Thingworx Analytics Test

Evaluates the ability to connect and ingest data from IoT devices using ThingWorx Edge SDKs, Remote Things, and RESTful APIs. Covers configuration of data properties, value streams, property bindings, persistence strategies, and integration with industrial protocols (e.g., MQTT, OPC-UA). Real-time data streaming setup and management of time-series ingestion are included.

Focuses on building user-centric mashups using widgets, containers, binding logic, and custom visualizations. Tests layout design, responsive UIs, interactivity, multi-tab navigation, conditional rendering, and best practices for KPI display. Candidates must know how to turn complex data into intuitive, decision-support dashboards using the Mashup Builder.

Covers the creation of reusable Thing Templates, Shapes, and DataShapes to model hierarchical, reusable representations of IoT devices and business entities. Includes relationships (One-to-One, One-to-Many), property inheritance, configuration tables, and structuring scalable models for enterprise deployment. Crucial for building maintainable digital twins.

Examines the full machine learning pipeline using ThingWorx Analytics Builder, including importing training data, selecting features, model evaluation (ROC, RMSE, AUC), and scoring. Also includes understanding insights such as confidence levels, feature impact scores, and deploying predictive services into runtime environments for real-time or batch use cases.

Assesses the creation of event-driven logic using the Rule Engine, Alerts, Subscriptions, Timers, and Services. Includes use of business logic to trigger actions (e.g., notifications, script execution, remote commands) based on conditions and thresholds. Covers workflow chaining, exception handling, and automated decision-making in live systems.

Tests proficiency in configuring the ThingWorx Analytics Server for streaming data applications. Includes processing real-time data with live model scoring, predictive scoring pipelines, latency management, and continuous anomaly detection. Advanced coverage includes feedback loop implementation, drift monitoring, and designing time-sensitive insights for critical operations.

Focuses on enabling system interoperability using REST APIs, WebSocket integrations, external service calls, and broker-based architectures (Kafka, MQTT). Also includes App Key management, authentication, OAuth, external ERP/SCADA connections, and webhook-driven triggers. Candidates must demonstrate ability to build scalable, loosely coupled systems.

Covers advanced use of scripting (JavaScript, Python) for extending analytics capabilities beyond native features. Includes development of custom scoring algorithms, anomaly detection models, integration of external ML libraries, and deployment of Jupyter-based workflows or edge ML use cases. Requires familiarity with statistical methods and custom logic implementation.

Tests capability to design enterprise-grade, fault-tolerant, multi-tenant ThingWorx deployments with horizontal scalability, load balancing, clustering, and high availability. Includes strategies for data governance, access control (RBAC), Explainable AI (SHAP, LIME), compliance (GDPR), and best practices for model transparency, trust, and auditability.