Wipro Data Scientist and Machine Learning Engineer Interview Questions and Answers

Q1. Basic ML questions and previous project-related queries
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Q1. Advanced ML questions
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Q1. Project handle queires
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Q1. What is evaluation Matrix for classification
Ans. 
Evaluation metrics for classification are used to assess the performance of a classification model.
• Common evaluation metrics include accuracy, precision, recall, F1 score, and ROC-AUC.

• Accuracy measures the proportion of correctly classified instances out of the total instances.

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

• Recall measures the proportion of true positive p...read more
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Q2. What L1 and L2 regression
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L1 and L2 regression are regularization techniques used in machine learning to prevent overfitting.
• L1 regression adds a penalty equivalent to the absolute value of the magnitude of coefficients.

• L2 regression adds a penalty equivalent to the square of the magnitude of coefficients.

• L1 regularization can lead to sparse models, while L2 regularization tends to shrink coefficients towards zero.

• L1 regularization is also know...read more
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Q3. Explain random forest algorithm
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Random forest is an ensemble learning algorithm that builds multiple decision trees and combines their predictions.
• Random forest creates multiple decision trees using bootstrapping and feature randomization.

• Each tree in the random forest is trained on a subset of the data and features.

• The final prediction is made by averaging the predictions of all the trees (regression) or taking a majority vote (classification).
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Q1. Tell me about self
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I am a dedicated and passionate Machine Learning Engineer with a strong background in computer science and data analysis.
• Experienced in developing machine learning models for various applications

• Proficient in programming languages such as Python, R, and Java

• Skilled in data preprocessing, feature engineering, and model evaluation

• Strong understanding of algorithms and statistical concepts

• Excellent problem-solving and ana
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Q2. Questions about salary discuss
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Q1. Screening round: Tell me about your experiences?
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Q2. Tell me Learning and development challenges you faced and improvements you have done
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Genral and technical aptitude test

Q1. Overall experience?
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Q2. What was challenging role in previous org?
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Q3. How you see L&D from your perspective?
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Q4. How you tackle problems in your role?
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Q5. How will you onboard 500 candidates every month?
Ans. 
By creating a structured onboarding process, utilizing technology for efficiency, and leveraging a team of trainers.
• Develop a comprehensive onboarding program with clear objectives and timelines.

• Utilize technology such as online training modules and virtual onboarding sessions.

• Assign a team of trainers to handle different aspects of the onboarding process.

• Implement a buddy system where existing employees mentor new hir...read more
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Q1. Started with basic os or network fundamentals like analyzing a core dump, handling tcp packet loss, difference between rest api and grpc etc. Then, from ML started with basic questions like bias variance t...read more
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Q1. ML algorithms, Live screen share coding
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Q1. ML deployment, Cloud knowledge
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My skills python and Java

Q1. What is L1 and L2 regression
Ans. 
L1 and L2 regression are regularization techniques used in machine learning to prevent overfitting by adding penalty terms to the loss function.
• L1 regression adds the absolute values of the coefficients as penalty term (Lasso regression)

• L2 regression adds the squared values of the coefficients as penalty term (Ridge regression)

• L1 regularization can lead to sparse models with some coefficients being exactly zero

• L2 regul...read more
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Q2. Explain auc and roc
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AUC (Area Under the Curve) is a metric that measures the performance of a classification model. ROC (Receiver Operating Characteristic) is a graphical representation of the AUC.
• AUC is a single scalar value that represents the area under the ROC curve.

• ROC curve is a plot of the true positive rate against the false positive rate for different threshold values.

• AUC ranges from 0 to 1, where a higher value indicates better ...read more
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Q3. Precision and recall
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Q4. Parameter of random forest
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Parameter of random forest is the number of trees in the forest.
• Number of trees in the forest affects model performance

• Higher number of trees can lead to overfitting

• Commonly tuned parameter in random forest algorithms
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Q5. What isp,d,q values in time series
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p, d, q values are parameters used in ARIMA time series models to determine the order of differencing and moving average components.
• p represents the number of lag observations included in the model (autoregressive order)

• d represents the degree of differencing needed to make the time series stationary

• q represents the number of lagged forecast errors included in the model (moving average order)

• For example, in an ARIMA(1,
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Q1. Difference between bias and variance
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Bias is error due to overly simplistic assumptions, variance is error due to overly complex models.
• Bias is error introduced by approximating a real-world problem, leading to underfitting.

• Variance is error introduced by modeling the noise in the training data, leading to overfitting.

• High bias can cause a model to miss relevant relationships between features and target variable.

• High variance can cause a model to be overl...read more
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Q2. What’s is Learning rate
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Learning rate is a hyperparameter that controls how much we are adjusting the weights of our network with respect to the loss gradient.
• Learning rate determines the size of the steps taken during optimization.

• A high learning rate can cause the model to converge too quickly and potentially miss the optimal solution.

• A low learning rate can cause the model to take a long time to converge or get stuck in a local minimum.

• Com...read more
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