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CompTIA DataX Certification Exam Sample Questions (Q66-Q71):
NEW QUESTION # 66
A data scientist is developing a model to predict the outcome of a vote for a national mascot. The choice is between tigers and lions. The full data set represents feedback from individuals representing 17 professions and 12 different locations. The following rank aggregation represents 80% of the data set:
(Screenshot shows survey rankings for just two professions and a few locations, all voting for "Tigers") Which of the following is the most likely concern about the model's ability to predict the outcome of the vote?
Answer: A
Explanation:
# Extrapolated data refers to making predictions about data points that fall outside the observed range or distribution. Since the sample data (80%) is heavily skewed toward a small subset of professions and locations, predicting results for the remaining, unrepresented professions and regions involves extrapolation.
Why the other options are incorrect:
* A: Interpolation occurs within the bounds of observed data - not the issue here.
* C: In-sample data refers to training data, which is overrepresented in this case.
* D: Out-of-sample data is a concern in generalization but extrapolation is more specific here.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 3.2:"Extrapolation introduces risk when models are used outside the range of data they were trained on, especially if certain subgroups are underrepresented."
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NEW QUESTION # 67
In a modeling project, people evaluate phrases and provide reactions as the target variable for the model.
Which of the following best describes what this model is doing?
Answer: C
Explanation:
# Sentiment analysis refers to using machine learning or NLP techniques to determine the sentiment or emotional tone behind a body of text (e.g., positive, neutral, or negative). When people provide reactions to phrases, the model is learning to associate language with subjective emotion or opinion.
Why the other options are incorrect:
* B: NER identifies entities (e.g., locations, organizations) - not emotions.
* C: TF-IDF is a feature engineering method, not a modeling goal.
* D: POS tagging classifies words by their grammatical function - not sentiment.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 6.3:"Sentiment analysis models associate textual input with subjective labels, such as emotional response or polarity."
* Applied Text Analytics, Chapter 8:"When modeling user reactions to text, sentiment classification techniques are commonly employed."
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NEW QUESTION # 68
A data scientist is working with a data set that covers a two-year period for a large number of machines. The data set contains:
* Machine system ID numbers
* Sensor measurement values
* Daily timestamps for each machine
The data scientist needs to plot the total measurements from all the machines over the entire time period.
Which of the following is the best way to present this data?
Answer: C
Explanation:
# Line plots are ideal for visualizing data trends over continuous time. In this case, plotting the total daily measurements across a two-year period is a time series task, and a line plot shows progression and pattern over time clearly.
Why the other options are incorrect:
* A: Scatter plots are better for relationship exploration, not time trends.
* C: Histograms display distribution - not suitable for continuous time trends.
* D: Box plots show spread and outliers - not temporal behavior.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 1.2:"Use line plots for visualizing temporal trends in time-series data."
* Time Series Visualization Guide, Chapter 2:"Line plots are effective for showing cumulative or aggregated values over time."
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NEW QUESTION # 69
An analyst is examining data from an array of temperature sensors and sees that one sensor consistently returns values that are much higher than the values from the other sensors. Which of the following terms best describes this type of error?
Answer: C
Explanation:
# A systematic error is a consistent, repeatable error caused by faulty equipment or flawed measurement techniques. Since one sensor consistently over-reports values, this is a classic case of systematic error.
Why the other options are incorrect:
* A: Synthetic data is artificially generated - unrelated to sensor malfunction.
* C: Heteroskedasticity refers to non-constant variance - not consistent bias.
* D: Idiosyncratic errors are random and unpredictable - not consistent.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 1.4:"Systematic errors arise from consistent biases in measurement devices or methods, requiring calibration or correction."
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NEW QUESTION # 70
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?
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 # 71
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