ML Workflow CT-AI Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A company has deployed a model to predict fraudulent transactions. The model was trained on historical data, but the fraud patterns change over time. The team wants to ensure the model remains effective. What is the most important ongoing activity?

   • AIncrease the model's complexity to handle new patterns automatically
   • BKeep the model unchanged to maintain consistency in fraud detection
   • CMonitor the model's performance metrics and retrain when drift is detected
   • DReduce the number of predictions to lower the risk of false positives
   Reveal Solution Discussion Select an answer first
2. 2application · medium

   A data scientist has trained a model and wants to evaluate its performance. The dataset is small, and the scientist wants to use all data for both training and evaluation while getting a reliable estimate. Which technique is most appropriate?

   • AUse a separate validation set only
   • BUse k-fold cross-validation
   • CSplit the data into 70% training and 30% test
   • DTrain on the entire dataset and evaluate on the same data
   Reveal Solution Discussion Select an answer first
4. 3foundation · easy

   What is the main purpose of the validation set in data preparation?

   • ATo tune model hyperparameters and compare different models during development
   • BTo provide a final unbiased assessment of the model's performance
   • CTo collect new data from external sources
   • DTo train the model on a larger dataset
   Reveal Solution Discussion Select an answer first
5. 4application · medium

   A data scientist is preparing a dataset for a binary classification model. The dataset has missing values in a feature and some outliers. The scientist wants to ensure the model is trained and evaluated fairly. Which action is most appropriate during data preparation?

   • AImpute missing values and split the data into training and test sets before any further processing
   • BSplit the data after scaling all features to the same range
   • CUse the entire dataset for training to maximize model accuracy
   • DRemove all rows with missing values and then split the data
   Reveal Solution Discussion Select an answer first
6. 5foundation · easy

   Which step typically follows model deployment in the ML workflow?

   • ACollecting raw data from various sources
   • BMonitoring the model's performance in production
   • CSelecting the ML algorithm
   • DSplitting data into training and test sets
   Reveal Solution Discussion Select an answer first