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| Vendor: | CompTIA |
|---|---|
| Exam Code: | DY0-001 |
| Exam Name: | CompTIA DataAI Certification Exam |
| Exam Questions: | 85 |
| Last Updated: | March 22, 2026 |
| Related Certifications: | CompTIA DataAI |
| Exam Tags: | Expert Data ScientistsMachine Learning Engineers |
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An analyst is examining data from an array of temperature sensors and sees that one sensor consistently returns values that are much higher than the values from the other sensors. Which of the following terms best describes this type of error?
A sensor that consistently reads higher than the others exhibits a repeatable bias, which is characteristic of a systematic error.
A data scientist is performing a linear regression and wants to construct a model that explains the most variation in the dat
a. Which of the following should the data scientist maximize when evaluating the regression performance metrics?
The following graphic shows the results of an unsupervised, machine-learning clustering model:

k is the number of clusters, and n is the processing time required to run the model. Which of the following is the best value of k to optimize both accuracy and processing requirements?
The curve shows a steep drop in processing time up to about k = 10, after which gains in speed taper off. Choosing 10 clusters balances sufficient model complexity with reasonable computational cost.
A data scientist is building a model to predict customer credit scores based on information collected from reporting agencies. The model needs to automatically adjust its parameters to adapt to recent changes in the information collected. Which of the following is the best model to use?
XGBoost supports ''warm-start'' incremental training, continuing to refine the existing ensemble with new data, so it can automatically update its parameters as new agency information arrives. The other methods require full retraining to incorporate recent changes.
In a modeling project, people evaluate phrases and provide reactions as the target variable for the model. Which of the following best describes what this model is doing?
The model predicts people's reactions (e.g., positive, negative, neutral) to given phrases, which is the core of sentiment analysis.
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