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Learning Technology Specialist Interview Questions

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14,618 learning technology specialist interview questions shared by candidates

1. C++ inheritance question + Sparse matmul, 2. A consumer producer scenario where you had to figure out a bottleneck in the design. 3. What is the output of a C++ code (accessing memory) 4. Implementation of an LRU cache in C++ 5. Matrix transposition plus some approximate computation 6. Implement matmul with smaller fixed size SIMD kernel (2x2)
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Machine Learning Architecture

Interviewed at Meta

3.5★
Feb 18, 2024

1. C++ inheritance question + Sparse matmul, 2. A consumer producer scenario where you had to figure out a bottleneck in the design. 3. What is the output of a C++ code (accessing memory) 4. Implementation of an LRU cache in C++ 5. Matrix transposition plus some approximate computation 6. Implement matmul with smaller fixed size SIMD kernel (2x2)

Had a couple of interviews. 3 coding rounds, one deep dive into deep learning, and ML design interview. The coding round interview questions were leetcode medium and hard questions. The interviewers were helpful and guided me so I was able to do them within reasonable. Be mindful of edge cases. ML design question is open ended, hard to prepare for. You are asked to design a predictive system and are told to focus on the machine learning side of things. Make sure to gather requirements (scale, scope, metrics to improve) before diving in. Talk about data collection, think what kind of a data a company like Meta collects and how you could label it and use it, what other data sources could you use, how would you go about collecting them and how would you label them. Be prepared to talk in detail about the ML model you think you should be using and defend the choice if needed.
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Machine Learning Engineer

Interviewed at Meta

3.5★
Oct 4, 2022

Had a couple of interviews. 3 coding rounds, one deep dive into deep learning, and ML design interview. The coding round interview questions were leetcode medium and hard questions. The interviewers were helpful and guided me so I was able to do them within reasonable. Be mindful of edge cases. ML design question is open ended, hard to prepare for. You are asked to design a predictive system and are told to focus on the machine learning side of things. Make sure to gather requirements (scale, scope, metrics to improve) before diving in. Talk about data collection, think what kind of a data a company like Meta collects and how you could label it and use it, what other data sources could you use, how would you go about collecting them and how would you label them. Be prepared to talk in detail about the ML model you think you should be using and defend the choice if needed.

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