# 8 Energizer SQL Interview Questions (Updated 2024)

Updated on

August 11, 2024

Energizer employees use SQL to analyze battery performance data, including voltage, discharge rates, and lifespan, to identify areas for improvement. It is also used to optimize supply chain management by querying and manipulating databases to track inventory levels and shipping logistics, the reason why Energizer includes SQL problems during interviews for Data Science and Data Engineering positions.

So, to help you prepare, here’s 8 Energizer SQL interview questions – can you answer each one?

## 8 Energizer SQL Interview Questions

### SQL Question 1: Calculate Average Monthly Energy Consumption

Assume you're provided with a data set that gives information about the energy use of different battery products produced by Energizer over time. The data set gives the , the of the year, the , and the in kilowatt-hours(kWh), i.e., the amount of energy consumed. The table might look like below.

The table is named .

##### Example Input:
product_idyearmonthusage
10012022145
20022022160
10012022233
20022022270
10012022250
10012023265
20022023280

Your task is to write a SQL query that calculates the average monthly usage of each battery product over the entire period, as well as the ranking of this average usage among all products for each particular month.

If a product has no entries for a given month (e.g., it was not in use), it should be excluded from the calculation and the ranking for that month. The output should be sorted by and .

##### Example Output:
year_monthproduct_idrankingavg_usage
2022-011001245
2022-012002160
2022-021001141.5
2022-022002270
2023-021001265
2023-022002180

The following PostgreSQL query using window function should solve the problem.

This query first calculates the average usage per product per month-year using a window function in a subquery. It then ranks these average usages within each month-year using the RANK() window function. The results are ordered by and , consistent with the specified output format.

For more window function practice, solve this Uber SQL problem within DataLemur's interactive SQL code editor:

### SQL Question 2: Employees Earning More Than Their Boss

Assume you had a table of Energizer employee salary data. Write a SQL query to find all employees who earn more than their direct manager.

#### Energizer Example Input:

employee_idnamesalarydepartment_idmanager_id
1Emma Thompson38001
2Daniel Rodriguez2230110
3Olivia Smith800018
4Noah Johnson680028
5Sophia Martinez1750110
8William Davis70002NULL
10James Anderson40001NULL

#### Example Output:

employee_idemployee_name
3Olivia Smith

This is the output because Olivia Smith earns \$8,000, surpassing her manager, William Davis who earns 7,800.

Write a SQL query for this problem directly within the browser on DataLemur:

First, we perform a SELF-JOIN where we treat the first table () as the managers' table and the second table () as the employees' table. Then we use a clause to filter the results, ensuring we only get employees whose salaries are higher than their manager's salary.

If the code above is confusing, you can find a detailed solution with hints here: Employee Salaries Higher Than Their Manager.

### SQL Question 3: Can you describe the meaning of a constraint in SQL in layman's terms?

Constraints are used to specify the rules concerning data in the table. It can be applied for single or multiple fields in an SQL table during the creation of the table or after creating using the ALTER TABLE command. The constraints are:

Say you were storing sales analytyics data from Energizer's CRM inside a database. Here's some example constraints you could use:

• PRIMARY KEY constraint: You might use a PRIMARY KEY constraint to ensure that each record in the database has a unique identifier. For example, you could use the "opportunity_id" field as the primary key in the "opportunities" table.

• FOREIGN KEY constraint: You might use a FOREIGN KEY constraint to link the data in one table to the data in another table. For example, you could use a foreign key field in the "opportunities" table to reference the "account_id" field in the "accounts" table.

• NOT NULL constraint: You might use a NOT NULL constraint to ensure that a field cannot contain a NULL value. For example, you could use a NOT NULL constraint on the "opportunity_name" field in the "opportunities" table to ensure that each opportunity has a name.

• UNIQUE constraint: You might use a UNIQUE constraint to ensure that the data in a field is unique across the entire table. For example, you could use a UNIQUE constraint on the "email" field in the "contacts" table to ensure that each contact has a unique email address.

• CHECK constraint: You might use a CHECK constraint to ensure that the data in a field meets certain conditions. For example, you could use a CHECK constraint to ensure that the "deal_probability" field in the "opportunities" table is a value between 0 and 100.

• DEFAULT constraint: You might use a DEFAULT constraint to specify a default value for a field. For example, you could use a DEFAULT constraint on the "stage" field in the "opportunities" table to set the default value to "prospecting"

### SQL Question 4: Analyzing Click-through Rates for Energizer

As a data analyst for the company Energizer, they want to analyze the performance of their digital ads. They are interested in getting information about the click-through rates (CTR) for their ads. Specifically, they like to know the number of unique users who have clicked their ads and the number of unique users who have purchased the products. They have data in two tables.

One table, named captures each click event with the user who clicked and the time of the click.

Another table, named captures each purchase event with the user who made the purchase and the time of the purchase.

Calculate the click through rate (CTR) as the number of unique users who have made a purchase after clicking an ad divided by the total number of unique users who clicked the ad, for each product.

##### Example Input:
click_iduser_idclick_timeproduct_id
10123512/01/2022 02:00:00501
26456812/01/2022 02:10:00502
85763512/02/2022 01:00:00501
12831912/02/2022 06:00:00501
93526812/02/2022 06:20:00502
##### Example Input:
purchase_iduser_idpurchase_timeproduct_id
7613512/01/2022 02:30:00501
3981912/02/2022 06:45:00501
5126812/01/2022 02:40:00502
9989012/01/2022 02:50:00502

This query first joins the clicks and purchases tables on user_id and product_id. It further filters the join to only include rows where the purchase happened after the click. Lastly, it groups by product_id, and for each product, it calculates the click through rate by dividing the count of distinct purchase user_id's by the count of distinct click user_id's. The output ctr is a float value representing the click-through rate for each product.

To practice another question about calculating rates, try this TikTok SQL Interview Question on DataLemur's interactive coding environment:

### SQL Question 5: Could you describe a self-join and provide a scenario in which it would be used?

A self-join is a operation in which a single table is joined to itself. To perform a self-join, you must specify the table name twice in the clause, giving each instance a different alias. You can then join the two instances of the table using a clause, and specify the relationship between the rows in a clause.

Think of using a self-joins whenever your data analysis involves analyzie pairs of the same things, like comparing the salaries of employees within the same department, or identifying pairs of products that are frequently purchased together (which you can do in this real SQL question from a Walmart interview).

For another self-join example, suppose you were conducting an HR analytics project and needed to examine how frequently employees within the same department at Energizer interact with one another, you could use a self-join query like the following to retrieve all pairs of Energizer employees who work in the same department:

This query returns all pairs of Energizer employees who work in the same department, and excludes pairs where the employee's id is the same (since this would represent the same Energizer employee being paired with themselves).

### SQL Question 6: Calculate the Average Power Capacity for Each Battery Type sold Monthly

In Energizer, we have a variety of batteries that vary by type and capacity. Could you write a SQL query that can tell us what the average capacity in mAh was for each type of battery for each month of the previous year? Use the sales data and product details given below.

##### Example Input:
sales_idbattery_idsale_date
101B012022-01-15
102B022022-01-20
103B032022-02-22
104B012022-02-23
105B022022-03-30
##### Example Input:
battery_idtypecapacity_mAh
B01AA1500
B02AAA1200
B03D3500
##### Example Output:
monthtypeavg_capacity
01AA1500
01AAA1200
02AA1500
02D3500
03AAA1200

The below PostgreSQL query joins both tables on the battery_id field and groups the result by the sale_date's month and battery type. The AVG function then calculates the average for each type in each month.

This SQL block will show the average battery capacity, grouped by battery type and month. This information could be useful for understanding which types of batteries tend to have higher capacities.

### SQL Question 7: Can you list the various types of joins in SQL, and describe their purposes?

Using a join in SQL, you can retrieve data from multiple tables and merge the results into a single table.

In SQL, there are four distinct types of JOINs. To demonstrate each kind, Imagine you had two database tables: an table that contains data on Google Ads keywords and their bid amounts, and a table with information on product sales and the Google Ads keywords that drove those sales.

• : An INNER JOIN 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.

• : A LEFT JOIN retrieves all rows from the left table (in this case, the table) and any matching rows from the right table (the Sales table). If there is no match in the right table, values will be returned for the right table's columns.

• : A RIGHT JOIN retrieves all rows from the right table (in this case, the Sales table) and any matching rows from the left table (the table). If there is no match in the left table, values will be returned for the left table's columns.

• : A FULL OUTER JOIN 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 8: Analyzing Customer Purchases and Product Ratings.

In your role as a database analyst at Energizer, you have been asked to analyze the relationship between customer purchases and product ratings. Specifically, your task is to write a SQL query that joins the and tables. Your query should return all products purchased by customers in 'California' with their corresponding average ratings.

Here are some sample tables:

#### Table:

purchase_idcustomer_idproduct_idpurchase_datecustomer_region
746712433000105/15/2022California
837673721365206/20/2022New York
984156733000107/25/2022California
324398431365208/11/2022California
453183533000108/12/2022Texas

#### Table:

rating_idproduct_idrating
6271300014.0
7814136523.5
9253300013.2
8326136524.1
3927300014.5

Here is a PostgreSQL query which can be used to solve this:

This query first joins the table with the table on the column. It then filters the results to only include purchases made by customers in 'California'. It then groups the results by and calculates the average rating for each product. The function is used to limit the average rating to 2 decimal places.

Since join questions come up so often during SQL interviews, try this SQL join question from Spotify:

### How To Prepare for the Energizer SQL Interview

Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Energizer SQL interview is to solve as many practice SQL interview questions as you can! Beyond just solving the above Energizer SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Netflix, Google, and Amazon.

Each interview question has multiple hints, full answers and most importantly, there is an interactive SQL code editor so you can right in the browser run your SQL query and have it graded.

To prep for the Energizer SQL interview you can also be useful to solve interview questions from other consumer good companies like:

Power up your knowledge with Energizer's latest news and press releases!

However, if your SQL skills are weak, don't worry about jumping right into solving questions – improve your SQL foundations with this free SQL for Data Analytics course.

This tutorial covers SQL concepts such as using ORDER BY and using wildcards with LIKE – both of these show up frequently during SQL job interviews at Energizer.

### Energizer Data Science Interview Tips

#### What Do Energizer Data Science Interviews Cover?

In addition to SQL query questions, the other question categories to prepare for the Energizer Data Science Interview include:

#### How To Prepare for Energizer Data Science Interviews?

To prepare for Energizer Data Science interviews read the book Ace the Data Science Interview because it's got:

• 201 interview questions taken from FAANG, tech startups, and Wall Street
• a refresher on Python, SQL & ML
• over 1000+ reviews on Amazon & 4.5-star rating

Don't forget about the behavioral interview – prepare for that using this guide on behavioral interview questions.