DXC Technology
American multinational B2B IT services provider.
4 Rounds
~21 Days
Medium
The Interview Loop
Recruiter Screen (30 min)
Standard fit check, behavioral questions, and resume overview.
Technical Loop (3-4 Rounds)
Deep dive into domain knowledge, coding, and system design.
Interview Question Bank
Machine Learning Engineer
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Behavioral
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medium
Tell me about a time you had to explain a complex machine learning model to a non-technical client or stakeholder.
#Client Facing
#Stakeholder Management
#Explainable AI
Machine Learning Engineer
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Behavioral
•
medium
Describe a situation where your machine learning model performed well in training but failed or underperformed in production. How did you resolve it?
#Troubleshooting
#Production Issues
#Lessons Learned
Machine Learning Engineer
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Behavioral
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easy
How do you prioritize tasks when working on multiple client deliverables with tight, overlapping deadlines?
#Prioritization
#Consulting
#Agile
Machine Learning Engineer
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Behavioral
•
medium
Tell me about a time you disagreed with a senior engineer or architect about a technical approach. How did you handle it?
#Conflict Resolution
#Teamwork
#Professionalism
Machine Learning Engineer
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Behavioral
•
medium
Describe a project where you had to quickly learn a new ML framework or cloud technology to meet client needs.
#Continuous Learning
#Cloud Platforms
#Consulting
Machine Learning Engineer
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Behavioral
•
medium
How do you handle scope creep when a client keeps asking for additional features or metrics in an ongoing ML pipeline project?
#Client Management
#Scope Management
#Agile
Machine Learning Engineer
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Behavioral
•
medium
Tell me about a time you identified a bottleneck in an existing data or ML pipeline and optimized it.
#Optimization
#Performance Tuning
#Initiative
Machine Learning Engineer
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Behavioral
•
medium
Describe your experience working in an Agile environment. How do you estimate time for ML research tasks, which are inherently uncertain?
#Agile
#Estimation
#Research
Machine Learning Engineer
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Behavioral
•
medium
Tell me about a time you had to work with messy, undocumented legacy data to build a predictive model.
#Data Cleaning
#Legacy Systems
#Resilience
Machine Learning Engineer
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Behavioral
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easy
Why do you want to work at DXC Technology, and how does your ML experience align with our enterprise IT and consulting focus?
#Company Knowledge
#Motivation
#Consulting
Machine Learning Engineer
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Coding
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easy
Write a Python function to merge two sorted arrays into a single sorted array without using built-in sorting functions.
#Python
#Arrays
#Two Pointers
Machine Learning Engineer
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Coding
•
medium
Given a list of customer transaction amounts, write a Python script to find the top K most frequent transaction values.
#Python
#Hash Maps
#Heaps
Machine Learning Engineer
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Coding
•
medium
Write a SQL query to calculate the 7-day rolling average of model inference latency times from a logs table.
#SQL
#Window Functions
#Time Series
Machine Learning Engineer
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Coding
•
medium
Write a SQL query to identify clients who have a churn probability greater than 0.8 and have not logged in for the past 30 days, joining a predictions table and a user_activity table.
#SQL
#Joins
#Filtering
Machine Learning Engineer
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Coding
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hard
Implement a basic K-Means clustering algorithm from scratch in Python using NumPy.
#Python
#NumPy
#Machine Learning Algorithms
Machine Learning Engineer
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Coding
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hard
Write a Python function to calculate the ROC-AUC score given an array of true labels and an array of predicted probabilities, without using scikit-learn.
#Python
#Metrics
#Machine Learning Algorithms
Machine Learning Engineer
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Coding
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easy
Write a Python function using pandas to impute missing values in a dataset: numerical columns with the median and categorical columns with the mode.
#Python
#Pandas
#Data Cleaning
Machine Learning Engineer
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Coding
•
medium
Write a Python script to perform a grid search over hyperparameters for an XGBoost model, utilizing cross-validation.
#Python
#XGBoost
#Hyperparameter Tuning
#Cross-Validation
Machine Learning Engineer
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System Design
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hard
Design a machine learning system to predict server and hardware failures in a large enterprise data center (Predictive Maintenance).
#Predictive Maintenance
#IoT Data
#Streaming Architecture
Machine Learning Engineer
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System Design
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medium
How would you deploy a machine learning model as a highly available REST API using Docker and Kubernetes?
#Docker
#Kubernetes
#Model Deployment
#API
Machine Learning Engineer
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System Design
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hard
Design a real-time fraud detection system for a banking client processing thousands of transactions per second.
#Fraud Detection
#Real-time Processing
#Scalability
Machine Learning Engineer
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System Design
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medium
Explain your approach to monitoring model drift in a production environment. How do you decide when to trigger retraining?
#Model Monitoring
#Data Drift
#Concept Drift
Machine Learning Engineer
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System Design
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hard
Design an enterprise document search system using Large Language Models (LLMs) and Vector Databases (RAG architecture).
#LLMs
#RAG
#Vector Databases
#NLP
Machine Learning Engineer
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System Design
•
medium
How do you handle A/B testing for a newly deployed recommendation model on an e-commerce platform?
#A/B Testing
#Recommendation Systems
#Experimentation
Machine Learning Engineer
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System Design
•
medium
Design a batch processing pipeline using PySpark to score millions of customer records nightly for a marketing campaign.
#PySpark
#Batch Processing
#Big Data
Machine Learning Engineer
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System Design
•
medium
How do you ensure data privacy and compliance (like GDPR or HIPAA) when training machine learning models on sensitive client data?
#Data Privacy
#GDPR
#Data Anonymization
Machine Learning Engineer
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Technical
•
medium
Explain the bias-variance tradeoff. How does it apply to Random Forests compared to single Decision Trees?
#Ensemble Methods
#Model Evaluation
#Theory
Machine Learning Engineer
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Technical
•
medium
How do you handle highly imbalanced datasets, such as those found in enterprise fraud detection?
#Data Imbalance
#Fraud Detection
#Metrics
Machine Learning Engineer
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Technical
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hard
Explain how Gradient Boosting works. What are the key differences between XGBoost and LightGBM?
#Boosting
#XGBoost
#LightGBM
Machine Learning Engineer
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Technical
•
medium
What are the core assumptions of linear regression, and how do you validate them before deploying a model?
#Linear Regression
#Statistics
Machine Learning Engineer
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Technical
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easy
How does L1 (Lasso) regularization differ from L2 (Ridge) regularization mathematically and practically?
#Regularization
#Feature Selection
Machine Learning Engineer
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Technical
•
hard
Explain the architecture of a Transformer model. Why has it largely replaced LSTMs for enterprise NLP tasks?
#NLP
#Transformers
#Attention Mechanism
Machine Learning Engineer
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Technical
•
medium
How do you evaluate the performance of an unsupervised learning model like K-Means clustering?
#Unsupervised Learning
#Clustering
#Metrics
Machine Learning Engineer
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Technical
•
medium
What is data leakage in machine learning? Give an example of how it might occur during feature engineering and how to prevent it.
#Data Leakage
#Feature Engineering
#Best Practices
Machine Learning Engineer
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Technical
•
easy
Explain the difference between bagging and boosting ensemble methods.
#Ensemble Methods
#Bagging
#Boosting
Difficulty Radar
Based on recent AI-sourced data.
Meet Your Interviewers
The "Standard" Interviewer
Senior EngineerFocuses on core competencies, system constraints, and clear communication.
SimulateUnwritten Rules
Think Out Loud
Always explain your thought process before writing code or drawing architecture.