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CompTIA DataX Certification Exam Sample Questions (Q57-Q62):

NEW QUESTION # 57
Given the equation:

Xt = # + #1Xt#1 + #t, where #t # N(0, ##²)
Which of the following time series models best represents this process?

• A. ARIMA(1,1,1)
• B. ARMA(1,1)
• C. SARIMA(1,1,1) × (1,1,1)1
• D. AR(1)

Answer: D

Explanation:
# The provided equation represents an autoregressive model of order 1 (AR(1)). It describes Xt as a function of its immediately prior value (Xt#1) plus white noise.
Key identifiers:
* No differencing (so not ARIMA).
* No moving average term (so not ARMA).
* No seasonal component (so not SARIMA).
Why the other options are incorrect:
* A: ARIMA(1,1,1) includes integration and MA terms, which are absent here.
* B: ARMA(1,1) includes both AR and MA terms, but only AR is present.
* C: SARIMA involves seasonal and differencing components - not applicable here.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 3.5:"AR(p) models describe a variable as dependent on its previous values with no differencing or moving average."
* Time Series Analysis Textbook, Chapter 4:"Xt = #Xt-1 + #t describes an AR(1) process when #t is white noise."
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NEW QUESTION # 58
A data scientist is merging two tables. Table 1 contains employee IDs and roles. Table 2 contains employee IDs and team assignments. Which of the following is the best technique to combine these data sets?

• A. inner join between Table 1 and Table 2
• B. outer join between Table 1 and Table 2
• C. left join on Table 1 with Table 2
• D. right join on Table 1 with Table 2

Answer: A

Explanation:
# An inner join returns only those records that have matching keys (employee IDs in this case) in both tables.
Since each table provides a different attribute for the same entity (employee), an inner join is the most efficient and accurate method when focusing on employees present in both tables.
Why the other options are less ideal:
* B & C: Left or right joins would include unmatched data, which may lead to nulls.
* D: An outer join brings in all records from both tables and fills nulls where no matches exist, which may introduce irrelevant or incomplete entries.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 5.2:"Inner joins are most appropriate when combining datasets with matching keys to retain only relevant, intersecting records."
* SQL for Data Analysts, Chapter 3:"Use inner joins when combining tables on a common key to include only matched data for analysis."
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NEW QUESTION # 59
A model's results show increasing explanatory value as additional independent variables are added to the model. Which of the following is the most appropriate statistic?

• A. Adjusted R²
• B. R²
• C. #²
• D. p value

Answer: A

Explanation:
# Adjusted R² is specifically designed to evaluate the goodness-of-fit of a regression model while adjusting for the number of predictors. Unlike R², which always increases with more variables, adjusted R² penalizes for adding irrelevant predictors and provides a more accurate measure of model quality.
Why the other options are incorrect:
* B: p-values assess significance of individual predictors, not overall model performance.
* C: #² tests are used in categorical data, not regression fit.
* D: R² may be misleading when more variables are added - it always increases or stays the same.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 3.2:"Adjusted R² accounts for the number of predictors, making it suitable for comparing models with different numbers of variables."
* Applied Regression Analysis, Chapter 5:"Adjusted R² is used to judge whether adding predictors actually improves the model beyond overfitting."
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NEW QUESTION # 60
Which of the following describes the appropriate use case for PCA?

• A. Recommendation
• B. Dimensionality reduction
• C. Regression
• D. Classification

Answer: B

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."
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NEW QUESTION # 61
Which of the following techniques enables automation and iteration of code releases?

• A. Virtualization
• B. CI/CD
• C. Code isolation
• D. Markdown

Answer: B

Explanation:
# CI/CD (Continuous Integration / Continuous Deployment) is a DevOps methodology that automates the building, testing, and deployment of code. It allows teams to iteratively release updates and improvements in a reliable and scalable manner.
Why the other options are incorrect:
* A: Virtualization provides environment emulation but doesn't manage code releases.
* B: Markdown is a documentation tool - unrelated to deployment automation.
* C: Code isolation refers to modular programming, not automation pipelines.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 5.3:"CI/CD pipelines streamline model deployment through automation, allowing continuous integration and delivery of updates."
* DevOps for Data Science, Chapter 4:"CI/CD supports fast and reliable code iterations by automatically testing and deploying to production environments."
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NEW QUESTION # 62
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