Prepare for and practice interview questions from Akamai across topics like Algorithms, SQL, Python and more.

Akamai Interview Questions

Akamai Interview Guides

Data Structures & Algorithms

Given two sorted lists, write a function to merge them into one sorted list.

Merge Sorted Lists

Write a function to create a single dataframe with complete addresses in the format of street, city, state, zip code.

Complete Addresses

Given an array and a target integer, write a function that returns the indices of two integers in the array that add up to the target integer.

Target Indices

The task is to write a function that takes an N-dimensional array (nested lists) as input and returns a 1D array. The N-dimensional array can have any number of nested lists and each nested list can contain any number of elements.

Flatten N-Dimensional Array to 1D Array

Given a list of strings, write a function that returns the longest common prefix

Common Prefix

Machine Learning

Design a function to detect anomalies in univariate and bivariate data

Variate Anomalies

Addressing imbalanced data in machine learning through carefully prepared techniques.

Data Preparation for Imbalanced Data

How would you decide between composition and inheritance when modeling different seller types on eBay?

Seller Type Modeling

Data Pipelines

Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners.

Skyscanner Partner ETL

Design a secure, real-time, scalable portfolio management system with notifications and caching.

Portfolio Platform Architecture

Design a scalable event data pipeline for real-time, multi-application ingestion and processing.

Centralized Event Ingestion

Behavioral

Describing a data project and its challenges

Hurdles In Data Projects

How would you negotiate and resolve disagreements when a client rejects your proposed solution?

Client Solution Pushback

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

Your Strengths and Weaknesses

Analytics

What kind of analysis would you conduct to recommend changes to the UI?

User Journey Analysis

You notice that the credit card payment amount per transaction has decreased. How would you investigate what happened?

Decreasing Payments

Given two sorted lists, write a function to merge them into one sorted list.

Bonus: What’s the time complexity?

Example:

Input:

<pre tabindex="0" class="chroma"><code>list1 = [1,2,5]
list2 = [2,4,6]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def merge_list(list1,list2) -&gt; [1,2,2,4,5,6]
</code></pre>

You have two sorted lists, one with m elements and one with n elements. What would be the time complexity of merging the two lists via merge sort?

You have two sorted lists, one with m elements and one with n elements. What would be the time complexity of merging the two lists via merge sort? 

Merging Lists Complexity

You work as a data engineer at Skyscanner. Your team is responsible for ingesting, transforming, and loading flight and hotel availability data from dozens of external partner APIs into Skyscanner’s real-time search platform. The system must ensure high data freshness, fault tolerance, and scalability as new partners are onboarded.

How would you architect the ETL pipeline to support these requirements? Design a system flow explaining this architecture.

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?

Given an array and a target integer, write a function <code>sum_pair_indices</code> that returns the indices of two integers in the array that add up to the target integer. If not found, just return an empty list.

Note: Can you do it on \(O(n)\) time?

Note: Even though there could be many solutions, only one needs to be returned.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>array = [1 2 3 4] 
target = 5 
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def sum_pair_indices(array, target) -&gt; [0 3] or [1 2]
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>array = [3]
target = 6 
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>Do NOT return [0 0] as you can&#39;t use an index twice.
</code></pre>

You work as a software engineer at Morningstar. You are tasked with architecting a web-based portfolio management platform where authenticated users can view their investment portfolios, including stocks, mutual funds, and other assets. The system must display real-time asset prices, send notifications for significant price drops or new recommendations from external providers, and handle large volumes of user traffic with scalable caching.

How would you architect this system?

How would you handle the data preparation for building a machine learning model using imbalanced data?

Let’s say we’re building a system for eBay to support multiple types of sellers: individuals, small businesses, and enterprise merchants. Each seller type shares core attributes like <code>seller_id</code> and <code>account_status</code>, but has unique features—enterprise merchants, for example, can manage up to 50 sub-accounts, while individuals have simpler profiles.

How would you decide between using composition or inheritance to model these seller types, and what trade-offs would you weigh in making your choice?

Find all sets of 3 indexes of an array so that the three indexed elements add up to 0.

Note: You may assume the input array is sorted in ascending order.

<h3>Example</h3>

For example, if the input array is:

<pre tabindex="0" class="chroma"><code>array = [-2, -1, 1, 1, 3]
</code></pre>

The result should be:

<pre tabindex="0" class="chroma"><code>result = [{0,1,2}, {2,3,4}]
</code></pre>

Find all sets of 3 indexes whose elements add up to 0.

Three Indexes Adding Zero

If given a univariate dataset, how would you design a function to detect anomalies?

What if the data is bivariate?

Let’s say we’re managing a database for a video-sharing platform where each video is stored as a Blob, and our dataset now exceeds 10 million videos.

How would you configure the database and set up indexing so that queries for video metadata (like title, upload date, or uploader) remain efficient, while also handling the storage and retrieval of large Blob data?

How would you design database indexing for efficient metadata queries when storing large Blobs?

Blob Indexing

Query Optimization

You have been tasked with designing three classes: <code>text_editor</code>, <code>moving_text_editor</code>, and <code>smart_text_editor</code>. These classes are to be created with specific functionalities as defined below:

<ol>
<li><code>text_editor</code> class:

<ul>
<li><code>write_line(string:str)</code>: A method which appends a given string to the end of the existing string.</li>
<li><code>delete_line(char_num : int)</code>: A method which deletes <code>char_num</code> number of characters from the existing string, starting from the end. If there are no characters left, the method should do nothing.</li>
<li><code>special_operation()</code>: A method which currently does nothing.</li>
<li><code>get_notes()</code>: A method which returns the internal string.</li>
</ul></li>

<li><code>moving_text_editor</code> class:

<ul>
<li>This class extends <code>text_editor</code>. The <code>special_operation()</code> method is overridden. Initially, the cursor will be at the end of the current string. If <code>special_operation()</code> is called, it moves the cursor to the beginning of the string, any additional appends will be appended to the beginning of the string instead. Calling <code>special_operation()</code> again reverses the cursor operation.</li>
</ul></li>

<li><code>smart_text_editor</code> class:

<ul>
<li>This class extends <code>text_editor</code>. In this class, the <code>special_operation()</code> method is overridden to serve as an undo operation, allowing it to undo an infinite number of operations.</li>
</ul></li>
</ol>

<h3>Input</h3>

<pre tabindex="0" class="chroma"><code>[[&#39;special_text_editor&#39;], [&#39;write_line&#39;, &#39;special_operation&#39;, &#39;write_line&#39;], [&#39;World&#39;, &#39;Hello, &#39;]]
</code></pre>

<h3>Output</h3>

<code>&#34;Hello, World&#34;</code>

Create a text editor while displaying method overriding as a concept.

Text Editor With OOP

Design Patterns

You have access to a set of tables summarizing user event data for a community forum app. You’re asked to conduct a user journey analysis using this data with the eventual goal of improving the user interface.

What kind of analysis would you conduct to recommend changes to the UI?

Create a class named <code>LRUCache</code> that implements the behavior expected of a Least Recently Used (LRU) Cache.

The <code>LRUCache</code> class implements the following methods:

<ul>
<li><code>__init__(self, capacity)</code> - initializes the object and takes in a capacity parameter specifying the maximum capacity of the cache.</li>
<li><code>get_if_exists(self, key) -&gt; Any|None</code> - gets a value based on a key. If the key exists, returns the associated value. If the key has expired or did not exist in the first place, returns <code>None</code>.</li>
<li><code>put(self, key, value)</code> - places a value inside the cache. In the case wherein the cache is full, invalidates the least recently used element. When two keys collide, the older value should be invalidated in place of the new one.</li>
</ul>

Example:

<pre tabindex="0" class="chroma"><code>cache = LRUCache(2)
cache.put(&#34;sample&#34;, 55)
cache.get_if_exists(&#34;sample&#34;) # returns 55
cache.get_if_exists(&#34;key&#34;) # returns None
cache.put(&#34;hello&#34;, 10) 
cache.put(&#34;sample&#34;, 9)
cache.put(&#34;world&#34;, 5)
cache.get_if_exists(&#34;hello&#34;) # returns None
</code></pre>

Notes:

<ul>
<li>It is assured that the value in the <code>put</code> method will never receive a <code>None</code> value. Moreover, it is assured that the capacity will always be &gt; 0.</li>

<li>All operations must be O(1).</li>

<li>Aside from Python’s standard dictionary implementation (<code>{}</code>), you may not use any other built-in data structures.</li>
</ul>

LRU Cache 1

Write a Python function called <code>longest_increasing_subsequence</code> that takes a list of integers as input and returns as output the length of the longest increasing subsequence in the list.

Note: An increasing subsequence is a subset of a list where the elements are in strictly increasing order. The subsequence does not have to be contiguous or unique.

<h3>Example:</h3>

Input:

<pre tabindex="0" class="chroma"><code>nums = [10, 9, 2, 5, 3, 3, 7, 101, 18]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>4
</code></pre>

Explanation:

The longest increasing subsequence is <code>[2, 3, 7, 101]</code>, for a total length of 4.

Find the longest increasing subsequence in a list of integers.

Longest Increasing Subsequence

Given a dictionary with weights, write a function <code>random_key</code> that returns a key at random with a probability proportional to the weights.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>weights = {&#39;A&#39;: 1, &#39;B&#39;: 2}
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>random_key(weights) -&gt; return A 1/3 of the time, B 2/3 of the time
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>weights = {&#39;A&#39;: 1, &#39;B&#39;: 1}
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>random_key(weights) -&gt; return A 1/2 of the time, B 1/2 of the time
</code></pre>

Weighted Keys

Probability

You’re given two dataframes. One contains information about addresses and the other contains relationships between various cities and states:

Example:

<code>df_addresses</code>

<table>
<thead>
<tr>
<th>address</th>
</tr>
</thead>

<tbody>
<tr>
<td>4860 Sunset Boulevard, San Francisco, 94105</td>
</tr>

<tr>
<td>3055 Paradise Lane, Salt Lake City, 84103</td>
</tr>

<tr>
<td>682 Main Street, Detroit, 48204</td>
</tr>

<tr>
<td>9001 Cascade Road, Kansas City, 64102</td>
</tr>

<tr>
<td>5853 Leon Street, Tampa, 33605</td>
</tr>
</tbody>
</table>
<code>df_cities</code>

<table>
<thead>
<tr>
<th>city</th>
<th>state</th>
</tr>
</thead>

<tbody>
<tr>
<td>Salt Lake City</td>
<td>Utah</td>
</tr>

<tr>
<td>Kansas City</td>
<td>Missouri</td>
</tr>

<tr>
<td>Detroit</td>
<td>Michigan</td>
</tr>

<tr>
<td>Tampa</td>
<td>Florida</td>
</tr>

<tr>
<td>San Francisco</td>
<td>California</td>
</tr>
</tbody>
</table>
Write a function <code>complete_address</code> to create a single dataframe with complete addresses in the format of street, city, state, zip code.

Input:

<pre tabindex="0" class="chroma"><code>import pandas as pd

addresses = {&#34;address&#34;: [&#34;4860 Sunset Boulevard, San Francisco, 94105&#34;, &#34;3055 Paradise Lane, Salt Lake City, 84103&#34;, &#34;682 Main Street, Detroit, 48204&#34;, &#34;9001 Cascade Road, Kansas City, 64102&#34;, &#34;5853 Leon Street, Tampa, 33605&#34;]}

cities = {&#34;city&#34;: [&#34;Salt Lake City&#34;, &#34;Kansas City&#34;, &#34;Detroit&#34;, &#34;Tampa&#34;, &#34;San Francisco&#34;], &#34;state&#34;: [&#34;Utah&#34;, &#34;Missouri&#34;, &#34;Michigan&#34;, &#34;Florida&#34;, &#34;California&#34;]}

df_addresses = pd.DataFrame(addresses)
df_cities = pd.DataFrame(cities)
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def complete_address(df_addresses,df_cities) -&gt;
</code></pre>

<table>
<thead>
<tr>
<th>address</th>
</tr>
</thead>

<tbody>
<tr>
<td>4860 Sunset Boulevard, San Francisco, California, 94105</td>
</tr>

<tr>
<td>3055 Paradise Lane, Salt Lake City, Utah, 84103</td>
</tr>

<tr>
<td>682 Main Street, Detroit, Michigan, 48204</td>
</tr>

<tr>
<td>9001 Cascade Road, Kansas City, Missouri, 64102</td>
</tr>

<tr>
<td>5853 Leon Street, Tampa, Florida, 33605</td>
</tr>
</tbody>
</table>

You are given an N-dimensional array (a nested list) and your task is to convert it into a 1D array. The N-dimensional array can have any number of nested lists and each nested list can contain any number of elements. The elements in the nested lists are integers. Write a function that takes an N-dimensional array as input and returns a 1D array.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>array = [1, [2, 3], [4, [5, 6]], 7]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>flatten_array(array) -&gt; [1, 2, 3, 4, 5, 6, 7]
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>array = [[1, 2], [3, 4], [5, 6]]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>flatten_array(array) -&gt; [1, 2, 3, 4, 5, 6]
</code></pre>

Let’s say that you work on the revenue forecasting team at a company like Facebook.

An executive comes to you asking about how much revenue Facebook will make in the coming year.

How would you forecast revenue for the next year?

How would you forecast revenue for the next year

Forecasting New Year Revenue

Forecasting & Time Series

Given a table of <code>flights</code>, extract the 2nd flight with the most duration between each pair of cities. Order the flights by the flight <code>id</code> ascending.

Note: For any cities <code>X</code> and <code>Y</code>, <code>(source_location=X, destination_location=Y)</code> and <code>(source_location=Y, destination_location=X)</code> are counted as the same pair of cities.

Note: If there are fewer than two flights between two cities, there is no 2nd longest flight.

Example:

Input:

<code>flights</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>destination_location</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>source_location</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>plane_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>flight_start</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>flight_end</code></td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
Output:

<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>destination_location</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>source_location</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>flight_start</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>flight_end</code></td>
<td>DATETIME</td>
</tr>
</tbody>
</table>

Find the second longest flight between each pair of cities.

Second Longest Flight

Blizzard Entertainment operates a global network of games and services, each generating massive volumes of real-time gameplay and infrastructure events, everything from player actions in Overwatch to server status updates for World of Warcraft. You are tasked with building a centralized system to reliably ingest, process, and distribute these events across the company for analytics, live operations, and player-facing features.

How would you architect the data ingestion and processing pipeline for this event system?

Let’s say we have an <code>annual_payments</code> table. Answer the following three questions via SQL queries and output them as a table with the answers to each question.

<ol>
<li>How many total transactions are in this table?</li>
<li>How many different users made transactions?</li>
<li>How many transactions listed as <code>&#34;paid&#34;</code> have an amount greater or equal to 100?</li>
<li>Which product made the highest revenue? (use only transactions with a <code>&#34;paid&#34;</code> status)</li>
</ol>

Example:

Input:

<code>annual_payments</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>amount</code></td>
<td>FLOAT</td>
</tr>

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

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

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

<tr>
<td><code>amount_refunded</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>product</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>last_updated</code></td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
Output:

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

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

<tr>
<td><code>answer</code></td>
<td>TEXT</td>
</tr>
</tbody>
</table>

Write a SQL query to count transactions filtered by several criterias.

Count Transactions

Given a list of strings, write a function that returns their longest common prefix. For example, if you were given the strings <code>&#34;flowers&#34;</code>, <code>&#34;flow&#34;</code>, and <code>&#34;flight&#34;</code>, your function should return the string <code>&#34;fl&#34;</code>.

If the list of strings has no common prefix, return an empty string.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>strings = [&#34;flowers&#34;, &#34;flow&#34;, &#34;flight&#34;] 
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>&#34;fl&#34;
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>strings = [&#34;showboat&#34;, &#34;showcase&#34;, &#34;shower&#34;]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>&#34;show&#34;
</code></pre>

Let’s say we manage a cloud infrastructure platform, and over the last 6 months, we’ve noticed that 20% of our compute nodes consistently sit idle while others run near full capacity.

How would you analyze and address this stranded capacity to improve overall resource utilization?

How would you investigate and resolve persistently underutilized resources in a cloud platform?

Stranded Capacity

Supply Chain

Let’s say we’re launching a new smart fitness tracker that targets both casual users and dedicated athletes, and we want to understand the opportunity before going to market.

How would you estimate the potential market size, segment the customer base, identify main competitor types, highlight likely roadblocks, outline your high-level marketing approach, and walk through one specific marketing campaign you’d propose?

How would you approach sizing the market, segmenting users, identifying competitors, and building a marketing plan for a new smart fitness tracker?

Smart Fitness Tracker Launch

Product Sense & Metrics

Let’s say we’ve proposed a new analytics dashboard to a client, but after reviewing our design and data sources, the client says the solution doesn’t meet their needs and pushes back on several key features.

How would you approach the conversation to negotiate alternative solutions, and what steps would you take to ensure disagreements are handled constructively so we can still reach a successful outcome?

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?

Your Strengths and Weaknesses II

Let’s say you work for a financial company. You notice that the credit card payment amount per transaction has decreased.

How would you investigate what happened?

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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