Data Science Program Final Exam

Data Science Final Exam

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1. Which of the following best defines data science?

A study of mathematical models without real-world application

A branch of software engineering that focuses on database management

A field that combines statistics, machine learning, and domain knowledge to extract insights from data

A discipline that focuses only on data visualization

2. Which stage of the data science lifecycle involves transforming raw data into a structured format?

Data Cleaning

Model Deployment

Data Collection

Data Visualization

3. Which industry most commonly applies predictive analytics for fraud detection?

E-commerce

Education

Finance

Healthcare

4. Which of the following is not a common dimensionality reduction technique?

t-SNE

Linear Discriminant Analysis (LDA)

Principal Component Analysis (PCA)

Decision Trees

5. Which of the following is a key challenge in working with different data formats like CSV, JSON, and TXT?

Eliminating the need for data cleaning

Avoiding structured data

Ensuring data consistency across formats

Not using schemas for storage

6. Which method is best suited for encoding categorical variables in machine learning models?

One-hot encoding

Normalization

Feature scaling

Principal Component Analysis

7. What is the primary goal of Exploratory Data Analysis (EDA)?

To replace feature engineering

To find hidden patterns and summarize the dataset

To validate a pre-trained machine learning model

To perform predictive modeling

8. Which measure of descriptive statistics is most affected by outliers?

Median

Mean

Standard deviation

Mode

9. Which Python library is commonly used for interactive data visualization?

Seaborn

Plotly

NumPy

Matplotlib

10. Which dashboarding tool is best suited for business intelligence reporting?

Scikit-learn

PyTorch

Hadoop

Tableau

11. Which machine learning algorithm is used for predicting continuous values?

K-Means Clustering

Linear Regression

Logistic Regression

Decision Trees

12. Which of the following evaluation metrics is most useful for an imbalanced classification problem?

F1-score

R-squared

Mean Squared Error

Accuracy

13. Which machine learning library is widely used for model building in Python?

Keras

Scikit-learn

Pandas

TensorFlow

14. Which of the following is a key difference between ANNs and traditional machine learning models?

ANNs do not require training data

Traditional ML models need GPUs

ANNs require labeled data

ANNs are inspired by biological neural networks

15. Which Python framework is most commonly used for deep learning?

Hadoop

Matplotlib

TensorFlow

Scikit-learn

16. Which preprocessing step is essential before applying LDA for dimensionality reduction?

Normalizing numerical features

Encoding categorical variables

Removing missing values

Ensuring class labels are available

17. Which NLP technique is commonly used for sentiment analysis?

Text Classification

Tokenization

TF-IDF

Word Embeddings

18. Which of the following best defines Big Data?

Data that is stored exclusively in SQL databases

Any dataset that can be processed on a single machine

Data that only consists of structured records

Data that exceeds traditional processing capabilities

19. Which distributed computing framework is widely used for big data processing?

Power BI

TensorFlow

Apache Hadoop

Scikit-learn

20. Which type of NoSQL database is best suited for real-time analytics?

Column-family stores

Key-value stores

Document stores

Graph databases

21. Which cloud service provides serverless computing for machine learning deployment?

Google BigQuery

Microsoft Excel

AWS Lambda

Amazon RDS

22. Which of the following is a common challenge in deploying machine learning models?

High accuracy on training data

Running models without preprocessing

Ensuring model interpretability

Reducing the number of hyperparameters

23. What is a common ethical concern in AI-driven business decisions?

Algorithmic bias

Improving automation

Feature selection

Data augmentation

24. Which best describes algorithmic bias?

A sudden increase in computation time

A systematic error due to flawed training data

A decrease in model accuracy

A model that performs well on all datasets

25. Which method is most effective in reducing bias in AI models?

Relying only on historical data

Minimizing dataset size

Ignoring categorical features

Using diverse and representative datasets

26. Which metric is commonly used to detect overfitting?

Mean squared error

Validation accuracy

Training accuracy

Learning rate

27. Which factor is most critical for scalability in cloud-based ML model deployment?

Data format compatibility

Data visualization techniques

Model selection

Compute resource allocation

28. Which AWS service is designed for relational data storage?

Amazon DynamoDB

Google BigQuery

Amazon RDS

Hadoop HDFS

29. Which cloud model offers full infrastructure control?

Platform as a Service (PaaS)

Function as a Service (FaaS)

Infrastructure as a Service (IaaS)

Software as a Service (SaaS)

30. Which best practice ensures responsible AI development?

Maximizing model complexity

Regular audits for fairness and bias

Avoiding feature selection

Using historical data without updates

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