Technical Interview Questions For Data Scientist

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

 

 

What is a decision tree?
A decision tree is a tree-like model that is used to make predictions
A decision tree is a type of database
A decision tree is a tool for data visualization
A decision tree is a type of machine learning algorithm
What is the purpose of the activation function in neural networks?
To speed up training
To introduce non-linearity
To reduce overfitting
To normalize inputs
What is the purpose of the weight initialization in neural networks?
To speed up training
To set initial values for model parameters
To reduce overfitting
To improve accuracy
The main purpose of A/B testing is:
To compare two versions of something
To analyze big data
To perform clustering
To reduce dimensionality
What is the purpose of the discriminator in GANs?
To generate fake samples
To classify real vs fake samples
To reduce model complexity
To speed up training
What is an example of supervised learning?
Spam email detection
Grouping similar customers
Finding patterns in data
Reducing data dimensionality
What is the primary goal of optical character recognition (OCR)?
To generate characters
To recognize and convert text in images
To classify fonts
To reduce image complexity
Which of these is not a type of data sampling method?
Random sampling
Stratified sampling
Cluster sampling
Quantum sampling
How do you approach a data analytics project?
Define problem and objectives
Collect and explore data
Model data and interpret results
All of the above
What is the main goal of anomaly detection?
To generate anomalies
To identify unusual patterns
To classify normal data
To increase data complexity
What is the role of feature scaling in clustering?
Feature scaling: standardizes data
Improves clustering perf.
Handles varying data scales
All of the above
What is the main purpose of k-means clustering?
To classify labeled data
To group similar data points
To reduce data dimensions
To predict continuous values
In deep learning, GAN stands for:
General Adversarial Network
Generative Adversarial Network
Grouped Analytical Nodes
Guided Attention Normalization
What does IoT stand for in data science context?
Internet of Things
Integration of Technologies
Inventory of Tools
None of the above
In the context of multicollinearity, what does a Variance Inflation Factor (VIF) greater than 10 typically indicate?
No multicollinearity
Moderate multicollinearity
Severe multicollinearity
Perfect multicollinearity
What is the main purpose of the Jensen-Shannon divergence?
To diverge Jensen's theories
To measure similarity between probability distributions
To classify Shannon entropy
To reduce divergence complexity
Which of these is not a type of data governance framework?
COBIT
ITIL
TOGAF
QUANTUM
What is the main difference between bagging and boosting?
Bagging reduces bias, boosting reduces variance
Bagging reduces variance, boosting reduces bias
They are the same
Neither affects bias or variance
Explain Principal Component Analysis (PCA).
Dimensionality reduction technique
Transforms data to lower dimension
Preserves most variance
All of the above
Which algorithm is best suited for regression tasks?
Linear Regression
K-Means Clustering
Decision Tree
Naive Bayes
What's the primary purpose of treating outlier values?
To remove all extreme values
To improve model accuracy
To increase data volume
To visualize data distribution
What is the purpose of cross-validation in machine learning?
To increase model complexity
To assess model performance
To speed up training
To visualize results
What is spaCy and its diff. from NLTK?
spaCy: NLP lib.
Focuses on prod. use
Efficient, supports deep learning
All of the above
Why is cross-validation important in machine learning?
Increases training speed
Provides more training data
Gives a better estimate of model performance
Simplifies the model architecture
What is the primary purpose of hypothesis testing?
To prove a theory
To make inferences about a population
To calculate probabilities
To determine causation
Score: 0/25