EMT Practice Test

1. Question Content...

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Question1: Which data source typically provides access to real-time financial market data?

Question2: What is "model deployment" in the context of data science and machine learning?

Question3: What is the primary purpose of model documentation in the model deployment phase?

Question4: What is the purpose of regularization techniques in model building, such as L1 and L2 regularization?

Question5: In a supervised learning pipeline, what is the role of the training data set?

Question6: What is the purpose of cross-validation in model building and evaluation?

Question7: What is a data lake architecture designed to store primarily?

Question8: In the context of model building, what is the purpose of hyperparameter tuning?

Question9: What is feature engineering in the context of machine learning pipelines?

Question10: When assessing a classification model, what is the confusion matrix used to measure?

Question11: What is a data lake?

Question12: What is the primary goal of building models in data science and machine learning?

Question13: What is the significance of the "bias-variance trade-off" in machine learning?

Question14: In reinforcement learning, what is the "reward signal"?

Question15: When building a deep learning neural network, what is the purpose of the activation function in each neuron?

Question16: What is the primary goal of A/B testing in the context of model deployment?

Question17: What is the primary function of a data catalog in managing data sources?

Question18: Which machine learning technique is typically used for building a model to predict a numeric target variable?

Question19: When building a recommendation system, what does "collaborative filtering" rely on?

Question20: In a supervised machine learning pipeline, what is the purpose of the test data set?

Question21: Which of the following metrics is commonly used to evaluate the performance of a binary classification model in a machine learning pipeline?

Question22: When deploying a model, what is "model explainability"?

Question23: What is the primary purpose of "continuous integration and continuous deployment" (CI/CD) in the context of model deployment?

Question24: In natural language processing (NLP), what is a common preprocessing step for text data before building models?

Question25: Which feature extraction method can take both interval variables and class variables as inputs?

Question26: What is "model versioning" in the context of model deployment?

Question27: In the context of data sources, what is ETL?

Question28: When deploying a machine learning model, what is "model drift"?

Question29: A project has been created and a pipeline has been run in Model Studio.
Which project setting can you edit?

Question30: What is the main goal of data preprocessing in a machine learning pipeline?

Question31: Which technique is used for feature selection in a machine learning pipeline when dealing with a large number of features?

Question32: Which SAS Viya component is typically used for deploying and monitoring machine learning models in production?

Question33: What is the purpose of hyperparameter tuning in a machine learning pipeline?

Question34: Which type of model is typically used for time-series forecasting?

Question35: What is the purpose of data profiling in data source management?

Question36: What is the primary goal of hyperparameter tuning during model building?

Question37: What is metadata in the context of data sources?

Question38: Which type of model is well-suited for solving classification problems when dealing with high- dimensional data, such as text?

Question39: What does "feature selection" refer to in the context of model building?

Question40: Which of the following is a common source for external data in the context of business analytics?

Question41: Which evaluation metric is commonly used for assessing the performance of a binary classification model?