DY0-001 DUMPS DOWNLOAD, DY0-001 PRACTICE EXAMS FREE

DY0-001 Dumps Download, DY0-001 Practice Exams Free

DY0-001 Dumps Download, DY0-001 Practice Exams Free

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CompTIA DataX Certification Exam Sample Questions (Q31-Q36):

NEW QUESTION # 31
A data scientist uses a large data set to build multiple linear regression models to predict the likely market value of a real estate property. The selected new model has an RMSE of 995 on the holdout set and an adjusted R² of 0.75. The benchmark model has an RMSE of 1,000 on the holdout set. Which of the following is the best business statement regarding the new model?

  • A. The model fails to improve meaningfully on the benchmark model.
  • B. The model should be deployed because it has a lower RMSE.
  • C. The model's adjusted R² is too low for the real estate industry.
  • D. The model's adjusted R² is exceptionally strong for such a complex relationship.

Answer: A

Explanation:
# The difference between the benchmark RMSE (1,000) and the new model RMSE (995) is minimal and may not justify replacing the existing model. Though the adjusted R² is decent, business decisions should be based on whether the improvement is statistically and practically significant.
Why the other options are incorrect:
* A: The RMSE improvement is marginal and may not be worth deployment effort.
* B: The adjusted R² of 0.75 is moderate, not necessarily "exceptionally strong."
* D: The claim about industry standards is unsupported and not universally true.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 3.2:"Model selection must consider both statistical improvement and practical significance."
* Data Science Best Practices, Chapter 8:"Small improvements in performance metrics must be evaluated in the context of deployment cost and business impact."
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NEW QUESTION # 32
A data scientist wants to predict a person's travel destination. The options are:
* Branson, Missouri, United States
* Mount Kilimanjaro, Tanzania
* Disneyland Paris, Paris, France
* Sydney Opera House, Sydney, Australia
Which of the following models would best fit this use case?

  • A. Principal component analysis
  • B. Linear discriminant analysis
  • C. Latent semantic analysis
  • D. k-means modeling

Answer: B

Explanation:
# Linear Discriminant Analysis (LDA) is a supervised classification method used to predict a categorical target (such as travel destination) based on multiple input features. It models decision boundaries between classes - which is appropriate when predicting a fixed set of destinations.
Why the other options are incorrect:
* B: k-means is unsupervised and doesn't use labeled output like travel destination.
* C: Latent Semantic Analysis is used for extracting relationships from textual data - not categorical prediction.
* D: PCA reduces dimensionality but doesn't classify.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 4.1:"Linear Discriminant Analysis is used when the response variable is categorical and the objective is classification."
* Classification Techniques Guide, Chapter 7:"LDA excels in multi-class prediction when the input data is continuous and the output is a known category."
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NEW QUESTION # 33
A data scientist is performing a linear regression and wants to construct a model that explains the most variation in the data. Which of the following should the data scientist maximize when evaluating the regression performance metrics?

  • A. p value
  • B. AUC
  • C. R²
  • D. Accuracy

Answer: C

Explanation:
# R² (coefficient of determination) quantifies how much of the variance in the dependent variable is explained by the model. A higher R² means a better fit to the data, making it the metric to maximize for explanatory power in regression analysis.
Why the other options are incorrect:
* A: Accuracy is used in classification, not regression.
* C: p-values test statistical significance of coefficients, not overall model fit.
* D: AUC (Area Under the Curve) applies to classification models, not regression.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 3.2:"R² is a regression performance metric indicating the proportion of variance explained by the independent variables."


NEW QUESTION # 34
A data scientist is attempting to identify sentences that are conceptually similar to each other within a set of text files. Which of the following is the best way to prepare the data set to accomplish this task after data ingestion?

  • A. One-hot encoding
  • B. Embeddings
  • C. Sampling
  • D. Extrapolation

Answer: B

Explanation:
# Embeddings (e.g., word2vec, sentence transformers) are vector representations of text that capture semantic similarity. They allow comparison of conceptual meaning between sentences in a high-dimensional space, which is essential for tasks like semantic similarity or clustering.
Why the other options are incorrect:
* B: Extrapolation predicts values beyond a dataset's range - not relevant here.
* C: Sampling reduces data volume but doesn't aid in similarity analysis.
* D: One-hot encoding captures presence of words but lacks semantic understanding.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 6.3:"Embeddings transform text into numeric vectors, enabling similarity computation and semantic analysis."
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NEW QUESTION # 35
A data scientist is standardizing a large data set that contains website addresses. A specific string inside some of the web addresses needs to be extracted. Which of the following is the best method for extracting the desired string from the text data?

  • A. Named-entity recognition
  • B. Regular expressions
  • C. Find and replace
  • D. Large language model

Answer: B

Explanation:
# Regular expressions (regex) are powerful tools for pattern matching in text. They are ideal for extracting substrings, such as domains, parameters, or specific keywords from URLs or structured text fields.
Why the other options are incorrect:
* B: NER is used to extract named entities (like names, places) - not substrings in structured text.
* C: LLMs are overkill and not efficient for simple string matching tasks.
* D: Find and replace is manual and non-scalable for large data sets.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 6.3:"Regular expressions provide a flexible method to extract patterns and substrings in structured or semi-structured text."
* Data Cleaning Handbook, Chapter 3:"Regex is the most effective tool for parsing text formats like URLs, emails, or custom tags."
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NEW QUESTION # 36
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