AT&T Data Scientist Interview Questions and Answers

Q1. Statistics, Machine Learning, Project -Easy to moderate questions
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Basic aptitude ques were asked

Q1. Projects discussion
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Q2. Probability questions
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Q1. Why Ericsson and 5 year plan
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Q2. Your strength and weakness
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Q1. Difference between Data Warehouse, Data Lakes and Tables
Ans. 
Data Warehouse stores structured data for reporting and analysis, Data Lakes store raw and unstructured data, Tables are basic data structures.
• Data Warehouse is used for storing structured data from various sources for reporting and analysis.

• Data Lakes store raw and unstructured data in its native format for future processing and analysis.

• Tables are basic data structures used to organize and store data in a structured ...read more
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Q2. Query difference betweeen Group by and Partition by
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Group by is used to group rows that have the same values into summary rows, while Partition by is used to divide the result set into partitions to which the function is applied separately.
• Group by is used with aggregate functions to group rows based on a column or set of columns.

• Partition by is used with window functions to divide the result set into partitions.

• Group by is used with SELECT statement, while Partition by...read more
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Q3. Machine Learning questions such as Regression, Trees etc
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Coding on Computer vision, NLP and Recommendation system, any 1 could be picked and solved

Q1. Integration and differentiation, Machine learning math behind it
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Q2. Linear regression assumptions, SVM model
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Q1. Expectation from the role and compensation
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Expectations include utilizing data to drive insights and decisions, contributing to business growth, and competitive compensation.
• Utilize data to drive insights and decisions

• Contribute to business growth through data-driven strategies

• Expect competitive compensation based on experience and skills
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Q2. Behavioral questions
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Q1. Isolatn forest work? evalution metrics in laymann tems , pyspark basics , job lib
Ans. 
Isolation Forest is an anomaly detection algorithm that works by isolating outliers in a dataset.
• Isolation Forest is an unsupervised machine learning algorithm used for anomaly detection.

• It works by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature.

• The number of splits required to isolate an outlier is used as a measure of its abnormali...read more
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Q1. Scenerio based data science questns , paramatric and non parametric data
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Q1. Statistics, python, machine learning, regression
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Q1. Statistics, machine learning, regression, python
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Q1. Tell me about yourself
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Round duration - 60 minutes
Round difficulty - Easy

Technical Interview round with questions on ML mainly.

Q1. What are the assumptions of a linear regression model?
Ans. 
Assumptions of a linear regression model include linearity, independence, homoscedasticity, and normality.
• Linearity: The relationship between the independent and dependent variables is linear.

• Independence: The residuals are independent of each other.

• Homoscedasticity: The variance of the residuals is constant across all levels of the independent variables.

• Normality: The residuals are normally distributed.

• No multicolline...read more
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Q2. What problems does multicollinearity cause in regression analysis?
Ans. 
Multicollinearity in regression analysis causes issues like inflated standard errors, unstable coefficients, and difficulty in interpreting the importance of predictors.
• Multicollinearity leads to inflated standard errors, making it difficult to determine the significance of predictors.

• It causes unstable coefficients, as small changes in the data can result in large changes in the coefficients.

• Interpreting the importanc...read more
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Q3. What are the different measures used to evaluate the performance of a classification model?
Ans. 
Different measures used to evaluate classification model performance
• Accuracy: Overall correctness of the model's predictions

• Precision: Proportion of true positive predictions among all positive predictions

• Recall: Proportion of true positive predictions among all actual positives

• F1 Score: Harmonic mean of precision and recall

• Confusion Matrix: Summarizes the performance of a classification model
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Q4. What are the disadvantages of logistic regression?
Ans. 
Disadvantages of logistic regression
• Assumes linearity between independent variables and log odds of the dependent variable

• Prone to overfitting with large number of features

• Not suitable for complex relationships or non-linear data

• Can't handle missing values well
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Q1. ML questions and deep learning
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Q2. Project in your cv
Ans. 
Developed a machine learning model to predict customer churn for a telecom company.
• Used Python and scikit-learn for data preprocessing and model building

• Performed feature engineering to improve model performance

• Evaluated model using metrics like accuracy, precision, and recall
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