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| Vendor: | CompTIA |
|---|---|
| Exam Code: | DY0-001 |
| Exam Name: | CompTIA DataAI Certification Exam |
| Exam Questions: | 85 |
| Last Updated: | May 11, 2026 |
| Related Certifications: | CompTIA DataAI |
| Exam Tags: | Expert Data ScientistsMachine Learning Engineers |
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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.
Which of the following distance metrics for KNN is best described as a straight line?
Euclidean distance measures the straight-line distance between two points in space, matching the geometric ''as-the-crow-flies'' notion of distance.
Which of the following is best solved with graph theory?
The traveling-salesman problem is a prototypical graph theory challenge, finding the shortest tour through a graph's nodes, whereas the other tasks rely on different domains (OCR on image processing, fraud detection often on statistical/anomaly methods, bandit problems on sequential decision theory).
A model's results show increasing explanatory value as additional independent variables are added to the model. Which of the following is the most appropriate statistic?
Adjusted R accounts for the number of predictors in the model, only increasing when a new independent variable adds genuine explanatory power beyond what random chance would predict. In contrast, plain R will always rise (or stay the same) as you add more variables, regardless of their true relevance.
A data scientist is deploying a model that needs to be accessed by multiple departments with minimal development effort by the departments. Which of the following APIs would be best for the data scientist to use?
RESTful APIs use standard HTTP methods and lightweight data formats (typically JSON), making them easy for diverse teams to integrate with minimal effort and without heavy tooling.
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