Concept Interview Questions

Juan moises T. - "There are three stages in the React lifecycle: mount, update, and unmount. The mount stage runs once when the component is loaded on the page. The update stage runs whenever the data that passes to the component, like props or state, changes. Finally, the unmount stage runs when the component is removed from the page, a.k.a. the cleanup stage. The useEffect hook can be used to track these stages, and it runs once right after the mount stage; it updates based on the data passed to the dependency"See full answer

Kavy U. - "Basically, normalization layer is a technique use to accelerate and stabilize the training of neural network."See full answer

Alex N. - "Perplexity - the measure LLM is surprised when it predicts next word. For example: I love to eat --- if LLM selects as next word "fruits" it will be less surprising than if LLM selects as next word "metal". It is better to have lower perplexity score. Cross Entropy is the measure how well model match true labels. So if the next word is "cat" and LLM assigns 0.5 to it then cross entropy value is -log(0.5) = 0.69 and if it assigns 0.9 probability to word cat then cross entropy value is - log(0.9"See full answer

Nathan B. - "There are many good answers to this that AI scientists around the world, I and my coworkers have tried over the years. For one, RAG is a great option to fact-check and enforce citation generation, update the data in the knowledge base of the generative AI, etc. "See full answer

Alex N. - "The adjusting context window size in LLM change trade off between reasoning capability, accuracy, computation cost. It influence how attention mechanist allocate resources across the input. Longer context window let it you input greater number of words and have more context to generate proper next token. However llms have lost in the middle issue. They remember the beginning of text and end of text but lost information located in the middle of long input. Another problem is Attention Dilution."See full answer

Mark G. - "I look at AI safety, ethics, guardrails, and governance as layers of one core responsibility: making systems useful without letting them cause harm. The Technical Core: Safety Safety is about managing technical risks like reliability, bias, and unintended behavior. It cannot be a final checklist before launch. It has to be part of the whole cycle, from how data is collected to how the system is monitored after it goes live. The Human Element: Ethics Ethics asks if a system should"See full answer

Anonymous Crow - "Find the median height then place all players less than that height in the front?"See full answer

Anchal V. - "supervised learning: model is trained on the labeled data unsupervised learning: no labels provided - model learns by finding patterns , structure and groupings in the data. Semi-supervised learning: use small set of labels to guide learning for the larger pool of unlabeled data. reinforcement learning: leans by interacting with students the environment, receives reward and penalties based on actions self supervised: no labelled data . The model makes its own practice problems by"See full answer

Amy M. - "Clarifying questions: Do we want to focus on front end or backend? Front end Do we want to focus on any particular platform? For ex: Site, mobile, apps Interviewer: Desktop Is there anything tools on gmail that you'd like me to focus on? For ex: Meet, Hangouts, Notes Interviewer: Just the main product Are there any specific product buckets that you'd like me to go through? For ex: Sign up flows, login flows, security, product experience, sign out flow, recommend"See full answer

Temesgen B. - "Very Good Explanation Thanks For This Rely Good Explanation"See full answer

Hardik saurabh G. - "Hallucinations are evaluated by measuring how often generated outputs contain information that is not supported by trusted sources. what hallucination means in context: Intrinsic hallucination: contradicts provided context Extrinsic hallucination: introduces unsupported facts Fabrication: confidently incorrect answers"See full answer

Dessalew A. - "Gradient Descent = core engine for training most ML models It works by iteratively minimizing loss via gradients Many improvements exist (Adam, RMSProp, etc.) Alternatives exist for: Faster convergence Non-differentiable problems Direct metric optimization"See full answer

J R. - "These are a set of utilities used to manage the heap memory as part of an application. The C standard library implements these functions. malloc(bytes) takes a number of bytes and returns a pointer to the start of the allocated buffer. If the allocation failed, a null pointer is returned instead. calloc(count, size) behaves like malloc(count * size), but also zero-initializes the allocated buffer, assuming the allocation succeeded. realloc(ptr, size) takes a pointer to a previously al"See full answer

Louie Z. - "DNNs can learn hierarchical features, with each layer learning progressively more abstract features, and generalizes better. SNNs are better for simplier problems involving smaller datasets and if low latency is required."See full answer

Anonymous Aardvark - "in simple words, linear regression helps in predicting the value whereas logistics regression helps in predicting the binary classification. But lets talk through some example Linear regression model: E-commerce website pricing recommendation engine is built on linear regression model where we do have some variables such as competitor price, internal economics and consumer demand etc when we put this in a supervised learning model, it helps in predicting prices Logistics regression model"See full answer