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AllCloud Interview Questions

A/B Testing

Algorithms

Analytics

Machine Learning

Probability

Product Metrics

Python

Statistics

AllCloud Interview Guides

Let’s say we’ve trained a machine learning model on SageMaker and now want to deploy it as an API endpoint that can handle up to 100 requests per second with low latency.

How would you design the end-to-end deployment architecture using AWS services, and what considerations would you have for scalability and monitoring?

How would you architect and monitor a scalable ML model deployment on AWS?

SageMaker Deployment Architecture

ML Ops & Training Pipelines

Suppose there exists a new airline named Jetco that flies domestically across North America. Jetco recently had a study commissioned that tested the boarding time of every airline, and it came out that Jetco had the fastest average boarding times of any airline.

What factors could have biased this result, and what would you look into?

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

Boarding Times Bias

A new airline came out as the fastest average boarding times compared to other airlines.  What factors could have biased this result and what would you look into?

You are testing hundreds of hypotheses with many t-tests. What considerations should be made?

What is a key disadvantage of the F-test when compared to multiple t-tests?

What is a key disadvantage of the F-test when compared to multiple t-tests?

Hundreds of Hypotheses

You are testing hundreds of hypotheses with many t-tests. What considerations should be made?

Question	Topic	Difficulty	Ask Chance
SageMaker Deployment Architecture	ML Ops & Training Pipelines	Medium	Very High
Boarding Times Bias	Statistics	Medium	Low
Hundreds of Hypotheses	Statistics	Medium	Low