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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #224

You are building a TensorFlow text-to-image generative model by using a dataset that contains billions of images with their respective captions. You want to create a low maintenance, automated workflo

The correct answer is D. Use the TensorFlow Extended (TFX) SDK to create multiple components that use Dataflow and. For an automated, low-maintenance ML workflow involving massive datasets, data processing, and TensorFlow model training/validation, TensorFlow Extended (TFX) SDK provides end-to-end components that integrate seamlessly with Dataflow.

Submitted by tarun92· Apr 18, 2026ML pipeline operationalization

Question

You are building a TensorFlow text-to-image generative model by using a dataset that contains billions of images with their respective captions. You want to create a low maintenance, automated workflow that reads the data from a Cloud Storage bucket collects statistics, splits the dataset into training/validation/test datasets performs data transformations trains the model using the training/validation datasets, and validates the model by using the test dataset. What should you do?

Options

  • AUse the Apache Airflow SDK to create multiple operators that use Dataflow and Vertex AI
  • BUse the MLFlow SDK and deploy it on a Google Kubernetes Engine cluster. Create multiple
  • CUse the Kubeflow Pipelines (KFP) SDK to create multiple components that use Dataflow and
  • DUse the TensorFlow Extended (TFX) SDK to create multiple components that use Dataflow and

How the community answered

(34 responses)
  • A
    6% (2)
  • B
    9% (3)
  • C
    3% (1)
  • D
    82% (28)

Why each option

For an automated, low-maintenance ML workflow involving massive datasets, data processing, and TensorFlow model training/validation, TensorFlow Extended (TFX) SDK provides end-to-end components that integrate seamlessly with Dataflow.

AUse the Apache Airflow SDK to create multiple operators that use Dataflow and Vertex AI

Apache Airflow can orchestrate workflows but requires more manual setup and integration of individual components compared to the opinionated, TensorFlow-specific framework of TFX.

BUse the MLFlow SDK and deploy it on a Google Kubernetes Engine cluster. Create multiple

MLFlow focuses primarily on ML lifecycle management (experiment tracking, model packaging) but is not a full-fledged pipeline orchestration and component framework like TFX for end-to-end ML.

CUse the Kubeflow Pipelines (KFP) SDK to create multiple components that use Dataflow and

Kubeflow Pipelines (KFP) is a general ML orchestration platform, but for a TensorFlow-specific model and workflow, TFX often provides more out-of-the-box, optimized components and a more streamlined experience.

DUse the TensorFlow Extended (TFX) SDK to create multiple components that use Dataflow andCorrect

TensorFlow Extended (TFX) is specifically designed for building and managing ML pipelines, offering components for data ingestion, validation, transformation (often using Apache Beam/Dataflow), model training, evaluation, and deployment. It provides a robust, opinionated, and integrated solution for complex, large-scale TensorFlow workflows, ensuring automation and low maintenance.

Concept tested: End-to-end TensorFlow ML pipelines with TFX

Source: https://www.tensorflow.org/tfx/guide/understanding_tfx_pipelines

Topics

#MLOps#Data Pipelines#TensorFlow Extended (TFX)#Workflow Orchestration

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