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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #135
You are an ML engineer responsible for designing and implementing training pipelines for ML models. You need to create an end-to-end training pipeline for a TensorFlow model. The TensorFlow model…
The correct answer is B. Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. TFX provides a set of standard components for building end-to-end ML pipelines, including data validation and model analysis. Vertex AI Pipelines is a fully managed service for building and orchestrating machine learning pipelines on Google Cloud.
Submitted by kev92· Apr 18, 2026ML pipeline operationalization
Question
You are an ML engineer responsible for designing and implementing training pipelines for ML models. You need to create an end-to-end training pipeline for a TensorFlow model. The TensorFlow model will be trained on several terabytes of structured data. You need the pipeline to include data quality checks before training and model quality checks after training but prior to deployment. You want to minimize development time and the need for infrastructure maintenance. How should you build and orchestrate your training pipeline?
Options
- ACreate the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined
- BCreate the pipeline using TensorFlow Extended (TFX) and standard TFX components.
- CCreate the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined
- DCreate the pipeline using TensorFlow Extended (TFX) and standard TFX components.
How the community answered
(41 responses)- A7% (3)
- B73% (30)
- C5% (2)
- D15% (6)
Explanation
TFX provides a set of standard components for building end-to-end ML pipelines, including data validation and model analysis. Vertex AI Pipelines is a fully managed service for building and orchestrating machine learning pipelines on Google Cloud.
Topics
#ML Pipelines#TensorFlow Extended (TFX)#Data Quality#Model Quality
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