Technical Interview Questions For Data Scientist

 Data Science Technical Interview Questions (MCQ): Test Your Knowledge!

 

 

What is the F1 score?
The F1 score is a weighted average of precision and recall
The F1 score is the sum of precision and recall
The F1 score is the difference between precision and recall
The F1 score is the product of precision and recall
What is the role of machine learning in chatbots?
To enable the chatbot to learn and improve its responses over time
To hardcode responses for the chatbot
To perform data visualization
To generate static content
What is the primary goal of data normalization in databases?
To make data abnormal
To organize data to reduce redundancy
To increase data complexity
To encrypt normal forms
What is a common technique for feature scaling in machine learning?
Standardization
Normalization
One-hot encoding
Label encoding
Which algorithm is commonly used for anomaly detection in images?
Linear regression
Naive Bayes
Isolation Forest
Decision Trees
What does GUI stand for in software design?
General User Integration
Graphical User Interface
Guided Universal Interaction
Generated Utility Implementation
Which of these is NOT a common method for natural language generation?
Rule-based
Statistical
Neural
Random generation
What is the main purpose of data lineage?
To create family trees for data
To track the origin and transformations of data
To increase data volume
To encrypt data paths
What is spaCy and its diff. from NLTK?
spaCy: NLP lib.
Focuses on prod. use
Efficient, supports deep learning
All of the above
What is the main purpose of generative adversarial networks?
To generate adversaries
To learn data distributions and generate new samples
To reduce network complexity
To encrypt generated data
What is the primary goal of LIME (Local Interpretable Model-agnostic Explanations)?
To increase model accuracy
To explain individual predictions
To speed up model training
To reduce model complexity
What is the difference between precision and accuracy?
Precision is consistency, accuracy is correctness
Accuracy is consistency, precision is correctness
They are the same
Neither relates to consistency
Which of these is an example of unsupervised learning?
Linear regression
Logistic regression
Principal Component Analysis
Random forest
What is the main purpose of dropout?
To drop out of training
To prevent overfitting
To reduce model size
To increase dropout rate
Which statement about machine learning is true?
It cannot predict outcomes
It always needs labeled data
It can learn from past data
It does not require any data
What is a real-world application of clustering?
Customer segmentation for targeted marketing
Predicting stock prices
Classifying email as spam or not spam
Reducing data dimensionality
What is the difference between precision and recall?
They are the same
Precision focuses on false positives, recall on false negatives
Recall focuses on false positives, precision on false negatives
Both measure the same thing
What is boosting in machine learning?
A technique to reduce model complexity
An ensemble method that combines weak learners
A method to increase training data size
A way to decrease model training time
What is the difference between machine learning and general programming?
ML uses data to learn
GP uses instructions
ML adapts to new data
GP follows fixed rules
What does MAE stand for in regression analysis?
Mean Absolute Error
Maximum Average Error
Mean Absolute Estimation
Median Average Error
What is time series analysis?
Time series analysis is a set of techniques for analyzing time series data
Time series analysis is a type of database
Time series analysis is a tool for data visualization
Time series analysis is a type of machine learning algorithm
Which of these is NOT a common technique for handling imbalanced datasets?
Oversampling
Undersampling
SMOTE
Overfitting
Which is not a type of data visualization chart?
Bar chart
Line chart
Pie chart
Quantum chart
What is the process of dividing a dataset into smaller, more manageable subsets for training and testing a model?
Data splitting
Data cleaning
Data transformation
Data aggregation
What does the Bias-Variance tradeoff address?
The speed of the model
The balance between model complexity and generalization
The size of the training data
The number of layers in a neural network
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