The Dandy Business Intelligence interview guide, interview questions, salary data, and interview experiences.

Dandy Business Intelligence Interview Guide

<p>Getting ready for an Business Intelligence interview at Dandy? The Dandy Business Intelligence interview span across 10 to 12 different question topics. In preparing for the interview:</p>
<ul>
<li>Know what skills are necessary for Dandy Business Intelligence roles.</li>
<li>Gain insights into the Business Intelligence interview process at Dandy.</li>
<li>Practice real Dandy Business Intelligence interview questions.</li>
</ul>
<p>Interview Query regularly analyzes interview experience data, and we've used that data to produce this guide, with sample interview questions and an overview of the Dandy Business Intelligence interview.</p>


Getting ready for an Business Intelligence interview at Dandy? The Dandy Business Intelligence interview span across 10 to 12 different question topics. In preparing for the interview:

- Know what skills are necessary for Dandy Business Intelligence roles.
- Gain insights into the Business Intelligence interview process at Dandy.
- Practice real Dandy Business Intelligence interview questions.

Interview Query regularly analyzes interview experience data, and we've used that data to produce this guide, with sample interview questions and an overview of the Dandy Business Intelligence interview.
        

Overview

Dandy Business Intelligence Salary

<p>We've gathered this data from parsing thousands of interview experiences sourced from members.</p>

We've gathered this data from parsing thousands of interview experiences sourced from members.

Dandy Business Intelligence Interview Process

<p>We’re given a table of bank transactions with three columns, <code>user_id</code>, a deposit or withdrawal value (determined if the value is positive or negative), and <code>created_at</code> time for each transaction.</p>

<p>Write a query to get the total three-day rolling average for deposits by day.</p>

<p><em>Note: Please use the format <code>&#39;%Y-%m-%d&#39;</code> for the date in the outout</em></p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>bank_transactions</code> table</p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>user_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>transaction_value</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>dt</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>rolling_three_day</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>


Rolling Bank Transactions

<p>Write a query to identify customers who placed more than three transactions each in both 2019 and 2020.</p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>transactions</code> table</p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>user_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>product_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>quantity</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><code>users</code> table</p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p> 
<strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>customer_name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>


<p>Which of the following queries will return a list of customers and the total amount they have spent on orders?</p>


Which of the following queries will return a list of customers and the total amount they have spent on orders?

Customer Orders

<p>Let’s say you’re working for a social media company trying to grow your user base. You know that people tend to use the same social network as their peers, so you want to target all your growth and marketing efforts on specific age groups so as to take advantage of network effects.</p>

<p>You wish to begin with the age group that is currently generating the most connections (fastest growing).</p>

<p>Write a SQL query to find the average number of accepted friend requests for each age group and order the results in descending order.</p>

<p><em>Note: The average number of accepted friend requests is calculated as the total number of friend requests sent by an age group that was accepted divided by the number of members in said age group.</em></p>

<p><em>We can assume the tables hold valid data in that there are no duplicate friend requests, friend requests are only accepted once, and user A can only accept a friend request from User B after B sends A a friend request.</em></p>

<p><em>The result should be rounded to two decimal places.</em></p>

<h3>Example:</h3>

<p><strong>Input:</strong></p>

<p><strong><code>friend_requests</code></strong></p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td>requester_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>receiver_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>sent_at</td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
<p><strong><code>requests_accepted</code></strong></p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td>accepter_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>requester_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>accepted_at</td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
<p><strong><code>age_groups</code></strong></p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td>user_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>age_group</td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td>age_group</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>average_acceptance</td>
<td>FLOAT</td>
</tr>
</tbody>
</table>


Find the average number of accepted friend requests for each age group that sent the requests.

Dandy Business Intelligence Interview Guide

Overview

Dandy Business Intelligence Salary

Dandy Business Intelligence Interview Process

Dandy Business Intelligence Interview Questions

Dandy Business Intelligence Jobs