Accenture Machine Learning Engineer Interview Questions + Guide in 2024
Overview
Accenture is a global professional services company with leading capabilities in digital, cloud, and security. With a strong emphasis on technological innovation and a commitment to societal impact, Accenture creates value for its clients, employees, and communities.
As a Machine Learning Engineer at Accenture Federal Services, you will collaborate with a multidisciplinary team to develop cutting-edge solutions for complex national security challenges. The role demands proficiency in programming languages such as Python, R, or Java and a solid grounding in machine learning and statistical methods. You’ll engage in the modeling process, from problem definition to deployment, and work on diverse, impactful projects.
This guide from Interview Query will walk you through the interview process, commonly asked Accenture machine learning engineer interview questions, and useful tips to help you prepare effectively. Let’s get you ready for your next big opportunity!
Accenture Machine Learning Engineer Interview Process
The interview process usually depends on the role and seniority; however, you can expect the following on an Accenture Machine Learning Engineer interview:
Recruiter/Hiring Manager Call Screening
If your CV gets shortlisted, a recruiter from the Accenture Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.
The whole recruiter call typically takes about 30 minutes, during which they might also provide preliminary information about the role and the company.
Technical Virtual Interview
Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the Accenture Machine Learning Engineer role usually is conducted through virtual means, including video conferences and screen sharing. In this 1-hour long interview stage, questions may revolve around your understanding of machine learning models like XGBoost, NLP models, classification models, and unsupervised learning models.
The interviewer may also discuss topics like predictive analysis, the optimization of models, and project-related questions. You should also be prepared to explain technical details from your past projects.
Online Test
An online test is also part of the interview process. This test will assess your knowledge on topics such as bias, variance, overfitting, and underfitting. Additionally, you may be asked to write pseudo-code for given problems using any framework you are comfortable with.
Onsite Interview Rounds
Followed by the recruiter calls and technical virtual interviews, you’ll be invited to attend the onsite interview loop. Multiple interview rounds will be conducted during your visit to the Accenture office, focusing on your technical prowess, including programming and ML modeling capabilities.
If you were assigned take-home exercises, a presentation round might be included as part of the onsite interview.
What Questions Are Asked in an Accenture Machine Learning Engineer Interview?
Typically, interviews at Accenture vary by role and team, but commonly, Machine Learning Engineer interviews follow a fairly standardized process across these question topics.
1. What are the drawbacks of the given data layouts, and how would you reformat them for better analysis?
Assume you have data on student test scores in two different layouts (dataset 1 and dataset 2). Identify the drawbacks of these layouts and suggest formatting changes to make the data more useful for analysis. Additionally, describe common problems seen in “messy” datasets.
2. Write a function to merge two sorted lists into one sorted list.
Given two sorted lists, write a function to merge them into one sorted list. Bonus: What’s the time complexity?
3. Create a function one_element_removed to find the missing integer between two nearly identical lists.
There are two lists, list X and list Y. Both lists contain integers from -1000 to 1000 and are identical to each other except that one integer is removed in list Y that exists in list X. Write a function one_element_removed 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.
4. Write a function sorting to sort a list of strings in ascending alphabetical order from scratch.
Given a list of strings, write a function, sorting to sort the list in ascending alphabetical order. Do NOT use the built-in sorted function. Return the new sorted list, rather than modifying the list in place. Bonus: Have your solution be (O(n \log n)).
5. How would you justify the complexity of a neural network model and explain predictions to non-technical stakeholders?
Your manager asks you to build a neural network model to solve a business problem. How would you justify the complexity of this model and explain its predictions to non-technical stakeholders?
6. How would you build a fraud detection model with a text messaging service for transaction approval?
You work at a bank that wants to detect fraud and implement a text messaging service to approve or deny transactions. How would you build this model?
7. How would you encode a categorical variable with thousands of distinct values?
You have a categorical variable with thousands of distinct values. How would you encode it?
8. What is the difference between XGBoost and random forest algorithms, and when would you use one over the other?
Explain the difference between XGBoost and random forest algorithms. Provide an example of when you would use one over the other.
9. What is the probability that it’s actually raining in Seattle?
You are about to get on a plane to Seattle and want to know if you should bring an umbrella. You call 3 random friends who live there, each with a 2⁄3 chance of telling the truth and a 1⁄3 chance of lying. All 3 friends say “Yes” it is raining. Calculate the probability that it is raining.
10. What is the mean and variance of the distribution of (2X - Y)?
Given that (X) and (Y) are independent variables with normal distributions (X \sim N(3, 2^2)) and (Y \sim N(1, 2^2)), determine the mean and variance of the distribution of (2X - Y).
How to Prepare for a Machine Learning Engineer Interview at Accenture
To help you succeed in your Accenture machine learning engineer interviews, consider these tips based on interview experiences:
Understand Core Machine Learning Concepts: Be prepared to discuss bias, variance, overfitting, and underfitting, and have a solid understanding of different machine learning algorithms, especially XGBoost and NLP models.
Be Thorough with Project Details: Expect questions about your past projects. Be ready to explain the technical specifics, the challenges you faced, and how you overcame them.
Brush Up on Coding Skills: Practice coding problems and writing pseudo-code for machine learning algorithms. Your coding proficiency may be assessed, so make sure to prepare using platforms like Interview Query.
FAQs
What is the average salary for a Machine Learning Engineer at Accenture?
Average Base Salary
Average Total Compensation
What skills and qualifications are needed for a Machine Learning Engineer position at Accenture?
Essential qualifications include experience or coursework in programming (Python, R, Java), machine learning, and statistics. Strong written and verbal communication skills, the ability to work independently and collaboratively, and a high level of curiosity and drive to solve hard problems are also valuable.
What is the work culture like at Accenture?
Accenture has a strong commitment to diversity and inclusion. The company fosters a culture where everyone feels a sense of belonging and is encouraged to bring innovative, creative solutions to their projects. The environment is collaborative, allowing for impactful contributions on multiple projects concurrently.
Conclusion
The interview process for a Machine Learning Engineer position at Accenture Federal Services is a comprehensive journey that thoroughly evaluates your technical, analytical, and problem-solving skills.
If you want more insights about the company, check out our main Accenture Interview Guide, where we have covered many interview questions you might encounter. We’ve also created interview guides for other roles, such as data scientist and software engineer, where you can learn more about Accenture’s interview processes for different positions.
You can also check out all our company interview guides for better preparation. Good luck with your interview!