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The Interview Process
Coding & SQL Test
Testing Python, PySpark, and advanced SQL. Focus is on handling massive telecommunications data (e.g., billions of mobile location pings per day).
Machine Learning Case Presentation
You must present a model leveraging KT's unique telecom data (e.g., predicting real-estate trends based on mobile foot traffic, or building an LLM for KT's AICC/Call Centers).
Cross-Functional Panel
Assessing your ability to break down data silos within a massive, deeply departmentalized organization.
Real KT Corporation Interview Questions
Practice these exact questions faced by previous AI / Big Data Engineer (Digico Unit) candidates.
1KT possesses massive amounts of mobile location data (Call Detail Records). Walk me through how you would build a data pipeline using PySpark to aggregate this data in real-time to predict traffic congestion in Seoul, while strictly adhering to Korean privacy laws (PIPA). (Data Engineering / Privacy)
2(Value: Digico Transformation) KT is building an AI Contact Center (AICC). Describe how you would utilize Speech-to-Text (STT) and Large Language Models (LLMs) to automatically classify customer complaints and route them to the correct department with higher accuracy than legacy IVR systems. (Applied AI)
3You are tasked with building a churn prediction model for KT's mobile subscribers. What specific features (e.g., data usage drops, call center complaints, competitor pricing) would you engineer, and how do you handle the severe class imbalance in the training data? (Machine Learning)
4Write a SQL query to identify 'Super Users' across multiple KT verticals (e.g., users who have KT Mobile, KT Internet, AND use KT IPTV heavily), calculating the combined monthly ARPU (Average Revenue Per User). (Advanced SQL)
5You want to utilize a powerful new open-source ML algorithm, but the conservative IT security team blocks the required Python libraries from being installed on the training clusters. How do you negotiate a solution? (Matrix Navigation / Collaboration)
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