154 | 867 | 08/03/2022 00:00:00 | 70002 |
| 193 | 372 | 08/05/2022 00:00:00 | 70001 |
| 262 | 482 | 08/10/2022 00:00:00 | 70003 |
| 413 | 681 | 08/15/2022 00:00:00 | 70002 |
Table:
| cart_id | user_id | add_date | product_id |
|---|---|---|---|
| 342 | 456 | 08/01/2022 00:00:00 | 70001 |
| 365 | 482 | 08/11/2022 00:00:00 | 70003 |
| 503 | 867 | 08/03/2022 00:00:00 | 70002 |
| 617 | 372 | 08/08/2022 00:00:00 | 70001 |
| 892 | 681 | 08/17/2022 00:00:00 | 70002 |
Answer:
SQL Code Block:
Answer Explanation: In this query, we first calculate the total number of views and adds to the cart per product for the month of August. We then combine these results to compute the click-through rates. If no views occurred, the rate is set as NULL to prevent division by zero. The result is ordered by the click-through rate in descending order; this gives us the products with the highest click-through rates first.
To solve a related SQL interview question on DataLemur's free interactive coding environment, attempt this SQL interview question asked by Facebook:
SQL Question 7: What's the difference between a unique and non-unique index?
Some similarities between unique and non-unique indexes include:
- Both indexes improve the performance of SQL queries by providing a faster way to lookup the desired data.
- Both indexes use an additional data which requires more storage space which impacts write performance.
- Both indexes can be created on one or more columns of a table
Some differences between unique and non-unique indexes include:
- A unique index enforces the uniqueness of the indexed columns, meaning that no duplicate values are allowed in the indexed columns. A non-unique index allows duplicate values in the indexed columns.
- A unique index can be used to enforce the primary key of a table, but a non-unique index cannot.
- A unique index can have a maximum of one NULL value in the indexed columns, but a non-unique index can have multiple NULLs
SQL Question 8: Filter Customer Records Based on a Specific Pattern
As an SQL Developer at a company like Nikon, you often have to filter through our customer records database. Suppose we want to identify the customers who have ordered any product related to "camera" from the company Nikon. For this exercise, the word "camera" is expected to be present somewhere in the product description field.
We have the following tables:
- : where each record represents a customer.
- : where each record represents a product sold by Nikon.
- : where each record represents an order made by a customer.
Write an SQL query that retrieves all the customer records who have ordered at least one product related to the "camera".
Example Input:
| customer_id | name | |
|---|---|---|
| 121 | Adam Lee | adam.lee@gmail.com |
| 235 | Eve Martin | eve.martin@yahoo.com |
| 362 | John Doe | john.doe@hotmail.com |
Example Input:
| product_id | description |
|---|---|
| 50001 | Nikon Z50 Mirrorless Camera |
| 69852 | Nikon D780 DSLR Camera |
| 30011 | Nikon 35mm Lens |
Example Input:
| order_id | customer_id | product_id |
|---|---|---|
| 8501 | 121 | 50001 |
| 8509 | 235 | 30011 |
| 8518 | 362 | 50001 |
| 8531 | 235 | 69852 |
| 8567 | 362 | 30011 |
Answer:
This query uses the LIKE operator to find all the products that have the word 'camera' in their description. Further, it joins the and tables to get the details of the customers who ordered these products. The GROUP BY clause is used to eliminate any duplicate customers from the result.
SQL Question 9: Calculate Monthly Sales of Nikon Cameras
As a data analyst at Nikon, your task is to calculate the total sales of each camera model on a monthly basis for the year 2022. Assume that every sale is logged into a database we'll call 'sales', which has the columns 'sale_id', 'camera_model', 'sale_timestamp', 'quantity', and 'price'. This will allow us to understand which camera models are performing best across different months.
Example Input:
| sale_id | camera_model | sale_timestamp | quantity | price |
|---|---|---|---|---|
| 101 | D5600 | 2022-06-01 10:20:00 | 2 | 700 |
| 102 | Z7 | 2022-06-15 14:00:00 | 1 | 3000 |
| 103 | D3500 | 2022-07-05 16:10:00 | 3 | 500 |
| 104 | D5600 | 2022-07-05 16:10:00 | 1 | 700 |
| 105 | Z7 | 2022-07-30 14:00:00 | 2 | 3000 |
Example Output:
| month | camera_model | total_sales |
|---|---|---|
| 6 | D5600 | 1400 |
| 6 | Z7 | 3000 |
| 7 | D3500 | 1500 |
| 7 | D5600 | 700 |
| 7 | Z7 | 6000 |
Answer:
You can extract the month from the 'sale_timestamp' column using the function and then group by the 'camera_model' and the extracted 'month' column to calculate the total sales for each camera model. Price multiplied by quantity will give us the total sale for each transaction - this sum is then calculated for each group.
This query first filters out the records of the year 2022. It then categorizes all the sales records into groups based on the month and camera model. In every group, it calculates the total sales by multiplying the 'price' and 'quantity' for every sale and summing them up. The resulting table lists the total sales for each camera model for each month, which shows which Nikon cameras sell the most in any given month of the year 2022.
How To Prepare for the Nikon SQL Interview
The key to acing a Nikon SQL interview is to practice, practice, and then practice some more!
Besides solving the earlier Nikon SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Google, Uber, and Microsoft.
Each exercise has hints to guide you, full answers and most importantly, there's an online SQL code editor so you can right online code up your SQL query and have it executed.
To prep for the Nikon SQL interview you can also be a great idea to solve SQL problems from other tech companies like:
However, if your SQL query skills are weak, forget about jumping right into solving questions – strengthen your SQL foundations with this interactive SQL tutorial.
This tutorial covers topics including filtering data with boolean operators and WHERE vs. HAVING – both of which pop up often during Nikon interviews.
Nikon Data Science Interview Tips
What Do Nikon Data Science Interviews Cover?
Beyond writing SQL queries, the other types of questions to prepare for the Nikon Data Science Interview are:
- Probability & Stats Questions
- Python or R Coding Questions
- Product-Sense Questions
- Machine Learning and Predictive Modeling Questions
- Behavioral & Resume-Based Questions
How To Prepare for Nikon Data Science Interviews?
To prepare for Nikon Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions sourced from FAANG tech companies
- a crash course covering SQL, Product-Sense & ML
- over 900+ reviews on Amazon & 4.5-star rating