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| Section | Weight | Objectives |
|---|---|---|
| Modeling, Analysis, and Outcomes | 24% | - Predictive and prescriptive analytics - Model selection and evaluation metrics - Feature engineering and selection - Data preparation and exploratory data analysis - Result interpretation and business communication |
| Operations and Processes | 22% | - Data governance and quality management - Security and compliance in data operations - Model deployment and monitoring - Data pipeline design and maintenance - Version control and reproducibility |
| Mathematics and Statistics | 17% | - Linear algebra fundamentals - Bayesian reasoning and modeling - Probability theory and distributions - Calculus and optimization concepts - Statistical inference and hypothesis testing |
| Specialized Applications of Data Science | 13% | - Natural Language Processing (NLP) - Computer Vision - Industry-specific analytics use cases - Time-series analysis - Anomaly detection and signal processing |
| Machine Learning | 24% | - Ethics and bias in machine learning - Algorithm selection and implementation - Deep learning fundamentals - Hyperparameter tuning and optimization - Supervised, unsupervised, and reinforcement learning |
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質問 # 41
During EDA, a data scientist wants to look for patterns, such as linearity, in the dat a. Which of the following plots should the data scientist use?
正解：D
解説：
Scatter plots display pairs of numeric values on two axes, letting you visually assess relationships and patterns, such as linear trends, between variables.
質問 # 42
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?
正解：A
解説：
# 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."
質問 # 43
Which of the following is best solved with graph theory?
正解：C
解説：
# The Traveling Salesman Problem (TSP) is a classic example in graph theory. It involves finding the shortest path that visits a set of nodes (cities) and returns to the starting point. Graph theory is used to model nodes (cities) and edges (paths between cities).
Why other options are incorrect:
* A: OCR is a computer vision problem - best handled with CNNs or ML image models.
* C: Fraud detection can involve graph-based approaches but is typically solved using anomaly detection or classification.
* D: One-armed bandit is a reinforcement learning problem - not related to graph theory.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 3.4:"Graph theory is frequently used in routing and path optimization problems such as the Traveling Salesman."
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質問 # 44
Which of the following describes the appropriate use case for PCA?
正解：D
解説：
Principal Component Analysis transforms correlated features into a smaller set of uncorrelated components that capture most of the variance, making it ideal for reducing dimensionality before modeling or visualization.
質問 # 45
A data analyst is examining the correlation matrix of a new data set to identify issues that could adversely impact model performance. Which of the following is the analyst most likely checking for?
正解：B
解説：
# Multicollinearity occurs when independent variables are highly correlated with each other. This can distort coefficient estimates and reduce model interpretability. A correlation matrix is the primary tool used to detect it.
Why the other options are incorrect:
* A & C: Under/oversampling relate to class imbalance, not variable correlation.
* D: Overfitting is related to model complexity, not directly observable via a correlation matrix.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 3.2:"Correlation matrices are used to detect multicollinearity - high correlations among predictors that may destabilize models."
質問 # 46
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BONUS！！！ Jpexam DY0-001ダンプの一部を無料でダウンロード：https://drive.google.com/open?id=13x-ilNBu-Feu6fUkaSw3lSZTAcD6kjdr