The Digital Transformation Technology Professional course provides IT professionals with knowledge of the fundamentals of digital transformation. It covers fundamental concepts of cloud computing, blockchain and IoT ensuring that there is a strong baseline knowledge of key components within digital transformation, allowing it to be put into practice.
As authorized partners of Arcitura, TechnoEdge Learning uses the official curriculum to prepare for the Digital Transformation Technology Professional Certification Exam, which can be taken through Pearson Vue online. The completion of the Certified Digital Transformation Technology Professional course and passing the Digital Transformation Technology Professional Certification Exam (DT90.TEC) proves proficiency in foundational digital transformation practices as they are utilized to implement digital transformation within an organization.
The course includes a 12-month subscription to the following digital course materials:
- Video Lessons (modules 1, 2, 3, 4 & 5)
- Workbook (modules 1, 2, 3, 4 & 5)
- Exam Preparation Guide (modules 1, 2, 3, 4 & 5)
- Mind Map Poster (modules 1, 2, 3, 4 & 5)
- Flashcards (modules 1, 2, 3, 4 & 5)
- Symbol Legend Poster (modules 4 & 5)
- Blockchain Models Poster
- Consensus Types Poster
- IoT Networking Supplement
- IoT Business Domains Poster
- Pearson Vue Digital Transformation Technology Professional Certification Exam (DT90.TEC) Exam Voucher
Arcitura Certified Digital Transformation Technology Professional Course Outline
Module 1: Fundamental Digital Transformation
- Understanding digital transformation
- Benefits of digital transformation
- Challenges of digital transformation
- Digital transformation business and technology drivers
- Understanding customer-centricity
- Product-centric vs. customer-centric relationships
- Relationship-value actions and warmth
- Omni-channel customer interactions
- Customer journeys and customer data intelligence
- Data intelligence basics
- Data origins and data sources
- Data collection methods and data utilization types
- Intelligent decision-making
- Computer- assisted manual decision-making and conditional automated decision-making
- Intelligent manual decision-making vs. intelligent automated decision-making
- Direct-driven automated decision-making and periodic automated decision-making
- Realtime automated decision-making
Module 2: Digital Transformation in Practice
- Understanding digital transformation solutions and distributed solution design basics
- Data ingress basics, including File Pull, File Push, API Pull, API push and data streaming
- An introduction to digital transformation automation technologies
- Cloud computing basics and cloud computing as part of digital transformation solutions
- Common cloud computing risks and challenges
- Blockchain basics and blockchain as part of digital transformation solutions
- Common blockchain risks and challenges
- Internet of things (IoT) basics and IoT as part of digital transformation solutions
- Common IoT risks and challenges
- Robotic process automation (RPA) and RPA as part of digital transformation solutions
- Common RPA risks and challenges
- An introduction to digital transformation data science technologies
- Big data and data analytics and big data as part of digital transformation solutions
- Common big data risks and challenges
- Machine learning basics and machine learning as part of digital transformation solutions
- Common machine learning risks and challenges
- Artificial intelligence (AI) basics and AI as part of digital transformation solutions
- Common AI risks and challenges
- Inside a customer-centric digital transformation solution (a comprehensive, step-by-step exploration)
- Mapping individual digital transformation technologies to solution processing
- Tracking how data intelligence is collected and used in a digital transformation solution
Module 3: Fundamentals of Cloud Computing
- Fundamental cloud computing terminology and concepts
- Basics of virtualization
- Specific characteristics that define a cloud
- Understanding elasticity, resiliency, on-demand and measured usage
- Benefits, challenges and risks of contemporary cloud computing platforms and cloud services
- Understanding the software as a service (SaaS) cloud delivery model
- Understanding the platform as a service (PaaS) cloud delivery model
- Understanding the infrastructure as a service (IaaS) cloud delivery model
- Cloud computing mechanisms that establish architectural building blocks
- Virtual servers, containers, ready-made environments, failover systems & pay-per-use monitors
- Automated scaling listeners, multi-device brokers & resource replication
- Understanding how individual cloud computing mechanisms support cloud characteristics
- An introduction to containerization, container hosting & logical pod containers
- A comparison of containerization and virtualization
- Cloud balancing and cloud bursting architectures
Module 4: Fundamental Blockchain
- Benefits and challenges of blockchain
- Blockchain business drivers and technology drivers
- Understanding blockchain’s decentralized model
- Blockchain value propositions
- How blockchain can be used for different industries
- Blockchain applications, networks and the distributed ledger
- How the distributed ledger can relate to relational database
- Fundamental components of a blockchain architecture
- Transactions, records and pools
- Blocks, chains and block headers
- Blockchain users, full nodes and partial nodes
- Step-by-step understanding of the record and block lifecycle
- Step-by-step understanding of how the merkle tree sorks
- Step-by-step understanding of how consensus works
- Consensus algorithms (PoW, PoS, PoA, DPoS, LPoS, PoI, PoET, PoC, PoB, round robin)
- Public vs. private / permissionless vs. permissioned blockchains
- Coins, tokens, smart contracts
- Basics of crypto hashing and cryptography
- On-chain, off-chain and cross-chain activity
- Common blockchain metrics
Module 5: Fundamental IOT
- Understanding things, connectivity, data, processing, commands and business analytics
- IoT business and technology drivers, benefits and challenges
- Miniaturization and nanotechnology
- IoT connectivity and contextual realtime data
- IoT business domains (personal, home, enterprise, utilities, mobile)
- IoT vs. the internet
- Resource-constrained devices and low-power wide-area networks (LPWANs)
- Active and passive devices (including RFID)
- Telemetry and command data
- Sensors (mechanical, resistive, optical, ranging, MEMS)
- Microcontrollers, rirmware and power sources
- IoT Gateways and common gateway functions
- Introduction to edge and fog computing
- IoT platforms and common platform functions
- IoT architecture layers and action modeling
- Key IoT architecture design considerations
- Radio transports (leased vs. unleased, high band vs. low band)
- IoT messaging with REST, HTTP and the constrained application protocol (CoAp)
- REST properties and constraints with IoT and CoAp
- HTTP resource identifiers, media types and method with IoT and CoAps
- IoT publish-and-subscribe and MQ telemetry transport (MQTT)
- Non-binary data serialization for IoT with JSONs
- Binary data serialization for IoT with protocol buffers
Who Should Take the Course
- Digital Transformation Project Manager
- Digital Transformation Manager
- Digital Strategy Consultant
- Digital Strategy Lead
