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3,051 ai developer technology engineer interview questions shared by candidates

Technical Interview: How do CNNs work ? How would they perform on 1D data ? What is a progressive web app ? How do LLMs work ? What do you know about RAG? Cultural Fit: Tell us about your last experience ? What type of environment do you thrive in ? What expectations do you have for your project manager and for the company's management team? Tell us about a time you had a conflict with a co-worker ?

AI Backend Developer

Interviewed at Explorai

4.8★
Feb 19, 2025

Technical Interview: How do CNNs work ? How would they perform on 1D data ? What is a progressive web app ? How do LLMs work ? What do you know about RAG? Cultural Fit: Tell us about your last experience ? What type of environment do you thrive in ? What expectations do you have for your project manager and for the company's management team? Tell us about a time you had a conflict with a co-worker ?

Question: Moving Average Anomaly Detection Problem: You are given a time series data of daily stock prices in the form of an array prices, where each prices[i] represents the stock price on day i. Implement a function that detects anomalies using a simple moving average (SMA) approach. An anomaly is detected if the stock price deviates from the moving average by more than a certain threshold. Given the list prices, an integer window_size, and a threshold percentage, return a list of days (indices) where anomalies are detected. Input: prices: List of integers representing stock prices over time. window_size: Integer representing the number of days to compute the moving average. threshold: Float representing the percentage deviation from the moving average to be considered an anomaly. Output: A list of indices where anomalies are detected.
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AI/ML Engineer

Interviewed at Google

4.4★
Nov 4, 2024

Question: Moving Average Anomaly Detection Problem: You are given a time series data of daily stock prices in the form of an array prices, where each prices[i] represents the stock price on day i. Implement a function that detects anomalies using a simple moving average (SMA) approach. An anomaly is detected if the stock price deviates from the moving average by more than a certain threshold. Given the list prices, an integer window_size, and a threshold percentage, return a list of days (indices) where anomalies are detected. Input: prices: List of integers representing stock prices over time. window_size: Integer representing the number of days to compute the moving average. threshold: Float representing the percentage deviation from the moving average to be considered an anomaly. Output: A list of indices where anomalies are detected.

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