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Analog Devices Interview Questions

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Data Structures & Algorithms

Find the missing integer from a array of consequtive integers

Find the Missing Number

Get Top N Frequent Words

Given a list of integers, find the index at which the sum of the left half of the list is equal to the right half.

Equivalent Index

Given an integer N, write a function that returns all of the prime numbers up to N

Prime to N

One Element Removed

Machine Learning

Booking Regression

Missing Housing Data

Lasso vs Ridge

Why would one algorithm generate different success rates with the same dataset?

Same Algorithm Different Success

Bagging vs Boosting

Select the 2nd highest salary in the engineering department

2nd Highest Salary

Paired Products

Write a query to get the number of customers that were upsold

Upsell Transactions

Create a report displaying which shipments were delivered to customers during their membership period.

Completed Shipments

Daily Active Users

Statistics

Say you are given a dataset of perfectly linearly separable data. What would happen when you run logistic regression?

Perfectly Separable

Reducing Error Margin

What are the assumptions of linear regression?

Assumptions of Linear Regression

Using R Squared

MLE vs MAP

Analytics

What metrics would you use to determine the value of each marketing channel?

Marketing Channel Metrics

We're interested in how user activity affects user purchasing behavior. 

Activity Conversion

Let’s say that you're in charge of an e-commerce D2C business that sells socks. What business health metrics would you care?

D2C Socks e-Commerce

How would you determine customer service quality through a chat box?

Measuring Customer Service Quality

Challenges of specific student test score layouts, recommended formatting changes for enhanced analysis, and common issues found in "messy" datasets.

Digitizing Student Test Scores

Write a SQL query to select the 2nd highest salary in the engineering department.

Note: If more than one person shares the highest salary, the query should select the next highest salary.

Example:

Input:

<code>employees</code> table

<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>first_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>last_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>department_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<code>departments</code> table

<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>
Output:

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

<tbody>
<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

We’re given a table of product purchases. Each row in the table represents an individual user product purchase.

Write a query to get the number of customers that were upsold, or in other words, the number of users who bought additional products after their first purchase.

Note: If the customer purchased two things on the same day, that does not count as an upsell, as they were purchased within a similar timeframe.

Example:

Input:

<code>transactions</code> table

<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>
Output:

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

<tbody>
<tr>
<td><code>num_of_upsold_customers</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

Let’s say we have two tables, <code>transactions</code> and <code>products</code>. Hypothetically the <code>transactions</code> table consists of over a billion rows of purchases bought by users.

We are trying to find paired products that are often purchased together by the same user, such as wine and bottle openers, chips and beer, etc..

Write a query to find the top five paired products and their names.

Notes:
 For the purposes of satisfying the test case, p2 should be the item that comes first in the alphabet.
 The <code>qty</code> column represents paired products count

Example:

Input:

<code>transactions</code> table

<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>
<code>products</code> table

<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>

<tr>
<td><code>price</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>
Output:

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

<tbody>
<tr>
<td><code>p1</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>p2</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>qty</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

Given an integer <code>N</code>, write a function that returns a list of all of the prime numbers up to <code>N</code>.

Note: Return an empty list there are no prime numbers less than or equal to <code>N</code>.

Example:

Input:

<pre tabindex="0" class="chroma"><code>N = 3
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def prime_numbers(N) -&gt; [2,3]
</code></pre>

You have an array of integers, <code>nums</code> of length <code>n</code> spanning <code>0</code> to <code>n</code> with one missing. Write a function <code>missing_number</code> that returns the missing number in the array.

Note: Complexity of $O(n)$ required.

Example:

Input:

<pre tabindex="0" class="chroma"><code>nums = [0,1,2,4,5] 
missing_number(nums) -&gt; 3
</code></pre>

Let’s say we have a table with an <code>id</code> and <code>name</code> fields. The table holds over 100 million rows and we want to sample a random row in the table without throttling the database.

Write a query to randomly sample a row from this table.

Input:

<code>big_table</code> table

<table>
<thead>
<tr>
<th>Columns</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>

Write a query to randomly sample a row from a big table.

Random SQL Sample

You’re given a string that may contain the characters <code>{</code>, <code>}</code>, <code>[</code>, <code>]</code>, <code>(</code>, and <code>)</code>.

Task: Verify that the string is balanced. A balanced string is one where every opening character, <code>{</code>, <code>[</code>, or <code>(</code>, has a corresponding closing character, <code>}</code>, <code>]</code>, or <code>)</code>.

Write a function called <code>is_balanced(string: str) -&gt; bool</code> which verifies the balance of a string.

Example:

<pre tabindex="0" class="chroma"><code>is_balanced(&#39;(())[]{}&#39;) -&gt; True
</code></pre>

<pre tabindex="0" class="chroma"><code>is_balanced(&#39;{([(){}])()}&#39;) -&gt; True
</code></pre>

<pre tabindex="0" class="chroma"><code>is_balanced(&#39;{}[]())&#39;) -&gt; False
</code></pre>

<hr/>

Write a function that tests whether a string of brackets is balanced.

The Brackets Problem

You have a table containing information about the time that employees have worked on a project.

Write a query to find how many unique calendar days each employee worked. Order your query by the <code>employee_id</code>.

Example:

An employee worked on two projects, and recorded their time like this:

<table>
<thead>
<tr>
<th>employee_id</th>
<th>project_id</th>
<th>start_date</th>
<th>end_date</th>
</tr>
</thead>

<tbody>
<tr>
<td>1	</td>
<td>1</td>
<td>2024-01-01</td>
<td>2024-01-06</td>
</tr>

<tr>
<td>1	</td>
<td>2</td>
<td>2024-01-03</td>
<td>2024-01-07</td>
</tr>
</tbody>
</table>
The number of unique calendar days is therefore 7.

<h3>Schema</h3>

Input:

<code>projects</code> table

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

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

<tr>
<td><code>project_id</code>	</td>
<td>	INTEGER	</td>
</tr>

<tr>
<td><code>start_date</code>	</td>
<td>	DATE	</td>
</tr>

<tr>
<td><code>end_date</code>	</td>
<td>	DATE	</td>
</tr>
</tbody>
</table>
Output:

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

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

<tr>
<td><code>unique_days_worked</code>	</td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

<h3>Example</h3>

Input:

<code>projects</code> table

<table>
<thead>
<tr>
<th>employee_id</th>
<th>project_id</th>
<th>start_date</th>
<th>end_date</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>1</td>
<td>2024-01-01</td>
<td>2024-01-15</td>
</tr>

<tr>
<td>1</td>
<td>1</td>
<td>2024-01-21</td>
<td>2024-01-31</td>
</tr>

<tr>
<td>1</td>
<td>2</td>
<td>2024-01-25</td>
<td>2024-02-28</td>
</tr>

<tr>
<td>2</td>
<td>3</td>
<td>2024-01-01</td>
<td>2024-01-31</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>employee_id</th>
<th>unique_days_worked</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>54</td>
</tr>

<tr>
<td>2</td>
<td>31</td>
</tr>
</tbody>
</table>

Unique Work Days

Say you are tasked with analyzing how well a model fits the data given. You want to determine a relationship between two variables.

What is the downside of only using the R-Squared $(R^2)$ value to do so?

In the context of regression analysis, why is it misleading to rely solely on a high R-squared ($R^2$) value to infer a causal relationship between variables?

In the context of regression analysis, why is it misleading to rely solely on a high R-squared ($R^2$) value to infer a causal relationship between variables?

Using R Squared I

Which of the following best describes the R-squared ($R^2$) value in the context of a regression model?

Which of the following best describes the R-squared ($R^2$) value in the context of a regression model?

Using R Squared II

What is a limitation of using R-squared ($R^2$) as the only metric to evaluate a regression model's fit?

What is a limitation of using R-squared ($R^2$) as the only metric to evaluate a regression model's fit?

Using R Squared III

To finish a class, students must pass four exams (exam ids: 1,2,3 and 4).

Given a table <code>exam_scores</code> containing the data about all of the exams that students took, form a new table to track the scores for each student.

Note: Students took each exam only once.

Example:

For the given input:

<table>
<thead>
<tr>
<th><code>student_id</code></th>
<th><code>student_name</code></th>
<th><code>exam_id</code></th>
<th><code>score</code></th>
</tr>
</thead>

<tbody>
<tr>
<td>100</td>
<td><code>Anna</code></td>
<td>1</td>
<td>71</td>
</tr>

<tr>
<td>100</td>
<td><code>Anna</code></td>
<td>2</td>
<td>72</td>
</tr>

<tr>
<td>100</td>
<td><code>Anna</code></td>
<td>3</td>
<td>73</td>
</tr>

<tr>
<td>100</td>
<td><code>Anna</code></td>
<td>4</td>
<td>74</td>
</tr>

<tr>
<td>101</td>
<td><code>Brian</code></td>
<td>1</td>
<td>65</td>
</tr>
</tbody>
</table>
the expected output should be:

<table>
<thead>
<tr>
<th><code>student_name</code></th>
<th><code>exam_1</code></th>
<th><code>exam_2</code></th>
<th><code>exam_3</code></th>
<th><code>exam_4</code></th>
</tr>
</thead>

<tbody>
<tr>
<td><code>Anna</code></td>
<td>71</td>
<td>72</td>
<td>73</td>
<td>74</td>
</tr>

<tr>
<td><code>Brian</code></td>
<td>65</td>
<td>NULL</td>
<td>NULL</td>
<td>NULL</td>
</tr>
</tbody>
</table>
Input:

<code>exam_scores</code> table

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

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

<tr>
<td><code>student_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>exam_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>score</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
Output:

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

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

<tr>
<td><code>exam_1</code></td>
<td>INT</td>
</tr>

<tr>
<td><code>exam_2</code></td>
<td>INT</td>
</tr>

<tr>
<td><code>exam_3</code></td>
<td>INT</td>
</tr>

<tr>
<td><code>exam_4</code></td>
<td>INT</td>
</tr>
</tbody>
</table>

List out the exams sources of each student in MySQL

Exam Scores

We’re given two tables. One is named <code>projects</code> and the other maps employees to the projects they’re working on.

Write a query to get the top five most expensive projects by budget to employee count ratio.

Note: Exclude projects with 0 employees. Assume each employee works on only one project.

<code>projects</code> table

<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>title</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>state_date</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>end_date</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>budget</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<code>employee_projects</code> table

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

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

<tr>
<td><code>employee_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
Output:

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

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

<tr>
<td><code>budget_per_employee</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

Employee Project Budgets

As an accountant for a local grocery store, you have been tasked with calculating the daily sales of each product since their last restocking.

You have been provided with three tables: <code>products</code>, <code>sales</code>, and <code>restocking</code>. The <code>products</code> table contains information about each product, the <code>sales</code> table records the sales transactions, and the <code>restocking</code> table tracks the restocking events.

Write a query to retrieve the running total of sales for each product since its last restocking event.

Notes: The <code>sales_since_last_restock</code> column represents the cumulative sales of the product for each day since its last restocking event. Order the result set by <code>product_id</code>.

Example:

Input:

Table <code>products</code>:

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

<tbody>
<tr>
<td>product_id</td>
<td>int</td>
</tr>

<tr>
<td>product_name</td>
<td>varchar</td>
</tr>
</tbody>
</table>
Table <code>sales</code>:

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

<tbody>
<tr>
<td>sales_id</td>
<td>int</td>
</tr>

<tr>
<td>product_id</td>
<td>int</td>
</tr>

<tr>
<td>date</td>
<td>date</td>
</tr>

<tr>
<td>sold_count</td>
<td>int</td>
</tr>
</tbody>
</table>
Table <code>restocking</code>:

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

<tbody>
<tr>
<td>restock_id</td>
<td>int</td>
</tr>

<tr>
<td>product_id</td>
<td>int</td>
</tr>

<tr>
<td>restock_date</td>
<td>date</td>
</tr>
</tbody>
</table>
Output

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

<tbody>
<tr>
<td>product_name</td>
<td>varchar</td>
</tr>

<tr>
<td>date</td>
<td>date</td>
</tr>

<tr>
<td>sales_since_last_restock</td>
<td>int</td>
</tr>
</tbody>
</table>

Calculate daily sales of each product since last restocking.

Cumulative Sales Since Last Restocking

Say you are tasked with designing a data mart or data warehouse for a new online retailer.

How would you design the system?

Note: Sketch a star schema to explain your design.

Design a data warehouse for a new online retailer

Retailer Data Warehouse

Data Modeling

Given a list of integers, find the index at which the sum of the left half of the list is equal to the right half.

If there is no index where this condition is satisfied return -1.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>nums = [1, 7, 3, 5, 6]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>equivalent_index(nums) -&gt; 2
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>nums = [1,3,5]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>equivalent_index(nums) -&gt; -1
</code></pre>

Given a <code>ListNode</code> representing the head of a linked list, write a function <code>is_cyclic</code> which returns <code>True</code> if a linked list is cyclic and <code>False</code> if it is not. A linked list is cyclic when there is no tail to the linked list, and the supposed tail is attached to another node inside the list itself, creating a cycle.

A <code>ListNode</code> is defined as:

<pre tabindex="0" class="chroma"><code>class ListNode:
	value: ListNode = None
 next: ListNode = None
</code></pre>

Example:

Input:

<img src="/s3/questions/1a65651d-a483-4481-b073-2738d65ab9d7.png" alt="image"/>

Output:

<pre tabindex="0" class="chroma"><code>def is_cyclic(head) -&gt; True
</code></pre>

Cyclic Detection

Let’s say that you’re a data scientist working on a distribution team at Amazon.

Create a report displaying which shipments were delivered to customers during their membership period.

If the transaction is shipped during the membership period, column <code>is_member</code> should have the value <code>Y</code>. If not, the column should have the value <code>N</code>.

Hint: You may assume that <code>customer_id</code> is unique and that shipments are made only to registered customers.

Example:

Input:

<code>customers</code> table

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

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

<tr>
<td><code>membership_start_date</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>membership_end_date</code></td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
<code>shipments</code> table

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

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

<tr>
<td><code>ship_date</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>customer_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>quantity</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
Output:

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

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

<tr>
<td><code>ship_date</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>customer_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>is_member</code></td>
<td>TEXT</td>
</tr>

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

There are two lists, <code>list X</code> and <code>list Y</code>. Both lists contain integers from <code>-1000</code> to <code>1000</code> and are identical to each other except that one integer is removed in <code>list Y</code> that exists in <code>list X</code>.

Write a function <code>one_element_removed</code> that takes in both lists and returns the integer that was removed in $O(1)$ space and $O(n)$ time without using the python set function.

Example:

Input:

<pre tabindex="0" class="chroma"><code>list_x = [1,2,3,4,5]
list_y = [1,2,4,5]

one_element_removed(list_x, list_y) -&gt; 3
</code></pre>

Which of the following methods would be the most efficient way of identifying what element is not included in two lists of integers that differ by a single element being removed?

For example, if the lists are <code>[1,2,3,4,5]</code> and <code>[1,2,4,5]</code>, the function should return <code>3</code>

Which of the following methods would be the most efficient way of identifying what element is not included in two lists of integers that differ by a single element being removed? 

For example, if the lists are `[1,2,3,4,5]` and `[1,2,4,5]`, the function should return `3`

Efficiently Identifying Missing Integer

A digital health startup is analyzing ECG sensor data captured from wearable heart monitors. Each row in the dataset represents one heartbeat, including its timestamp and calculated heart rate at that moment.

The clinical team wants to detect tachycardia runs, periods where a patient’s heart rate stays above 100 bpm continuously for at least 10 seconds .

Write a SQL query to identify every valid tachycardia run and return:

<ol>
<li><code>patient_id</code></li>
<li><code>run_start</code>: timestamp of the first beat in the run</li>
<li><code>run_end</code>: timestamp of the last beat in the run</li>
<li><code>duration_seconds</code>: difference in seconds between start and end (rounded to 2 decimals)</li>
</ol>

Sort results by <code>patient_id</code>, then <code>run_start</code>.

Definition of a Tachycardia Run

<ul>
<li>All consecutive heartbeats must have <code>heart_rate &gt; 100</code></li>
<li>Total duration ≥ 10 seconds</li>
<li>Any gap ≥ 3 seconds between consecutive beats breaks the run</li>
</ul>

Note: A patient may have multiple separate runs.

Schema

Input

<code>ecg_readings</code> table

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

<tbody>
<tr>
<td>patient_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>timestamp</td>
<td>DATETIME</td>
</tr>

<tr>
<td>heart_rate</td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
Output

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

<tbody>
<tr>
<td>patient_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>run_start</td>
<td>DATETIME</td>
</tr>

<tr>
<td>run_end</td>
<td>DATETIME</td>
</tr>

<tr>
<td>duration_seconds</td>
<td>DECIMAL</td>
</tr>
</tbody>
</table>
Example

Input

<code>ecg_readings</code> table

<table>
<thead>
<tr>
<th>patient_id</th>
<th>timestamp</th>
<th>heart_rate</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>2025-03-01 10:00:00</td>
<td>98</td>
</tr>

<tr>
<td>1</td>
<td>2025-03-01 10:00:02</td>
<td>105</td>
</tr>

<tr>
<td>1</td>
<td>2025-03-01 10:00:04</td>
<td>110</td>
</tr>

<tr>
<td>1</td>
<td>2025-03-01 10:00:07</td>
<td>108</td>
</tr>

<tr>
<td>1</td>
<td>2025-03-01 10:00:12</td>
<td>107</td>
</tr>

<tr>
<td>1</td>
<td>2025-03-01 10:00:15</td>
<td>85</td>
</tr>

<tr>
<td>1</td>
<td>2025-03-01 10:00:20</td>
<td>120</td>
</tr>

<tr>
<td>1</td>
<td>2025-03-01 10:00:28</td>
<td>122</td>
</tr>
</tbody>
</table>
Output

<table>
<thead>
<tr>
<th>patient_id</th>
<th>run_start</th>
<th>run_end</th>
<th>duration_seconds</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>2025-03-01 10:00:02</td>
<td>2025-03-01 10:00:12</td>
<td>10.00</td>
</tr>
</tbody>
</table>

<h3>Explanation</h3>

The high-rate streak from 10:00:02 to 10:00:12 forms one continuous sequence because each gap between timestamps (2 to 4, 4 to 7, and 7 to 12) is less than three seconds. This creates a valid 10-second run. The later high values at seconds 20 and 28 are separated by an eight-second gap, so they form a different sequence that is shorter than 10 seconds and is therefore ignored.

Write a SQL query to identify every valid tachycardia run.

Detecting ECG Tachycardia Runs

Let’s say we have a sample size of $n$. The margin of error for our sample size is 3.

How many more samples would we need to decrease the margin of error to 0.3?

Let's say we have a sample size of N. The margin of error for our sample size is 3.

How many more samples would we need to decrease the margin of error to 0.3?

Let's say we have a sample size of N. The margin of error for our sample size is 3.

How many more samples would we need to decrease the margin of error to 0.3?

Say you are given a dataset of perfectly linearly separable data.

What would happen when you run logistic regression?

What happens when logistic regression is run on a dataset of perfectly linearly separable data?

What happens when logistic regression is run on a dataset of perfectly linearly separable data?

Given that $X$ and $Y$ are independent random variables with normal distributions, what is the mean and variance of the distribution of $2X - Y$ when the corresponding distributions are $X\sim \mathcal{N}(3, 4)$ and $Y \sim \mathcal{N}(1, 4)$?

Given that $X$ and $Y$ are independent random variables with normal distributions, and the corresponding distributions are $X\sim \mathcal{N}(3, 4)$ and $Y \sim \mathcal{N}(1, 4)$, what is the mean of the distribution of $2X - Y$?

Given that $X$ and $Y$ are independent random variables with normal distributions, and the corresponding distributions are $X\sim \mathcal{N}(3, 4)$ and $Y \sim \mathcal{N}(1, 4)$, what is the mean of the distribution of $2X - Y$?

Distribution of 2X - Y I

Given that $X$ and $Y$ are independent random variables with normal distributions, and the corresponding distributions are $X\sim \mathcal{N}(3, 4)$ and $Y \sim \mathcal{N}(1, 4)$, what is the variance of the distribution of $2X - Y$?

Given that $X$ and $Y$ are independent random variables with normal distributions, and the corresponding distributions are $X\sim \mathcal{N}(3, 4)$ and $Y \sim \mathcal{N}(1, 4)$, what is the variance of the distribution of $2X - Y$?

Distribution of 2X - Y II

Distribution of 2X - Y

Probability

Given a 2-D matrix P of predicted values and actual values, write a function precision_recall to calculate precision and recall metrics.

Return the ordered pair (precision, recall).

Example:

Input:

<pre tabindex="0" class="chroma"><code>
P = [[121, 9],
 [17, 144]]
</code></pre>

where P represents the following table

<table>
<thead>
<tr>
<th></th>
<th>Predicted =TRUE</th>
<th>Predicted =FALSE</th>
</tr>
</thead>

<tbody>
<tr>
<td>Actual =TRUE</td>
<td>121</td>
<td>9</td>
</tr>

<tr>
<td>Actual =FALSE</td>
<td>17</td>
<td>144</td>
</tr>
</tbody>
</table>
Output:

<pre tabindex="0" class="chroma"><code>def precision_recall(P) -&gt; (0.931, 0.877,)
</code></pre>

Write a function to calculate precision and recall metrics.

Precision and Recall

We want to build a model to predict housing prices in the city of Seattle. We’ve scraped 100K sold listings over the past three years but found that around 20% of the listings are missing square footage data.

How do we deal with the missing data to construct our model?

We want to build a model to predict housing prices in the city of Seattle. We’ve scraped 100K sold listings over the past three years, but have discovered that around 20% of the listings are missing square footage data.

How would you approach dealing with this missing data, in order to construct the most useful predictive model possible?

We want to build a model to predict housing prices in the city of Seattle. We’ve scraped 100K sold listings over the past three years, but have discovered that around 20% of the listings are missing square footage data.

How would you approach dealing with this missing data, in order to construct the most useful predictive model possible?



You are given a binary tree of unique positive numbers. Each node in the binary tree is represented as a dictionary with the following keys:

<ul>
<li><code>data</code>: integer value stored in the node</li>
<li><code>left</code>: reference to the left child (or <code>None</code>)</li>
<li><code>right</code>: reference to the right child (or <code>None</code>)</li>
</ul>

<pre tabindex="0" class="chroma"><code>node = {
 &#34;data&#34;: 6,
 &#34;left&#34;: {
 &#34;data&#34;: 3,
 &#34;left&#34;: {...},
 &#34;right&#34;: {...}
 },
 &#34;right&#34;: {
 &#34;data&#34;: 9,
 &#34;left&#34;: {...},
 &#34;right&#34;: {...}
 }
}
</code></pre>

Given two node values as input (<code>value1</code> and <code>value2</code>), write a function to return the value of the nearest common ancestor (lowest node in the tree that has both nodes as descendants).

Note: If one of the nodes doesn’t exist in the tree, return <code>-1</code>.

Example:

Input:

<pre tabindex="0" class="chroma"><code># Diagram of the binary tree
&#39;&#39;&#39;
 6 
 / \ 
 3 9 
 / \
 2 11
 / \
 5 8
&#39;&#39;&#39;
value1 = 8
value2 = 2
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>common_ancestor(root,value1,value2) -&gt; 3
</code></pre>

Explanation:

<ul>
<li>Ancestors of <code>8</code>: <code>11 → 3 → 6</code></li>
<li>Ancestors of <code>2</code>: <code>3 → 6</code></li>
<li>Common ancestors: <code>3</code> and <code>6</code></li>
<li>The nearest common ancestor is <code>3</code>.</li>
</ul>

Write a function to return the value of the nearest node that is a parent to both nodes.

Nearest Common Ancestor

What is the difference between covariance and correlation? Provide an example.

Covariance vs Correlation

Given the head of a singly linked list represented as a <code>ListNode</code>, and two zero-indexed positions <code>x</code> and <code>y</code>, write a function <code>swap_node</code> which swaps the positions of nodes <code>x</code> and <code>y</code> and returns the new head. Note that you cannot simply swap the values of these nodes, as these nodes do not have values. Instead, you must swap these two using pointer manipulation (i.e., modifying the <code>next</code> references of the involved nodes inside the list).

A <code>ListNode</code> is defined as:

<pre tabindex="0" class="chroma"><code>class ListNode:
 next: ListNode = None
</code></pre>

Example:

Input:

<pre tabindex="0" class="chroma"><code>linked_list = ListNode(0x01) -&gt; ListNode(0x02) -&gt; ListNode(0x03) 
# Here, imagine that &#39;0x0_&#39; refer to their memory locations.
head = 0x01
x = 1
y = 2
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def swap_node(head, x, y) -&gt; ListNode(0x01) -&gt; ListNode(0x03) -&gt; ListNode(0x02)
</code></pre>

Swapping Nodes

Write a Python function called <code>group_anagrams</code> that takes a list of strings as input and returns a list of lists, where each inner list contains a group of anagrams that were present in the input list.

An anagram is a word or phrase formed by rearranging the letters of a different word or phrase, using all the original letters exactly once.

Note: Each of the inner lists should be ordered alphabetically and contain no duplicate values

<h3>Example:</h3>

Input:

<pre tabindex="0" class="chroma"><code>words = [&#34;eat&#34;, &#34;tea&#34;, &#34;tan&#34;, &#34;ate&#34;, &#34;nat&#34;, &#34;bat&#34;]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>[ 
 [&#34;nat&#34;,&#34;tan&#34;],
 [&#34;ate&#34;,&#34;eat&#34;, &#34;tea&#34;],
 [&#34;bat&#34;]
]
</code></pre>

Explanation:

In the example, the words “eat”, “tea”, and “ate” are anagrams of each other, as are the words “tan” and “nat”. The word “bat” does not have any anagrams in the input list, and therefore is listed on its own. The function then groups the anagrams together and returns a list of lists representing these groups.

Each of the lists representing a group of anagrams is sorted in alphabetical order.

Identify the groups of anagrams in a list of words

Groups of Anagrams

Let’s say you’re given all the different marketing channels along with their respective marketing costs at a company called Mode, that sells B2B analytics dashboards.

What metrics would you use to determine the value of each marketing channel?

When determining the value of each marketing channel for the company Mode, which metric is considered the key metric?

When determining the value of each marketing channel for the company Mode, which metric is considered the key metric?

Given a table of account statuses, write a query to get the percentage of accounts that were active on December 31st, 2019, and closed on January 1st, 2020, over the total number of accounts that were active on December 31st.
Each account has only one daily record indicating its status at the end of the day.

Note: Round the result to 2 decimal places.

Example:

Input:

<code>account_status</code> table

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

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

<tr>
<td><code>date</code></td>
<td>DATETIME</td>
</tr>

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

<table>
<thead>
<tr>
<th>account_id</th>
<th>date</th>
<th>status</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>2020-01-01</td>
<td>closed</td>
</tr>

<tr>
<td>1</td>
<td>2019-12-31</td>
<td>open</td>
</tr>

<tr>
<td>2</td>
<td>2020-01-01</td>
<td>closed</td>
</tr>
</tbody>
</table>
Output:

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

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

Closed Accounts

Given a table of user logs with platform information, count the number of daily active users on each platform for the year of 2020.

Example:

Input:

<code>events</code> table

<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>action</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>url</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>platform</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
Output:

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

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

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

<tr>
<td><code>daily_users</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

Calculate Moving Average	SQL	Easy
Predict Customer Churn	Machine Learning	Medium
A/B Test Significance	Statistics	Medium
Optimize Query Performance	SQL	Hard
Feature Importance Analysis	Machine Learning	Medium
Clean Missing Data	Python	Easy
Neural Network Architecture	Deep Learning	Hard
Calculate Cohort Retention	SQL	Medium
Bayesian Probability	Statistics	Easy
Recommend Similar Products	Machine Learning	Hard

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