Free DY0-001 Exam Braindumps
CompTIA DataX Questions 16–20 (page: 5)

Updated On: 10-Feb-2026

During EDA, a data scientist wants to look for patterns, such as linearity, in the dat

  1. Which of the following plots should the data scientist use?
  2. Violin
  3. Box-and-whisker
  4. Scatter
  5. Q-Q

Answer(s): C

Explanation:

Scatter plots display pairs of numeric values on two axes, letting you visually assess relationships and patterns, such as linear trends, between variables.



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?

  1. Binomial
  2. Exponential
  3. Normal
  4. Poisson

Answer(s): C

Explanation:

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 has constructed a model that meets the minimum performance requirements specified in the proposal for a prediction project. The data scientist thinks the model's accuracy should be improved, but the proposed deadline is approaching.
Which of the following actions should the data scientist take first?

  1. Continue collecting data.
  2. Request additional funding.
  3. Consult the key project stakeholder.
  4. Test additional model specifications.

Answer(s): C

Explanation:

Since the model already meets the agreed-upon requirements and the deadline is near, the first step is to confirm with the stakeholder whether pursuing further accuracy gains is worth the additional time and resources. This ensures you align with business priorities before collecting more data, requesting funding, or tweaking the model further.



Which of the following best describes the minimization of the residual term in a ridge linear regression?

  1. |e|
  2. e
  3. e2
  4. 0

Answer(s): C

Explanation:

Ridge regression extends ordinary least squares by adding an L2 penalty on the coefficients, but it still minimizes the sum of squared residuals (e²) as its loss term.



A statistician notices gaps in data associated with age-related illnesses and wants to further aggregate these observations.
Which of the following is the best technique to achieve this goal?

  1. Label encoding
  2. Linearization
  3. Binning
  4. Imputing

Answer(s): C

Explanation:

Binning groups continuous age values into discrete intervals (e.g., age ranges), filling gaps by aggregating observations into broader categories. This directly addresses uneven or sparse age data by creating consistent age groups.



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