The most likely concern with a one-feature, machine-learning model is high error due to:
Answer : A
A model with only one feature is unlikely to capture the true complexity of the data's underlying relationships, leading to systematic underfitting - i.e., high bias.
Which of the following distribution methods or models can most effectively represent the actual arrival times of a bus that runs on an hourly schedule?
Answer : C
Scheduled buses tend to arrive around a fixed time with random delays that cluster symmetrically around the hour. A normal distribution effectively models those continuous, bell-shaped deviations from the exact schedule.
A data scientist wants to predict a person's travel destination. The options are:
Which of the following models would best fit this use case?
Answer : A
You need a supervised multiclass classification model to predict one of the four labeled destinations. Linear Discriminant Analysis is designed for such tasks, finding the linear boundaries that best separate the known destination classes.
A data scientist is preparing to brief a non-technical audience that is focused on analysis and results. During the modeling process, the data scientist produced the following artifacts:
Which of the following artifacts should the data scientist include in the briefing? (Choose two.)
Answer : A
For a nontechnical audience centered on results, polished visualizations (charts and dashboards) and clear, high-level performance metrics (accuracy, precision, recall, F1 score) best convey the key takeaways. The deeper technical details, code docs, data dictionaries, and algorithm math, should be omitted at this level.
A data scientist is building an inferential model with a single predictor variable. A scatter plot of the independent variable against the real-number dependent variable shows a strong relationship between them. The predictor variable is normally distributed with very few outliers. Which of the following algorithms is the best fit for this model, given the data scientist wants the model to be easily interpreted?
Answer : C
Which of the following methods should a data scientist use just before switching to a potential replacement model?
Answer : A
A/B testing lets you compare the current model against the candidate in parallel, measuring performance on live data, before fully switching to the new model.
Which of the following types of layers is used to downsample feature detection when using a convolutional neural network?
Answer : A
Pooling layers (such as max pooling or average pooling) reduce the spatial dimensions of the feature maps by summarizing local neighborhoods, effectively downsampling the detected features and controlling overfitting.