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Q11. What is “model retraining” in the context of model deployment?

The process of building the initial model

The process of feature selection

Periodically updating and improving a deployed model with new data

The process of data preprocessing

Q12. Which evaluation metric is commonly used for assessing the performance of a regression model?

F1 Score

Mean Absolute Error (MAE)

Precision

Confusion Matrix

Q13. What does “feature selection” refer to in the context of model building?

The process of choosing the most relevant variables (features) for the model

The creation of synthetic features from existing data

The evaluation of model accuracy

The visualization of data distribution

Q14. In the context of data sources, what is meant by data versioning?

Storing multiple copies of the same data to increase redundancy

Keeping track of different versions or changes to data over time

Encrypting data to protect against unauthorized access

Compressing data to reduce storage space

Q15. Which evaluation metric is commonly used for assessing the performance of a binary classification model?

Mean Absolute Error (MAE)

Root Mean Squared Error (RMSE)

Accuracy

R-squared

Q16. What is the difference between a classification problem and a regression problem in machine learning?

Classification predicts categorical outcomes, while regression predicts numeric outcomes.

Classification is a type of regression problem.

Regression predicts categorical outcomes, while classification predicts numeric outcomes.

There is no difference; the terms are used interchangeably.

Q17. In a machine learning pipeline, what is the purpose of cross-validation?

To split the dataset into training and testing sets

To evaluate the model’s performance on new data

To train multiple models on different subsets of the data to assess generalization

To visualize the data distribution

Q18. What is the purpose of hyperparameter tuning in a machine learning pipeline?

To train the model

To select the most important features

To optimize the model’s hyperparameters for better performance

To evaluate the model’s predictions

Q19. What is “model deployment” in the context of data science and machine learning?

The process of building a model

The process of selecting features

Making the model available for use in real-world applications

The process of data cleaning

Q20. Which type of model is typically used for time-series forecasting?

Decision Trees

Logistic Regression

AutoRegressive Integrated Moving Average (ARIMA)

K-Means Clustering

Q21. Which data source allows for real-time data streaming and processing?

Data warehouses

Cloud storage

IoT devices

Static data files

Q22. In natural language processing (NLP), what is a common preprocessing step for text data before building models?

Standardization

Tokenization

Principal Component Analysis (PCA)

One-Hot Encoding

Q23. What is metadata in the context of data sources?

Data that is stored in a physical format

Data about data, providing information such as data source, structure, and context

Data that is encrypted for security

Data that is in a non-standard, proprietary format

Q24. What is the primary purpose of “continuous integration and continuous deployment” (CI/CD) in the context of model deployment?

To evaluate the model’s accuracy

To automate the testing, integration, and deployment of new model versions

To create synthetic data

To visualize data distribution

Q25. What is the purpose of a “canary release” in the context of model deployment?

To assess data quality

To deploy a new model version to a small subset of users or systems for testing

To create synthetic data

To evaluate model accuracy

Q26. What is the main goal of data preprocessing in a machine learning pipeline?

To train the model

To remove irrelevant features

To prepare the data for analysis and modeling

To visualize the data

Q27. What is a common example of an external data source for an organization?

Employee databases

Customer surveys

Internal emails

Intranet portals

Q28. In natural language processing, what does “stemming” involve?

Grouping similar words together based on their meanings

Reducing words to their base or root form

Converting text to numbers for model input

Creating new words to improve model performance

Q29. When building a deep learning neural network, what is the purpose of the activation function in each neuron?

To initialize the model

To define the learning rate

To introduce non-linearity

To control the number of hidden layers