TensorFlow Assessment

Online assessment to evaluate advanced TensorFlow modeling and deployment expertise

This assessment helps organizations evaluate a candidate’s ability to design, train, and scale models using TensorFlow in production environments. It focuses on advanced-level concepts such as distributed training with MirroredStrategy, data pipeline optimization, memory management, and serving with TensorFlow Serving. The test also assesses candidates’ understanding of explainability tools, model governance, and fairness monitoring. Ideal for data scientists, ML engineers, and TensorFlow developers, this assessment identifies candidates who can manage robust and scalable ML workflows. Automated scoring ensures efficient, data-driven hiring.

Sample Question

You lead a TensorFlow project predicting weather anomalies from satellite imagery. Local training succeeds, but multi-GPU training shows diverging losses between replicas. Logs show asynchronous gradient reductions through custom code, creating inconsistent updates. You must design a production-ready distributed training approach that maintains synchronization and high GPU utilization.