Top 10 Frequently Asked Questions in Alphabet’s AI Interviews

Tech aspirants wish to ace AI interviews at Alphabet to kick-start their career growth

Google and its parent company, Alphabet, have been pioneers of some of the most advanced AI innovations. Google has time and again explained elaborately the different ways in which it is applying AI and machine learning to improve the Google search experience. Google users can see how busy roads or places are in Google Maps and search grocery stores, specific beaches, pharmacies, or other locations. Google's business metrics also enable companies to add information to their profiles. Also, Alphabet's recent AI initiatives have presented the world with an AI-based earthquake detection system that can easily detect waves generated by the waves. All these innovations have talented individuals behind them, and Alphabet is known to be the home of tech pioneers. Alphabet's reputation precedes all others, attracting an influx of new tech aspirants every day. If you are a Google aspirant, this space is yours. In this article, we have listed out the top and most frequently asked questions in Alphabet's AI interviews, that will prep you for the interview.

What is ANN?

An artificial Neural network is a computational model on the structure of the biological neural network (BNN). There are three layers of ANN, namely, the input layer, the hidden layer, and the output layer.

What do you understand by A/B testing in machine learning?

It is a statistical way of comparing two or more techniques, typically an incumbent against a new rival. A/B testing aims to determine not only which technique performs better but also to understand whether the difference is statistically significant. It usually considers only two techniques using one measurement, but it can be applied to a finite number of techniques.

What is the activation function in ML?

It is a function that takes in the weighted sum of all of the inputs from the previous layer and generates an output value and passes it to the next layer. While comparing with a neuron-based model, the activation function stays at the end deciding what to be fired at the next neuron.

Explain Alpha-Beta Pruning.

Alpha-Beta Pruning is a search algorithm that tries to reduce the number of nodes that are searched by the minimax algorithm in the search tree. It can be applied to 'n' depths and can prune the entire subtrees and leaves.

How route weights are optimized to reduce the error in an AI model?

Weights in AI determine how much influence the input is going to have on the output. In neural networks, algorithms use weights to process the information and train the model. The output is expected to be the same as the target attributes. However, the output may have some errors, which sometimes need to be rectified to produce the exact output.

What are the methods that are used for reducing dimensionality?

Dimensionality reduction is a process of reducing the number of random variables. The AI practitioners can reduce dimensionality using techniques such as missing values ratio, low variance filter, high correlation filter, random, forest, and others.

Explain the calibration layer in ML.

It is a post prediction adjustment, typically to account for prediction bias. The adjusted predictions and probabilities should match the distribution of an observed set of labels.

Do you know which algorithm Facebook uses for face verification?

Facebook uses DeepFace for face verification. It works on the face verification algorithm, structured by artificial intelligence techniques using neural network models. With the help of DeepFace, the Facebook platform can also detect whether two images represent the same person or not.

Explain how AI can aid targeted marketing.

Target marketing involves breaking a market into segments and then concentrating it on a few key segments consisting of the customers whose needs and desires will clearly match the company's product. AI and ML can conduct market basket analysis, clustering, classification, and use text analysis systems to decode what the customers actually want from the company.

What are intermediate tensors?

The intermediate tensors are tensors that are neither inputs nor outputs of the Session.run() call but are in the path leading from the inputs to the outputs, they will be freed at or before the end of the call.