DT-DS Exam Information and Outline
Digital Transformation Data Scientist
Syllabus Update Checked on: November 10, 2026
DT-DS Exam Syllabus & Study Guide
Before you start practicing with our exam simulator, it is essential to understand the
official DT-DS exam objectives. This course outline serves as your roadmap.
The information below reflects the 2026 syllabus defined by
Arcitura.
Below are complete topics detail with latest syllabus and course outline, that will help you good knowledge about exam objectives and topics that you have to prepare. These contents are covered in questions and answers pool of exam.
- Exam Format
- Online-proctored multiple-choice certification exam (Pearson VUE) covering the modules below
- Practice exam questions provided with the certification bundle
- Note: Machine Learning modules may be replaced by the equivalent Predictive AI modules (alternatives)
- Module 1: Fundamental Digital Transformation
- Benefits, challenges, business and technology drivers
- Customer-centricity, omni-channel interaction and customer journeys
- Data intelligence: sources, collection and utilization
- Manual vs. automated and real-time decision-making
- Module 2: Digital Transformation in Practice
- Distributed solution design and data ingress (file, API, streaming)
- Cloud, blockchain, IoT, RPA, big data, ML and AI with their risks
- Step-by-step customer-centric DT solution
- Module 3: Fundamental Big Data Analysis & Analytics
- Big data fundamentals and analysis lifecycle
- Business intelligence and data visualization
- Regression, clustering, classification and text analytics
- Module 4: Fundamental Machine Learning
- ML drivers, benefits and usage scenarios
- Data types and the ML process: collection, pre-processing, training, deployment
- Supervised, unsupervised, semi-supervised and reinforcement learning
- Classification, regression, decision trees, ensembles, clustering, dimension reduction
- Module 5: Fundamental Generative AI
- Business drivers, benefits, risks and problem categories
- Generative models, algorithms and neural networks
- GANs, VAEs and Transformers
- Steps to building AI systems and best practices
- Module 6: Advanced Big Data Analysis & Analytics
- Statistics, exploratory and confirmatory data analysis
- Model evaluation, decision trees, association rules
- Outlier detection and time series
- Module 7: Advanced Machine Learning
- Data exploration: central tendency, variability, graphical summaries
- Feature selection and extraction; data wrangling
- Supervised and unsupervised patterns
- Model evaluation and optimization: ensembles, retraining, incremental learning
- Module 8: Advanced Generative AI
- Output control: noise and temperature; ethical oversight
- Pre-trained models and transfer learning
- GANs, VAEs, Transformers, cGANs and RNNs/LSTMs
- Large language models and NLP
- Model evaluation, optimization and avoiding overfitting