30+ Engineer Plus Interview Questions and Answers

Object detection using CNN involves training a neural network to identify and locate objects within an image.

• CNNs use convolutional layers to extract features from images

• These features are then passed through fully connected layers to classify and locate objects

• Common architectures for object detection include YOLO, SSD, and Faster R-CNN

Central Limit Theorem states that the sampling distribution of the mean of any independent, random variable will be normal or nearly normal.

• The theorem applies to large sample sizes.

• It is a fundamental concept in statistics.

• It is used to estimate population parameters from sample statistics.

• It is important in hypothesis testing and confidence intervals.

• Example: If we take a large number of samples of the same size from a population, the distribution of the sample means will b...read more

Gradient descent is an iterative optimization algorithm used to minimize a cost function by adjusting model parameters.

• Gradient descent is used in machine learning to optimize models.

• It works by iteratively adjusting model parameters to minimize a cost function.

• The algorithm calculates the gradient of the cost function and moves in the direction of steepest descent.

• There are different variants of gradient descent, such as batch, stochastic, and mini-batch.

• Gradient descent can...read more

Image segmentation is the process of dividing an image into multiple segments or regions.

• It is used in computer vision to identify and separate objects or regions of interest in an image.

• It can be done using various techniques such as thresholding, clustering, edge detection, and region growing.

• Applications include object recognition, medical imaging, and autonomous vehicles.

• Examples include separating the foreground and background in an image, identifying tumors in medical i...read more

R-squared measures the proportion of variance explained by the model, while Adjusted R-squared adjusts for the number of predictors in the model.

• R-squared is the proportion of variance in the dependent variable that is predictable from the independent variables. It ranges from 0 to 1, with 1 indicating a perfect fit.

• Adjusted R-squared penalizes the addition of unnecessary predictors to the model, providing a more accurate measure of the model's goodness of fit.

• R-squared can i...read more

Classification metrics like accuracy, precision, and recall are used to evaluate the performance of a classification model.

• Accuracy measures the overall correctness of the model's predictions.

• Precision measures the proportion of true positive predictions out of all positive predictions.

• Recall measures the proportion of true positive predictions out of all actual positive instances.

Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models by combining multiple weak learners.

• Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and then combining their predictions through averaging or voting.

• Boosting involves training multiple models sequentially, where each subsequent model corrects the errors made by the previous ones. Examples inc...read more

Various types of joins in SQL include inner join, outer join, left join, right join, and full join.

• Inner join: Returns rows when there is a match in both tables.

• Outer join: Returns all rows when there is a match in one of the tables.

• Left join: Returns all rows from the left table and the matched rows from the right table.

• Right join: Returns all rows from the right table and the matched rows from the left table.

• Full join: Returns rows when there is a match in one of the tables...read more

Feature selection techniques help in selecting the most relevant features for building predictive models.

• Filter methods: Select features based on statistical measures like correlation, chi-squared test, etc.

• Wrapper methods: Use a specific model to evaluate the importance of features by adding or removing them iteratively.

• Embedded methods: Feature selection is integrated into the model training process, like LASSO regression.

• Principal Component Analysis (PCA): Transform the da...read more

Find the greatest number from an array of strings.

• Convert the array of strings to an array of integers.

• Use a sorting algorithm to sort the array in descending order.

• Return the first element of the sorted array as the greatest number.

Bias-variance tradeoff is the balance between model complexity and generalization error.

• Bias refers to error from erroneous assumptions in the learning algorithm, leading to underfitting.

• Variance refers to error from sensitivity to fluctuations in the training data, leading to overfitting.

• Increasing model complexity reduces bias but increases variance, while decreasing complexity increases bias but reduces variance.

• The goal is to find the right balance to minimize both bias a...read more

Ensemble techniques combine multiple models to improve prediction accuracy.

• Ensemble techniques can be used with various types of models, such as decision trees, neural networks, and support vector machines.

• Common ensemble techniques include bagging, boosting, and stacking.

• Bagging involves training multiple models on different subsets of the data and combining their predictions through averaging or voting.

• Boosting involves iteratively training models on the data, with each sub...read more

I would politely thank the customer for their time and offer to follow up at a more convenient time.

• Acknowledge the customer's lack of interest without being pushy

• Offer to follow up at a later time when the customer may be more receptive

• Provide additional information or resources that may spark the customer's interest

Segmenting a mobile phone involves dividing the market based on different criteria such as demographics, psychographics, and behavior.

• Segment based on demographics like age, gender, income, and occupation.

• Segment based on psychographics like lifestyle, personality, and values.

• Segment based on behavior like usage patterns, brand loyalty, and purchase frequency.

Bias is error due to erroneous assumptions in the learning algorithm. Variance is error due to sensitivity to small fluctuations in the training set.

• Bias is the difference between the expected prediction of the model and the correct value that we are trying to predict.

• Variance is the variability of model prediction for a given data point or a value which tells us spread of our data.

• High bias can cause an algorithm to miss relevant relations between features and target outputs...read more

Ensemble techniques combine multiple models to improve prediction accuracy.

• Bagging: Bootstrap Aggregating

• Boosting: AdaBoost, Gradient Boosting

• Stacking: Meta-model combines predictions of base models

• Voting: Combining predictions of multiple models by majority voting

Classification techniques are used to categorize data into different classes or groups based on certain features or attributes.

• Common classification techniques include decision trees, logistic regression, k-nearest neighbors, and support vector machines.

• Classification can be binary (two classes) or multi-class (more than two classes).

• Evaluation metrics for classification include accuracy, precision, recall, and F1 score.

• Feature selection and engineering can improve classifica...read more

I prioritize keywords based on relevance, search volume, competition, and potential impact on business goals.

• Identify keywords relevant to the business or industry

• Consider search volume to target high traffic keywords

• Assess competition to determine feasibility of ranking

• Align keywords with business goals and target audience

• Regularly review and adjust keyword strategy based on performance

Random forest is an ensemble learning method for classification, regression and other tasks.

• Random forest builds multiple decision trees and combines their predictions to improve accuracy.

• It uses bagging technique to create multiple subsets of data and features for each tree.

• Random forest reduces overfitting and is robust to outliers and missing values.

• It can handle high-dimensional data and is easy to interpret feature importance.

• Example: predicting customer churn, fraud det...read more

Introducing the revolutionary new smartwatch that will change the way you live your life!

• Our smartwatch allows you to track your fitness goals and monitor your health

• Stay connected with notifications for calls, texts, and social media

• Control your music and take photos with just a tap on your wrist

• Water-resistant and durable design for all your adventures

• Long battery life that lasts up to 5 days on a single charge

Inside sales refers to sales conducted remotely, typically over the phone or online, rather than in person.

• Inside sales involves selling products or services remotely, often through phone calls, emails, or online demos.

• Inside sales reps focus on generating leads, qualifying prospects, and closing deals without meeting clients face-to-face.

• Technology plays a key role in inside sales, with CRM systems, sales automation tools, and video conferencing used to streamline the sales ...read more

Marketing focuses on creating awareness and interest in a product or service, while sales involves closing deals and generating revenue.

• Marketing involves activities such as advertising, branding, and market research.

• Sales involves activities such as prospecting, qualifying leads, and negotiating contracts.

• Marketing aims to attract and retain customers through various channels.

• Sales focuses on converting leads into customers and driving revenue.

• Marketing is about creating dem...read more

Data Science is a field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

• Data Science involves collecting, analyzing, and interpreting large amounts of data to make informed decisions.

• It combines statistics, machine learning, data visualization, and programming to uncover patterns and trends in data.

• Data Scientists use tools like Python, R, SQL, and Hadoop to work with data.

• Examples of Data Science a...read more

Boosting is an ensemble learning technique that combines multiple weak models to create a strong model.

• Boosting iteratively trains weak models on different subsets of data

• Each subsequent model focuses on the misclassified data points of the previous model

• Final prediction is made by weighted combination of all models

• Examples include AdaBoost, Gradient Boosting, XGBoost

Bagging is a technique used in machine learning to improve the stability and accuracy of a model by combining multiple models.

• Bagging stands for Bootstrap Aggregating.

• It involves creating multiple subsets of the original dataset by randomly sampling with replacement.

• Each subset is used to train a separate model, and the final prediction is the average of all the predictions made by each model.

• Bagging reduces overfitting and variance in the model.

• Random Forest is an example of...read more

AD level analysis on Google ads involves evaluating the performance of individual ads to optimize campaign effectiveness.

• Analyze click-through rates (CTR) of each ad to determine which ones are most effective

• Evaluate conversion rates to see which ads are driving the most valuable actions

• Consider ad relevance and quality score to improve ad performance

• Use A/B testing to compare different ad variations and identify the most successful ones

I have 5 years of SQL experience in querying databases and generating reports.

• 5 years of experience in writing complex SQL queries

• Proficient in using SQL functions and joins

• Experience in creating and optimizing database tables

• Familiar with database management systems like MySQL and SQL Server

Designing a low level architecture for a quiz portal application

• Use a microservices architecture for scalability and flexibility

• Implement a database schema to store quiz questions, answers, and user responses

• Utilize caching mechanisms to improve performance

• Design an authentication system to ensure secure access to quizzes

• Include features for creating, editing, and taking quizzes