Prepare for and practice interview questions from Techligent Systems Inc. across topics like SQL, Statistics, Machine Learning and more.

Techligent Systems Inc. Interview Questions

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Data Structures & Algorithms

Write a function data_stream_median to calculate the median from a stream of integers.

Data Stream Median

This question requires the implementation of the Fibonacci sequence using three different methods: recursively, iteratively, and using memoization.

Implementing the Fibonacci Sequence in Three Different Methods

Given the root node, verify if a binary search tree is valid or not.

Binary Tree Validation

Machine Learning

How does random forest generate the forest and why use it over logistic regression

Random Forest Explanation

Explain the difference between XGBoost and random forest and give an example where you would use one over the other

Xgboost vs Random Forest

Behavioral

Describing a data project and its challenges

Hurdles In Data Projects

What do you tell an interviewer when they ask you what your strengths and weaknesses are?

Your Strengths and Weaknesses

Data Modeling

Design a database schema for a Tinder-style dating app and discuss needed optimizations

Swiping App Design

Design a database for a ride-sharing app.

Ride-Sharing App Schema

Product Sense & Metrics

Delivering an exceptional customer experience by focusing on key customer-centric parameters

Uber Eats Customer Experience

How does random forest generate the forest? Additionally, why would we use it over other algorithms such as logistic regression?

What happens when you average the output on multiple decision trees?

What happens when you average the output on multiple decision trees?

Average Trees

Tell me about a data project that didn’t go the way you expected. What did you set out to do, what surprised you, and how did you handle it?

Design a database to represent a Tinder style dating app. 

What does the schema look like and what are some optimizations that you think we might need?

Let’s say you are making a swiping app, similar to Tinder.

What would be the most efficient way (in terms of retrieval time) to store which users have swiped on each other on a database backend?

Let's say you are making a swiping app, similar to Tinder.

What would be the most efficient way (in terms of retrieval time) to store which users have swiped on each other on a database backend?

Swipes Database Design

The Fibonacci sequence is a series of numbers in which each number is the sum of the two preceding ones, usually starting with 0 and 1. It is often used in algorithm examples, and is defined by the following formula: F(n) = F(n-1) + F(n-2), with F(0) = 0 and F(1) = 1.

Your task is to implement the Fibonacci algorithm in three different methods:
1. Recursively
2. Iteratively
3. Using Memoization

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>n = 5
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>fibonacci(n) -&gt; 5
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>n = 10
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>fibonacci(n) -&gt; 55
</code></pre>

The Fibonacci sequence starts as follows: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55…

<h1>Binary Tree Validation</h1>

You are given the root of a binary tree. You need to determine if it is a valid binary search tree (BST).

A valid BST is defined as follows:

<ul>
<li>The left subtree of a node contains only nodes with values less than or equal to the node’s value.</li>
<li>The right subtree of a node contains only nodes with values greater than or equal to the node’s value.</li>
<li>Both the left and right subtrees must also be binary search trees.</li>
</ul>

Given the function <code>def is_valid_bst(root: Node) -&gt; bool:</code>, return True if the binary tree is a valid BST. Otherwise, return False.

<h3>Example:</h3>

Input:

<img src="/s3/questions/9664817c-3e12-4a40-9236-8382b13f55a0.png" alt="image"/>Converted Binary Tree.png

Output:

<pre tabindex="0" class="chroma"><code>def is_valid_bst(Node(3)) -&gt; True
</code></pre>

Let’s say you work at a company developing a new ride-sharing app.

How would you design a database that could record rides between riders and drivers?

What would the schema of the tables look like and how would they join together?

Write a function <code>data_stream_median</code> to calculate the new median from a stream of ordered integers and the new element which is not yet inserted into the stream.

The median is the middle value in an ordered integer list. If the size of the list is even, there is no middle value, so the median is the mean of the two middle values.

Example:

<pre tabindex="0" class="chroma"><code>new_value = 2
stream = [1, 2, 3, 4, 5, 6]
def data_stream_median(new_value, stream): -&gt; 3
</code></pre>

To improve customer experience on Uber Eats, what key parameters would you focus on improving?

Explain the difference between the XGBoost and random forest algorithms and give an example where you would use one over the other.

Why might a data scientist choose to use XGBoost instead of Random Forest for a particular machine learning task?

Why might a data scientist choose to use XGBoost instead of Random Forest for a particular machine learning task?

XGBoost vs Random Forest: Choice

When an interviewer asks a question along the lines of:

<ul>
<li>What would your current manager say about you? What constructive criticisms might he give?</li>
<li>What are your three biggest strengths and weaknesses you have identified in yourself?</li>
</ul>

How would you respond?

When asked about your strengths in an interview, what is an effective way to respond?

When asked about your strengths in an interview, what is an effective way to respond?

Your Strengths and Weaknesses I

Which of the following is an acceptable strategy when discussing weaknesses in an interview?

Which of the following is an acceptable strategy when discussing weaknesses in an interview?

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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