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CompTIA DataX Certification Exam Sample Questions (Q79-Q84):

NEW QUESTION # 79
A data scientist is analyzing a data set with categorical features and would like to make those features more useful when building a model. Which of the following data transformation techniques should the data scientist use? (Choose two.)

A. Normalization
B. One-hot encoding
C. Linearization
D. Scaling
E. Pivoting
F. Label encoding

Answer: B,F

NEW QUESTION # 80
An analyst wants to show how the component pieces of a company's business units contribute to the company's overall revenue. Which of the following should the analyst use to best demonstrate this breakdown?

A. Residual chart
B. Box-and-whisker chart
C. Sankey diagram
D. Scatter plot matrix

Answer: C

Explanation:
# A Sankey diagram is ideal for illustrating flow-based relationships, such as how different units or sources contribute to a total. It's especially effective for showing proportions, hierarchy, and decomposition - such as revenue contribution by business units.
Why the other options are incorrect:
* A: Box plots show distributions and spread - not contributions or breakdowns.
* C: Scatter plot matrix explores relationships between numeric variables, not part-to-whole relationships.
* D: Residual charts are diagnostic tools for regression - not for revenue visualization.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 5.5:"Sankey diagrams are useful for visualizing contributions, flows, and proportional allocations across categories."
* Data Visualization Best Practices, Chapter 7:"Sankey charts are preferred when tracking contributions from multiple inputs to a unified output."

NEW QUESTION # 81
A data scientist is building a forecasting model for the price of copper. The only input in this model is the daily price of copper for the last ten years. Which of the following forecasting techniques is the most appropriate for the data scientist to use?

A. Autoregressive
B. Moving average
C. Relative strength
D. Dynamic time warping

Answer: A

Explanation:
# An Autoregressive (AR) model is ideal when past values of a time series are used to predict future values.
Since the only input is historical price data of copper, AR is the most appropriate technique.
Why the other options are incorrect:
* B: Moving average smooths noise but doesn't model the dependencies for prediction.
* C: Dynamic time warping is used for measuring similarity between time series, not forecasting.
* D: Relative strength is a financial metric used for comparing asset performance - not a forecasting technique.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 3.5:"Autoregressive models are used when the goal is to predict future values based solely on past values in a univariate time series."
* Time Series Analysis and Forecasting, Chapter 5:"AR models capture the temporal dependencies in time series data and are foundational in time-based prediction."
-

NEW QUESTION # 82
Which of the following measures would a data scientist most likely use to calculate the similarity of two text strings?

A. Word cloud
B. String indexing
C. Edit distance
D. k-nearest neighbors

Answer: C

Explanation:
# Edit distance (also known as Levenshtein distance) measures how many single-character edits (insertions, deletions, or substitutions) are needed to transform one string into another. It's a common metric for assessing string similarity, especially in natural language processing (NLP) tasks.
Why the other options are incorrect:
* A: Word clouds visualize word frequency, not similarity.
* C: String indexing is a method for referencing string positions, not comparison.
* D: k-NN is a classification algorithm, not a string similarity measure.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 6.3:"Edit distance is a key similarity metric in text comparison tasks, particularly in cleaning or matching string records."
-

NEW QUESTION # 83
A data scientist is clustering a data set but does not want to specify the number of clusters present. Which of the following algorithms should the data scientist use?

A. Logistic regression
B. k-nearest neighbors
C. k-means
D. DBSCAN

Answer: D

Explanation:
# DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm that does not require specifying the number of clusters in advance. It identifies clusters of arbitrary shape and separates noise/outliers based on density thresholds.
Why other options are incorrect:
* B: k-NN is a supervised classification algorithm, not used for clustering.
* C: k-means requires predefining the number of clusters (k).
* D: Logistic regression is a classification model, not for clustering.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 4.2:"DBSCAN detects clusters based on data density without the need for a predefined k value and handles outliers effectively."
-

NEW QUESTION # 84
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