Free, original, no-dumps exam prep
CompTIA DataAI (DY0-001)
An advanced, vendor-neutral data science certification, formerly DataX, spanning mathematics, statistics, modeling, machine learning, data science operations, MLOps, and specialized AI applications.
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Answer-first overview
What to know before you study
Use these concise answers as an orientation, then verify registration details on the official provider pages before paying.
What does this credential cover?
An advanced, vendor-neutral data science certification, formerly DataX, spanning mathematics, statistics, modeling, machine learning, data science operations, MLOps, and specialized AI applications.
Who is it for?
Experienced data scientists and machine learning practitioners with substantial hands-on work in statistics, modeling, data pipelines, deployment, and MLOps.
How should I prepare?
Start with the official objective map, study one domain at a time, test the same domain with original practice, and route every missed question back to a lesson or syllabus topic.
Exam snapshot
Current public exam facts
- Former name
- CompTIA DataX
- Duration
- 165 minutes
- Format
- Maximum of 90 questions
- Question types
- Multiple choice and performance-based
- Passing rule
- Pass/fail; no fixed scaled score is published
- Delivery
- Pearson VUE OnVUE or authorized test center
Administrative facts can change. The official provider and testing-vendor pages remain authoritative for prices, availability, policies, languages, and scheduling.
Official-objective map
Domains to study
Weights are shown only when the provider publishes them. They guide study time; they do not predict the exact mix on an individual exam form.
Mathematics and statistics
Statistical methods, probability, modeling, linear algebra, calculus, and temporal models.
Modeling, analysis, and outcomes
EDA, data issues, enrichment, model iteration, evaluation, and results communication.
Machine learning
Foundational concepts, supervised and unsupervised learning, tree-based methods, and deep learning.
Operations and processes
Business context, ingestion, wrangling, the data science lifecycle, DevOps, MLOps, and deployment.
Specialized applications of data science
Optimization, natural language processing, computer vision, and other specialized applications.
Practical study route
Turn the blueprint into practice
- Turn the five-domain blueprint into a checklist and identify the areas where you lack recent hands-on experience.
- Practice mathematical and model-selection reasoning without relying only on memorized definitions.
- Work through lifecycle scenarios from data ingestion and EDA through deployment, monitoring, and communication.
- Use mixed original questions and PBQ-style reasoning prompts to explain tradeoffs and defend recommendations.