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  1. Home
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  3. DY0-001

CompTIA DY0-001 Exam (page: 2)
CompTIA DataX
Updated on: 12-Feb-2026

Viewing Page 2 of 12
DY0-001 PDF 85 Questions

Which of the following is a key difference between KNN and k-means machine-learning techniques?

  1. KNN operates exclusively on continuous data, while k-means can work with both continuous and categorical data.
  2. KNN performs better with longitudinal data sets, while k-means performs better with survey data sets.
  3. KNN is used for finding centroids, while k-means is used for finding nearest neighbors.
  4. KNN is used for classification, while k-means is used for clustering.

Answer(s): D

Explanation:

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.



A data scientist needs to:

Build a predictive model that gives the likelihood that a car will get a flat tire.

Provide a data set of cars that had flat tires and cars that did not.

All the cars in the data set had sensors taking weekly measurements of tire pressure similar to the sensors that will be installed in the cars consumers drive.
Which of the following is the most immediate data concern?

  1. Granularity misalignment
  2. Multivariate outliers
  3. Insufficient domain expertise
  4. Lagged observations

Answer(s): D

Explanation:

Because tire-pressure sensors report only weekly measurements, you risk missing the critical pressure drop immediately preceding a flat. Those stale ("lagged") readings may not reflect the condition just before failure, undermining your model's ability to learn the true precursors to a flat tire.



The term "greedy algorithms" refers to machine-learning algorithms that:

  1. update priors as more data is seen.
  2. examine even/ node of a tree before making a decision.
  3. apply a theoretical model to the distribution of the data.
  4. make the locally optimal decision.

Answer(s): D

Explanation:

Greedy algorithms build the solution iteratively by choosing at each step the option that appears best at that moment, without reconsidering earlier choices.



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?

  1. SOAP
  2. RPC
  3. JSON
  4. REST

Answer(s): D

Explanation:

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.



Which of the following compute delivery models allows packaging of only critical dependencies while developing a reusable asset?

  1. Thin clients
  2. Containers
  3. Virtual machines
  4. Edge devices

Answer(s): B

Explanation:

Containers encapsulate just the application and its critical dependencies on a lightweight runtime, making the resulting asset portable and reusable without bundling an entire operating system.



A data analyst is analyzing data and would like to build conceptual associations.
Which of the following is the best way to accomplish this task?

  1. n-grams
  2. NER
  3. TF-IDF
  4. POS

Answer(s): A

Explanation:

n-grams capture contiguous sequences of words, revealing which terms co-occur and form meaningful multi-word concepts. By analyzing these frequent word combinations, you directly uncover conceptual associations in the text.



Which of the following belong in a presentation to the senior management team and/or C-suite executives? (Choose two.)

  1. Full literature reviews
  2. Code snippets
  3. Final recommendations
  4. High-level results
  5. Detailed explanations of statistical tests
  6. Security keys and login information

Answer(s): C

Explanation:

Senior leaders need actionable insights and the overarching outcomes, not the implementation details, so you present your key recommendations alongside a summary of results at a high level.



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.



Viewing Page 2 of 12



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