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Test DY0-001 Valid | Latest DY0-001 Test CramOur experts are researchers who have been engaged in professional qualification CompTIA DataX Certification Exam DY0-001 exams for many years and they have a keen sense of smell in the direction of the examination. Therefore, with our DY0-001 Study Materials, you can easily find the key content of the exam and review it in a targeted manner so that you can successfully pass the CompTIA DY0-001 exam. CompTIA DataX Certification Exam Sample Questions (Q60-Q65):NEW QUESTION # 60
The most likely concern with a one-feature, machine-learning model is high error due to:
A. bias
B. probability
C. variance
D. dimensionality
Answer: A
Explanation:
# A one-feature model is likely to be overly simplistic and may not capture the true complexity of the target variable. This leads to underfitting, which is associated with high bias - the model consistently misses the mark regardless of the data.
Why the other options are incorrect:
* B: High dimensionality is not a concern in this case - the model has too few features.
* C: Variance refers to overfitting - more common in overly complex models.
* D: Probability is a modeling technique, not a source of error.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 4.2:"Models with insufficient features tend to underfit and exhibit high bias due to their inability to represent complex relationships."
* Bias-Variance Tradeoff - Data Science Textbook:"A high-bias model makes strong assumptions and is typically too simple to capture the underlying patterns in data."
NEW QUESTION # 61
Which of the following issues should a data scientist be most concerned about when generating a synthetic data set?
A. The data set not being representative of the population
B. The data set having insufficient features
C. The data set having insufficient row observations
D. The data set consuming too many resources
Answer: A
Explanation:
# When generating synthetic data, the key concern is ensuring it accurately reflects the characteristics of the real-world population. A non-representative synthetic dataset may lead to biased models and invalid conclusions.
Why the other options are incorrect:
* A: Resource usage is a technical concern but not as critical as representativeness.
* B: Feature set can often be replicated or engineered - quality matters more.
* C: Synthetic datasets can be scaled up easily - representativeness is harder to validate.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 5.4:"Synthetic data must maintain representational fidelity to the original population in order to be useful for modeling or validation."
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NEW QUESTION # 62
During EDA, a data scientist wants to look for patterns, such as linearity, in the data. Which of the following plots should the data scientist use?
A. Violin
B. Box-and-whisker
C. Scatter
D. Q-Q
Answer: C
Explanation:
# Scatter plots are used to examine relationships and trends between two numeric variables. They are especially effective at revealing linear (or nonlinear) patterns, clusters, and outliers.
Why the other options are incorrect:
* A: Violin plots show distribution and density, not relationships.
* B: Box plots compare distributions across groups but don't reveal linearity.
* D: Q-Q plots test normality, not variable relationships.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 1.2:"Scatter plots are commonly used during EDA to identify correlations, linearity, and outliers between two continuous variables."
* Data Science Fundamentals, Chapter 2 - EDA Techniques:"To assess linear trends and relationships, scatter plots provide a direct visual assessment between variables."
NEW QUESTION # 63
Which of the following does k represent in the k-means model?
A. Number of data splits
B. Number of model tests
C. Number of clusters
D. Distance between features
Answer: C
Explanation:
# In k-means clustering, k represents the number of clusters that the algorithm will attempt to form. The algorithm partitions the dataset into k distinct, non-overlapping clusters based on feature similarity. Each cluster has a centroid, and the algorithm aims to minimize the intra-cluster variance.
Why the other options are incorrect:
* A: Number of tests is unrelated to the k-means algorithm.
* B: Data splits refer to cross-validation or train/test splits, not k in k-means.
* D: Distance between features is computed during clustering but is not what "k" represents.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 4.2:"In k-means clustering, k denotes the number of clusters into which the dataset will be partitioned."
* Introduction to Machine Learning, Chapter 6:"The 'k' in k-means specifies how many groupings the algorithm will seek to discover based on proximity in feature space."
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NEW QUESTION # 64
A computer vision model is trained to identify cats on a training set that is composed of both cat and dog images. The model predicts a picture of a cat is a dog. Which of the following describes this error?
A. Error due to reality
B. Sampling error
C. Type II error
D. False positive error
Answer: C
Explanation:
# A Type II error occurs when the model fails to identify a positive instance - in this case, a cat. That is, it incorrectly classifies a cat (positive class) as a dog (negative class). This is also referred to as a false negative.
Why the other options are incorrect:
* A: "Error due to reality" is not a recognized statistical concept.
* B: A false positive would mean misclassifying a dog as a cat (opposite error).
* C: Sampling error refers to discrepancies between the sample and population, not a misclassification.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 1.5:"Type II errors occur when a model incorrectly identifies a true positive as a negative - also known as a false negative."
* Pattern Recognition and Machine Learning, Chapter 9:"In binary classification, a Type II error means failing to detect a positive class instance, leading to a false negative result."
NEW QUESTION # 65
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