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Here are sample CompTIA DataX Certification (DY0-001) Exam questions from real exam. You can get more CompTIA DataX (DY0-001) Exam premium practice questions at TestInsights.
Question 1
A data scientist has built a model that provides the likelihood of an error occurring in a factory. The historical accuracy of the model is 90%. At a specific factory, the model is reporting a likelihood score of 0.90. Which of the following explains a confidence score of 0.90?
Correct : D
A confidence score of 0.90 is a probabilistic estimate, interpreted as the model assigning a 90% chance of an error on that particular factory instance, which in the long run corresponds to predicting ''error'' in about 90 out of every 100 identical runs.
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Question 2
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?
Correct : A
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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Question 3
A data scientist is building a forecasting model for the price of copper. The only input in this model is the daily price of copper for the last ten years. Which of the following forecasting techniques is the most appropriate for the data scientist to use?
Correct : A
An autoregressive model uses past values of the series itself (here, historical daily copper prices) as predictors for future values, making it the most suitable technique when only the timeseries history is available.
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Question 4
Which of the following is a key difference between KNN and k-means machine-learning techniques?
Correct : D
KNN is a supervised algorithm that assigns labels based on the closest labeled examples, whereas k-means is an unsupervised method that partitions data into clusters by finding centroids without using any pre-existing labels.
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Question 5
Which of the following describes the appropriate use case for PCA?
Correct : A
Principal Component Analysis transforms correlated features into a smaller set of uncorrelated components that capture most of the variance, making it ideal for reducing dimensionality before modeling or visualization.
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