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DataAI

CompTIA DataAI (formerly DataX) is the premier certification for highly experienced professionals seeking to validate competency in the rapidly evolving field of data science. DataAI equips you with the skills to precisely and confidently demonstrate expertise in handling complex data sets, implementing data-driven solutions, and driving business growth through insightful data interpretation.

Skills you'll learn

Apply mathematical and statistical methods appropriately, including data processing, cleaning, statistical modeling, linear algebra, and calculus concepts.

Utilize appropriate analysis and modeling methods to make justified model recommendations for modeling, analysis, and outcomes.

Implement machine learning models and understand deep learning concepts to advance data science capabilities.

Implement data science operations and processes effectively to support organizational goals.

Demonstrate an understanding of industry trends and specialized applications of data science in various fields.

Exam Details

Exam version: V1

Exam series code: DY0-001

Launch date: July 25, 2024

Number of questions: maximum of 90 questions

Types of questions: multiple-choice and performance-based

Duration: 165 minutes

Passing score: pass/fail only (no scaled score)

Language: English and Japanese

Recommended experience: 5+ years in data science or a similar role

Retirement: usually three years after launch (estimated 2027)

DataAI (V1) exam objectives summary

  • Statistical methods: applying t-tests, chi-squared tests, analysis of variance (ANOVA), hypothesis testing, regression metrics, gini index, entropy, p-value, receiver operating characteristic/area under the curve (ROC/AUC), akaike information criterion/bayesian information criterion (AIC/BIC), and confusion matrix.
  • Probability and modeling: explaining distributions, skewness, kurtosis, heteroskedasticity, probability density function (PDF), probability mass function (PMF), cumulative distribution function (CDF), missingness, oversampling, and stratification.
  • Linear algebra and calculus: understanding rank, eigenvalues, matrix operations, distance metrics, partial derivatives, chain rule, and logarithms.
  • Temporal models: comparing time series, survival analysis, and causal inference.

  • EDA methods: using exploratory data analysis (EDA) techniques like univariate and multivariate analysis, charts, graphs, and feature identification.
  • Data issues: analyzing sparse data, non-linearity, seasonality, granularity, and outliers.
  • Data enrichment: applying feature engineering, scaling, geocoding, and data transformation.
  • Model iteration: conducting design, evaluation, selection, and validation.
  • Results communication: creating visualizations, selecting data, avoiding deceptive charts, and ensuring accessibility.

  • Foundational concepts: applying loss functions, bias-variance tradeoff, regularization, cross-validation, ensemble models, hyperparameter tuning, and data leakage.
  • Supervised learning: applying linear regression, logistic regression, k-nearest neighbors (KNN), naive bayes, and association rules.
  • Tree-based learning: applying decision trees, random forest, boosting, and bootstrap aggregation (bagging).
  • Deep learning: explaining artificial neural networks (ANN), dropout, batch normalization, backpropagation, and deep-learning frameworks.
  • Unsupervised learning: explaining clustering, dimensionality reduction, and singular value decomposition (SVD).

  • Business functions: explaining compliance, key performance indicators (KPIs), and requirements gathering.
  • Data types: explaining generated, synthetic, and public data.
  • Data ingestion: understanding pipelines, streaming, batching, and data lineage.
  • Data wrangling: implementing cleaning, merging, imputation, and ground truth labeling.
  • Data science life cycle: applying workflow models, version control, clean code, and unit tests.
  • DevOps and MLOps: explaining continuous integration/continuous deployment (CI/CD), model deployment, container orchestration, and performance monitoring.
  • Deployment environments: comparing containerization, cloud, hybrid, edge, and on-premises deployment.

  • Optimization: comparing constrained and unconstrained optimization.
  • NLP concepts: explaining natural language processing (NLP) techniques like tokenization, embeddings, term frequency-inverse document frequency (TF-IDF), topic modeling, and NLP applications.
  • Computer vision: explaining optical character recognition (OCR), object detection, tracking, and data augmentation.
  • Other applications: explaining graph analysis, reinforcement learning, fraud detection, anomaly detection, signal processing, and others.

  • Deployment issues: diagnosing and resolving deployment problems.
  • Network connectivity: troubleshooting connectivity issues in cloud environments.
  • Security incidents: addressing issues like leaked credentials and privilege escalation.
  • Service disruptions: resolving disruptions in services like DNS, DHCP, and NTP.
  • Misconfigurations: identifying and fixing misconfigurations in cloud setups.

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