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CompTIA DataAI Certification Exam Sample Questions (Q16-Q21):

NEW QUESTION # 16
The most likely concern with a one-feature, machine-learning model is high error due to:

Answer: C

Explanation:
A model with only one feature is unlikely to capture the true complexity of the data's underlying relationships, leading to systematic underfitting - i.e., high bias.


NEW QUESTION # 17
A data scientist wants to predict a person's travel destination. The options are:
Which of the following models would best fit this use case?

Answer: A

Explanation:
You need a supervised multiclass classification model to predict one of the four labeled destinations. Linear Discriminant Analysis is designed for such tasks, finding the linear boundaries that best separate the known destination classes.


NEW QUESTION # 18
A movie production company would like to find the actors appearing in its top movies using data from the tables below. The resulting data must show all movies in Table 1, enriched with actors listed in Table 2.

Which of the following query operations achieves the desired data set?

Answer: C

Explanation:
# A LEFT JOIN ensures all rows from Table 1 (Top Movies) are preserved, even if there's no matching actor data in Table 2. This matches the requirement to show all movies, enriched with actor information when available.
Why the other options are incorrect:
* A: INNER JOIN would exclude movies without matching actor entries.
* B: UNION combines distinct rows - not appropriate for matching columns between two tables.
* C: INTERSECT shows only common movies - excludes unmatched top movies.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 5.2:"LEFT JOINs are used when all records from one table (primary) must be retained, even if there are no matching rows in the secondary table."
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NEW QUESTION # 19
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: D

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 # 20
Which of the following methods should a data scientist use just before switching to a potential replacement model?

Answer: A

Explanation:
A/B testing lets you compare the current model against the candidate in parallel, measuring performance on live data, before fully switching to the new model.


NEW QUESTION # 21
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