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1. Which statement is true regarding decision trees and models based on ensembles of trees?

In the gradient boosting algorithm, for all but the first iteration, the target is the residual from the previous decision tree model.

For a Forest model, the out-of-bag sample is simply the original validation data set from when the raw data partitioning took place.

In the Forest algorithm, each individual tree is pruned based on using minimum Average Squared Error.

A single decision tree will always be outperformed by a model based on an ensemble of trees.

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2. What is the primary purpose of model assessment in the context of data science and machine learning?

Data preprocessing

Model building

Evaluating and selecting the best-performing model

Data visualization

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3. 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

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4. 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

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5. In machine learning, what does "overfitting" refer to?

A model that performs well on new, unseen data

A model that has too much complexity and fits the training data too closely

A model that is undertrained and has high bias

A model that is unable to make predictions

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6. What is the primary purpose of model deployment in the context of data science and machine learning?

Model building

Model evaluation

Making the model available for use in real-world applications

Data preprocessing

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7. 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

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8. Which hyperparameter in a decision tree model controls the depth of the tree and helps prevent overfitting?

Learning rate

Max features

Max depth

Min samples split

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9. When assessing a classification model, what is the confusion matrix used to measure?

Model accuracy

Precision and recall

Model complexity

Data distribution

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10. What is the main advantage of ensemble methods in model building?

They require minimal data preprocessing

They produce simple and interpretable models

They combine multiple models to improve predictive performance

They work well with high-dimensional data

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11. What is the primary function of a data catalog in managing data sources?

Data storage

Data analysis

Data documentation and discovery

Data visualization

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12. Which statements are true for the F1 score?

(Choose 2.)

F1 score is calculated based on a depth value.

F1 score is calculated based on a cut off value.

F1 score is applicable to a model with a binary target.

F1 score is applicable to a model with an interval target.

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

Data warehouses

Cloud storage

IoT devices

Static data files

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14. What does "data lineage" refer to in the context of data source management?

The history of data transformation processes

The physical location of data storage

The structure of a relational database

The security protocols for data access

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15. What is the purpose of a confusion matrix in the context of classification models?

To summarize the distribution of target variables

To visualize the data

To evaluate model performance, especially for binary classification

To compute the mean squared error

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16. 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

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17. 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

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18. What is the primary purpose of model documentation in the model deployment phase?

To create synthetic data

To assess data quality

To provide information on the model's development, architecture, and usage

To evaluate the model's accuracy

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19. Which technique is commonly used for feature scaling or normalization in machine learning pipelines?

One-Hot Encoding

Principal Component Analysis (PCA)

Standardization

Decision Trees

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20. Which feature extraction method can take both interval variables and class variables as inputs?

Autoencoder

Principal component analysis

Singular value decomposition

Robust PCA

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21. What is the primary objective of model validation during the model assessment phase?

To build a model from scratch

To assess the accuracy of the model

To ensure the model generalizes well to new, unseen data

To create synthetic data

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

F1 Score

Mean Absolute Error (MAE)

Precision

Confusion Matrix

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23. Which of the following is a common technique for handling missing data in a machine learning pipeline?

Ignoring missing data

Replacing missing values with zeros

Imputing missing values

Deleting rows with missing data

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24. What is the purpose of cross-entropy loss in machine learning, especially in the context of classification?

To calculate the mean squared error of a regression model

To measure the dissimilarity between predicted and actual class probabilities

To quantify the variance of a model

To evaluate feature importance

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25. 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

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26. What is a data lake?

A data storage solution designed for high-speed data retrieval

A centralized repository for storing all structured and unstructured data at any scale

A specialized database for time-series data

A backup system for relational databases

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27. In the context of model deployment, what is "model compliance"?

The model's simplicity

The model's efficiency

The degree to which the model adheres to regulatory or ethical guidelines

The process of feature selection

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28. Which data source typically provides access to real-time financial market data?

Social media platforms

Weather stations

Stock market APIs

Online news websites

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29. Given the following properties for a neural network model, which statement is true regrading hidden units in the model? The following SAS program is submitted:

There are no hidden units in the model.

The number of hidden units is 1.

The number of hidden units is 50.

The number of hidden units is 26.

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30. When deploying a machine learning model, what is "model drift"?

A sudden increase in the model's accuracy

A change in the distribution of the input data or target variable over time

The process of feature extraction

A measure of feature importance

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