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Actual Exam Questions in CompTIA DY0-001 PDF for Quick Preparation
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CompTIA DataX Certification Exam Sample Questions (Q57-Q62):
NEW QUESTION # 57
Which of the following best describes the minimization of the residual term in a ridge linear regression?
- A. e²
- B. 0
- C. e
- D. |e|
Answer: A
Explanation:
# In ridge regression, the model minimizes the sum of squared residuals (errors), with an added penalty term on the magnitude of coefficients (L2 regularization). The residual component specifically is represented by:
# e² (squared error)
Thus, ridge regression minimizes:
Minimize: #(y# # ##)² + ##(#²)
Why the other options are incorrect:
* A: |e| corresponds to L1 loss (used in Lasso).
* B: e represents the error term itself, not its minimized quantity.
* D: Zero error is ideal but practically unachievable and not the actual loss function being minimized.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 1.4:"Ridge regression minimizes the squared error term with an L2 penalty."
* Introduction to Statistical Learning, Chapter 6:"Ridge regression uses squared error loss, which emphasizes larger deviations more heavily than linear loss."
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NEW QUESTION # 58
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. Artificial neural network
- D. Decision trees
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."
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NEW QUESTION # 59
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. Linear discriminant analysis
- B. Principal component analysis
- C. Latent semantic analysis
- D. k-means modeling
Answer: A
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 # 60
Which of the following problem-solving approaches is a set of guidelines to handle highly variable and not fully apparent situations?
- A. Plan
- B. Algorithm
- C. Heuristic
- D. Schedule
Answer: C
Explanation:
# Heuristics are informal rules or guidelines used to solve problems when full information is unavailable or when optimal solutions are computationally impractical. They are often used in complex decision-making and AI.
Why the other options are incorrect:
* A: Schedule refers to timing, not problem-solving.
* B: A plan is a formal structure, not flexible for uncertain conditions.
* D: Algorithms are step-by-step procedures for defined problems - not suited for ambiguity.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 5.1:"Heuristics provide flexible guidance for solving problems with high uncertainty or limited data."
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NEW QUESTION # 61
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's adjusted R² is too low for the real estate industry.
- B. The model fails to improve meaningfully on the benchmark model.
- C. The model's adjusted R² is exceptionally strong for such a complex relationship.
- D. The model should be deployed because it has a lower RMSE.
Answer: B
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 # 62
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