AI and ML Quiz

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AI and ML Quiz - Day 5

This quiz tests your understanding of end-to-end ML pipelines and building projects with AI tools. Each question carries 5 points.

Name *

1. What are the components of an ML pipeline? *

Data collection, preprocessing, training, deployment

Training, testing, deployment

Only data preprocessing

Only training

2. What is the role of data preprocessing? *

Preparing data for training

Training the model

Evaluating the model

Deploying the model

3. What does model evaluation involve? *

Checking accuracy, precision, and recall

Collecting data

Building APIs

None of the above

4. What is the purpose of hyperparameter tuning? *

Improve model performance

Normalize data

Handle missing values

Visualize data

5. Which tool is used for tracking experiments in ML projects? *

TensorFlow

MLflow

Pandas

NumPy

6. What is cross-validation used for? *

Assess model performance on unseen data

Preprocess data

Train the model

Deploy the model

7. Which method is used to deploy ML models? *

Using Flask or Streamlit

Training a new model

Building a dataset

Improving visualization

8. What format is commonly used to save models? *

Pickle (.pkl)

CSV

JSON

Text file

9. What is the purpose of a requirements.txt file? *

List dependencies for an ML project

Store data

Save the model

Track experiments

10. Which metric is best for binary classification? *

Accuracy, Precision, Recall, F1-Score

Mean Absolute Error

R-squared

Correlation

11. What is overfitting? *

Model performs well on training but poorly on test data

Model performs well on all data

Model fails to learn patterns

None of the above

12. Which Python library is used for handling large datasets? *

Pandas

NumPy

Dask

Matplotlib

13. What is the role of Docker in ML? *

Containerizing applications for consistent deployment

Handling large datasets

Training models

Tracking experiments

14. What is the significance of feature engineering? *

Improves model accuracy

Handles missing values

Improves data visualization

All of the above

15. What is the purpose of logging in ML projects? *

Track training and evaluation details

Store data

Improve performance

Handle missing values

16. What is the first step in deploying a project? *

Saving the trained model

Building an API

Collecting data

Handling missing values

17. Which framework simplifies model monitoring in production? *

Prometheus

TensorFlow

PyTorch

Seaborn

18. What is an ensemble model? *

Combines multiple models to improve performance

Only uses one model

Removes outliers

Normalizes data

19. Why is model interpretability important? *

To explain predictions

To improve accuracy

To handle data preprocessing

To visualize models

20. What is the role of APIs in ML deployment? *

Facilitate interaction between model and application

Improve model accuracy

Handle missing data

Visualize data

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