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Valid DY0-001 Test Preparation | DY0-001 Exam Practice

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Valid DY0-001 Test Preparation | DY0-001 Exam Practice

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CompTIA DataX Certification Exam Sample Questions (Q35-Q40):NEW QUESTION # 35
A client has gathered weather data on which regions have high temperatures. The client would like a visualization to gain a better understanding of the data.
INSTRUCTIONS
Part 1
Review the charts provided and use the drop-down menu to select the most appropriate way to standardize the data.
Part 2
Answer the questions to determine how to create one data set.
Part 3
Select the most appropriate visualization based on the data set that represents what the client is looking for.
If at any time you would like to bring back the initial state of the simulation, please click the Reset All button.

















Answer:
Explanation:
See explanation below.
Explanation:
Part 1
Select Table 2. Table 2 contains mixed temperature scales (°F and °C) that must be standardized before visualization.
Variable: Temperature/scale
Action: Correct
Value to correct: 50 °C

Part 2
Method: Data matching
Join variable: Zip code
You need to merge the two tables by aligning matching records, which is a data-matching (join) operation, and ZIP code is the shared, uniquely identifying field linking each region's weather reading to its city.

Part 3
Choose the choropleth map (the first option).
A choropleth map best shows geographic variation in temperature by coloring each state (or region) according to its recorded value. This lets the client immediately see where the highest and lowest temperatures occur across the U.S. without distracting elements like bubble size or combined chart axes.


NEW QUESTION # 36
A data scientist is designing a real-time machine-learning model that classifies a user based on initial behavior. The run times of these models are provided in the following table:

Which of the following models should the data scientist recommend for deployment?
  • A. Random forest
  • B. XGBoost
  • C. Decision trees
  • D. Artificial neural network
Answer: B
Explanation:
# In real-time systems, low latency (short run time) is critical. While the Artificial Neural Network provides the highest accuracy, its 12-minute runtime makes it unsuitable for real-time inference. Random forest is the fastest but offers the lowest accuracy.
XGBoost provides an excellent balance between runtime (5 minutes) and accuracy (90%). It's well-optimized for performance and scalability, and thus is a strong candidate for real-time classification when balancing both efficiency and predictive quality.
Why the other options are less ideal:
* B: Random forest is faster but significantly less accurate.
* C: Decision trees have longer run time than XGBoost with only a 2% accuracy improvement.
* D: Artificial neural network has the highest accuracy but is too slow for real-time applications.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 4.3:"In real-time applications, model selection involves a trade-off between accuracy and inference speed. XGBoost offers competitive accuracy with efficient runtime."
* Machine Learning Systems Design Guide, Chapter 7:"XGBoost is well-suited for real-time systems due to its balance of model complexity and fast prediction times."
-

NEW QUESTION # 37
Which of the following distance metrics for KNN is best described as a straight line?
  • A. Radial
  • B. Cosine
  • C. Euclidean
  • D. Manhattan
Answer: C
Explanation:
# Euclidean distance is the most intuitive distance metric. It measures the shortest "straight-line" distance between two points in Euclidean space. This is typically used in KNN and clustering when features are continuous and appropriately scaled.
Why the other options are incorrect:
* A: "Radial" isn't a standard distance metric; may refer vaguely to radial basis functions.
* C: Cosine measures the angle (orientation) between vectors - not straight-line distance.
* D: Manhattan distance sums the absolute differences across dimensions - visualized as block-like (taxicab) paths, not direct lines.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 4.4:"Euclidean distance is the default metric in KNN for measuring straight-line proximity in feature space."
* Data Mining Techniques, Chapter 3:"Euclidean distance represents the shortest path between two points and is widely used in distance-based learning algorithms."
-

NEW QUESTION # 38
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?
  • A. JSON
  • B. REST
  • C. SOAP
  • D. RPC
Answer: B
Explanation:
# REST (Representational State Transfer) is a web-based API style that is widely adopted for its simplicity, scalability, and use of standard HTTP methods (GET, POST, PUT, DELETE). It is stateless and can be consumed easily by multiple systems and departments with minimal integration work.
Why the other options are incorrect:
* A: SOAP is heavy, XML-based, and requires more development overhead.
* B: RPC is lower-level and not well-suited for scalable, modern web services.
* C: JSON is a data format, not an API protocol.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 5.4 (API and Model Deployment):"REST APIs are preferred for exposing models to various consumers due to their simplicity, platform-agnostic nature, and use of standard HTTP."
* Data Engineering Design Patterns, Section 6:"RESTful services enable easy integration of machine learning models with front-end and enterprise systems." 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.

NEW QUESTION # 39
Which of the following describes the appropriate use case for PCA?
  • A. Dimensionality reduction
  • B. Regression
  • C. Recommendation
  • D. Classification
Answer: A
Explanation:
# Principal Component Analysis (PCA) is an unsupervised technique used to reduce the dimensionality of large datasets by transforming correlated features into a smaller set of uncorrelated components (principal components) while retaining the most variance.
Why the other options are incorrect:
* B: Classification is a predictive modeling task; PCA is not inherently predictive.
* C: Regression models numerical relationships; PCA does not predict outcomes.
* D: Recommendation systems use collaborative or content filtering, not PCA directly.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 3.3:"PCA is primarily used for reducing the number of variables while preserving data structure and minimizing information loss."
* Pattern Recognition and Machine Learning, Chapter 12:"PCA identifies principal axes of variation and is widely used in preprocessing for dimensionality reduction."
-

NEW QUESTION # 40
......
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