Free IBM A1000-080: Assessment: Data Science and AI Practice Test

A data scientist needs to handle missing values in a dataset where approximately 5% of values are randomly missing across multiple features. Which approach is most appropriate for this scenario?

In a machine learning project, a model shows 98% accuracy on training data but only 65% accuracy on test data. What problem is the model experiencing, and what is the best initial solution?

Which activation function is most commonly used in hidden layers of deep neural networks due to its ability to mitigate the vanishing gradient problem?

In IBM Watson Studio, which component is primarily used for collaborative data science projects, allowing teams to organize assets, data connections, and notebooks?

What is the primary purpose of the confusion matrix in classification problems?

A retail company wants to segment customers based on purchasing behavior without predefined categories. Which type of machine learning approach should be used?

When implementing a Random Forest classifier, which of the following statements best describes how the algorithm reduces overfitting compared to a single decision tree?

A data science team is performing feature engineering on a dataset with a highly skewed income distribution. Which transformation is most appropriate to normalize this feature?

In a convolutional neural network (CNN) for image classification, what is the primary purpose of pooling layers?

Which IBM tool provides capabilities for automated machine learning (AutoAI) to automatically prepare data, select algorithms, and optimize hyperparameters?

A model is being evaluated for a medical diagnosis application where failing to detect a disease (false negative) is more critical than a false alarm (false positive). Which metric should be prioritized?

In natural language processing, what is the primary advantage of using transformer-based models like BERT over traditional RNN-based models?

A data scientist needs to evaluate multiple regression models. The dataset has 10 features and 100 observations. Which metric would best account for model complexity and prevent overfitting when comparing models?

In the context of gradient boosting algorithms, what is the primary difference between XGBoost and traditional gradient boosting?

When deploying a machine learning model using IBM Watson Machine Learning, what is the primary purpose of creating a deployment space?

A deep learning model for image segmentation needs to preserve spatial information while reducing dimensions. Which architecture component is specifically designed for this purpose?

A company is implementing a recommendation system using collaborative filtering. The user-item interaction matrix is extremely sparse (99.5% missing values). Which approach would best address the cold-start problem for new users?

In a time series forecasting problem with multiple seasonal patterns (daily and weekly), which modeling approach would be most appropriate?

When implementing transfer learning for a computer vision task with a small dataset, which strategy typically yields the best results?

A data science team needs to ensure model reproducibility and track experiment lineage across multiple collaborators using IBM Watson Studio. Which combination of features should they implement?