Technical Interview Questions For Data Scientist

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

 

 

What is the purpose of fine-tuning in transfer learning?
To make the model slower
To adapt a pre-trained model to a new task
To perform clustering
To increase model complexity
What is the main purpose of the DBSCAN algorithm?
To scan databases
To cluster data based on density
To classify data points
To reduce data noise
Data science involves processing diverse sets of data through:
Analysing data
Processing data
Organizing data
All of the above
Which technique is commonly used for handling imbalanced datasets?
Increasing model complexity
Oversampling minority class
Using only majority class
Ignoring minority class
What is the main difference between parametric and non-parametric tests?
Sample size requirements
Assumptions about population distribution
Complexity
Accuracy
The primary purpose of cross-validation in machine learning is:
To increase model complexity
To assess model performance on unseen data
To speed up training
To create more features
Which algorithm is best suited for regression tasks?
Linear Regression
K-Means Clustering
Decision Tree
Naive Bayes
What is the purpose of a cold start problem in recommender systems?
To handle new users or items with no historical data
To select the best features for a model
To reduce data size
To visualize model performance
What is the primary purpose of building a decision tree?
To visualize data
To make sequential decisions based on features
To perform clustering
To reduce dimensionality
What is the main purpose of the attention mechanism in deep learning?
To reduce model attention
To allow the model to focus on different parts of the input
To perform clustering
To reduce dimensionality
What is the purpose of the margin in SVM?
To speed up training
To maximize separation between classes
To reduce overfitting
To simplify model interpretation
In model evaluation, ROC stands for:
Rate of Change
Receiver Operating Characteristic
Random Oscillation Curve
Recursive Operational Calculation
What is the primary focus of descriptive analytics?
Predicting future trends
Summarizing and visualizing past data
Prescribing optimal actions
Uncovering hidden patterns
What is the main purpose of the Swish activation function?
Binary classification
Multi-class classification
To provide a smooth, non-monotonic function
Feature scaling
What is the main purpose of the dropout technique?
To remove data points
To prevent overfitting in neural networks
To classify dropout rates
To reduce network size
What is the Q-learning alg.?
Model-free RL algorithm
Learns action-value function
Uses Q-table/function
All of the above
What is the main purpose of data preprocessing?
To analyze data insights
To visualize data distributions
To transform raw data into usable format
To evaluate model performance
Examples of artificial intelligence and machine learning?
Image recognition and voice assistants
Manual data entry and rule-based systems
File management and word processing
Static websites and hard-coded logic
What is Fisher Scoring in log. regression?
Optimization algorithm
Similar to Newton-Raphson
Used for max. likelihood est.
All of the above
What does the term "bias" refer to in machine learning?
A systematic error in the model's predictions
A random error in data collection
The process of clustering data points
A method for data visualization
Which algorithm is best suited for anomaly detection in streaming data?
K-means
Isolation Forest
DBSCAN
HDBSCAN
What is the difference between a convolutional neural network (CNN) and a recurrent neural network (RNN)?
CNNs are good for image data, RNNs are good for sequential data
CNNs are used for classification, RNNs are used for regression
CNNs are faster than RNNs
CNNs require more data than RNNs
What is the main advantage of using Transformer models over RNNs in NLP?
Faster training
Better parallelization
Better handling of long-term dependencies
All of the above
What does ROI stand for in time series analysis?
Return on Investment
Region of Interest
Rate of Increase
Recursive Outlier Identification
The main idea behind autoencoders is:
To classify data points
To compress and reconstruct data
To perform clustering
To increase dimensionality
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