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CompTIA DY0-001 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|
| Topic 1: Mathematics and Statistics | 17% | - Statistical Methods and Concepts
- 1. Correlation coefficients (Pearson, Spearman)
- 2. Gini index, Entropy, Information gain
- 3. Confusion matrix and Classifier metrics (Accuracy, Recall, Precision, F1, MCC)
- 4. t-tests, Chi-squared test, ANOVA, Hypothesis testing
- 5. Distributions, Skewness, Kurtosis
- 6. Regression performance metrics (R2, RMSE, F statistic)
- 7. Confidence intervals, p-value, Type I and Type II errors
- 8. Central limit theorem, Law of large numbers
- Applied Mathematics
- 1. Probability Density Function (PDF), PMF, CDF
- 2. Calculus
- 3. Linear Algebra
|
| Topic 2: Modeling, Analysis, and Outcomes | 24% | - Model Lifecycle
- 1. Performance Evaluation and Benchmarking
- 2. Time Series, Longitudinal Studies, Causal Inference
- 3. Model Selection and Requirements Validation
- Data Analysis Techniques
- 1. Univariate and Multivariate Analysis
- 2. Exploratory Data Analysis (EDA)
- 3. Visualization (Box plots, Scatter plots, Heatmaps, Sankey diagrams)
- Feature Engineering & Transformation
- 1. Handling missingness and Oversampling
- 2. Data transformation (Geocoding, Scaling, Standardization)
- 3. Feature type identification
|
| Topic 3: Operations and Processes | 22% | - MLOps & Deployment
- 1. CI/CD, Model deployment, Container orchestration
- 2. Workflow models, Version control, Clean code, Unit tests
- 3. Deployment environments (Cloud, Hybrid, Edge, On-premises)
- Business & Data Lifecycle
- 1. Data wrangling, Cleaning, Imputation, Ground truth labeling
- 2. Compliance, KPIs, Requirements gathering
- 3. Data types (Synthetic, Public data)
- 4. Ingestion pipelines, Streaming, Batching, Data lineage
|
| Topic 4: Machine Learning | 24% | - Deep Learning & Unsupervised Learning
- 1. Backpropagation, Deep-learning frameworks, Optimizers
- 2. Artificial Neural Networks (ANN), Dropout, Batch Normalization
- 3. Clustering (K-Means, DBSCAN), Dimensionality Reduction (PCA, t-SNE)
- Foundational Concepts
- 1. Cross-validation, Ensemble models, Hyperparameter tuning
- 2. Data leakage prevention
- 3. Loss functions, Bias-variance tradeoff, Regularization
- Supervised & Tree-based Learning
- 1. Decision Trees, Random Forest, Boosting, Bagging
- 2. Linear/Logistic Regression, KNN, Naive Bayes, Association rules
|
| Topic 5: Specialized Applications of Data Science | 13% | - Specialized Domains
- 1. Computer Vision
- 2. Anomaly Detection
- 3. Natural Language Processing (NLP)
|
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최신 CompTIA Data+ DY0-001 무료샘플문제 (Q13-Q18):
질문 # 13
Which of the following describes the appropriate use case for PCA?
- A. Recommendation
- B. Classification
- C. Regression
- D. Dimensionality reduction
정답:D
설명:
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.
질문 # 14
A data scientist is presenting the recommendations from a monthslong modeling and experiment process to the company's Chief Executive Officer. Which of the following is the best set of artifacts to include in the presentation?
- A. Methodology, code snippets, findings, data tables, and p-values
- B. Methods, data overview, results, recommendations, and charts
- C. Recommendation, charts, justifications, code reviews, and results
- D. Results, recommendations, justifications, and clear charts
정답:D
설명:
# For executive-level presentations, the focus should be on strategic outcomes. Therefore, concise results, clear actionable recommendations, visual summaries (charts), and minimal justifications are best. Technical details such as p-values, code, or full methods are too granular.
Why the other options are incorrect:
* A: Too method-heavy for executive audiences.
* C: Includes code reviews - not suitable for a CEO.
* D: Overly technical for high-level stakeholders.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 5.5:"Executive communication should focus on outcome-driven recommendations, high-level insights, and actionable visuals."
* Harvard Business Review - Communicating Data to Executives:"Avoid technical detail. Use visuals and clearly stated recommendations supported by business-focused justifications."
질문 # 15
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. Type II error
- B. Error due to reality
- C. False positive error
- D. Sampling error
정답:A
설명:
Classifying an actual cat (positive instance) as a dog (negative prediction) is a false negative, which corresponds to a Type II error.
질문 # 16
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. Relative strength
- B. Moving average
- C. Autoregressive
- D. Dynamic time warping
정답:C
설명:
An autoregressive model uses past values of the series itself (here, historical daily copper prices) as predictors for future values, making it the most suitable technique when only the time‐series history is available.
질문 # 17
Which of the following belong in a presentation to the senior management team and/or C-suite executives? (Choose two.)
- A. Full literature reviews
- B. High-level results
- C. Detailed explanations of statistical tests
- D. Final recommendations
- E. Code snippets
- F. Security keys and login information
정답:D
설명:
Senior leaders need actionable insights and the overarching outcomes, not the implementation details, so you present your key recommendations alongside a summary of results at a high level.
질문 # 18
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