CompTIA DY0-001 Exam Details & Actual Exam Questions

  • Exam Code/Number: DY0-001
  • Exam Name/Title: CompTIA DataAI Certification Exam
  • Certification Provider: CompTIA
  • Corresponding Certification: CompTIA Data+
  • Exam Questions: 85
  • Updated On: Sep,21 2026
  • Certification Level: Expert

CompTIA DataAI Certification Exam Questions

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CompTIA DY0-001 Exam Overview:

Certification Vendor:CompTIA
Exam Name:CompTIA DataAI Certification Exam (V1)
Exam Number:DY0-001
Real Exam Qty:Up to 90
Related Certifications:CompTIA DataX
Exam Format:Performance-based questions, Multiple-choice
Exam Duration:165 minutes
Passing Score:Pass/Fail (no scaled score)
Available Languages:Japanese, English
Certificate Validity Period:Approximately 3 years from launch (retirement expected around 2027)
Recommended Training:CompTIA CertMaster Learn (DataAI)
CompTIA Official Training Partners
Exam Registration:CompTIA DataAI Official Page
Pearson VUE CompTIA Registration
Sample Questions:CompTIA DY0-001 Sample Questions
Exam Way:Test center or online proctored exam (Pearson VUE)
Pre Condition:Recommended: 5+ years experience in data science, analytics, or related technical roles
Official Syllabus URL:https://www.comptia.org/en-us/certifications/dataai/

CompTIA DY0-001 Exam Syllabus Topics:

SectionWeightObjectives
Mathematics and Statistics17%- Probability and Modeling
  • 1. PDF, PMF, CDF, missing data handling
    • 2. Distributions, skewness, kurtosis
      - Statistical Methods
      • 1. ROC/AUC, AIC/BIC, confusion matrix
        • 2. t-tests, chi-square tests, ANOVA
          • 3. Hypothesis testing and regression metrics
            - Time Series and Causal Models
            • 1. Forecasting and temporal modeling concepts
              - Linear Algebra and Calculus
              • 1. Derivatives, gradients, optimization basics
                • 2. Matrix operations, eigenvalues, rank
                  Operations and Processes22%- Business Context
                  • 1. KPIs and requirements gathering
                    - Data Engineering Concepts
                    • 1. Data ingestion, pipelines, streaming
                      • 2. Data wrangling and cleaning
                        - MLOps and Deployment
                        • 1. Model monitoring and deployment environments
                          • 2. CI/CD pipelines
                            - Data Science Lifecycle
                            • 1. Workflow, version control, testing
                              Modeling, Analysis, and Outcomes24%- Model Development
                              • 1. Model selection, validation, evaluation
                                - Exploratory Data Analysis (EDA)
                                • 1. Feature identification and visualization
                                  • 2. Univariate and multivariate analysis
                                    - Data Issues and Preparation
                                    • 1. Feature engineering and transformation
                                      • 2. Missing data, outliers, sparsity
                                        - Communication of Results
                                        • 1. Visualization best practices
                                          • 2. Avoiding misleading charts
                                            Specialized Applications of Data Science13%- Natural Language Processing (NLP)
                                            • 1. Topic modeling and applications
                                              • 2. Tokenization, embeddings, TF-IDF
                                                - Computer Vision
                                                • 1. OCR, object detection, tracking
                                                  - Optimization and Advanced Methods
                                                  • 1. Constrained and unconstrained optimization
                                                    - Other AI Applications
                                                    • 1. Reinforcement learning, anomaly detection
                                                      • 2. Graph analysis and signal processing
                                                        Machine Learning24%- Supervised Learning
                                                        • 1. KNN and Naive Bayes
                                                          • 2. Linear and logistic regression
                                                            - Tree-Based Models
                                                            • 1. Decision trees, random forests, boosting
                                                              - Unsupervised Learning
                                                              • 1. Clustering and dimensionality reduction
                                                                - Core ML Concepts
                                                                • 1. Bias-variance tradeoff
                                                                  • 2. Cross-validation and regularization
                                                                    - Deep Learning
                                                                    • 1. Neural networks and backpropagation
                                                                      • 2. Dropout and batch normalization


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