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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #141

PROFESSIONAL-MACHINE-LEARNING-ENGINEER Question #141: Real Exam Question with Answer & Explanation

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Submitted by yuriko_h· Apr 18, 2026ML pipeline operationalization

Question

You are working on a system log anomaly detection model for a cybersecurity organization. You have developed the model using TensorFlow, and you plan to use it for real-time prediction. You need to create a Dataflow pipeline to ingest data via Pub/Sub and write the results to BigQuery. You want to minimize the serving latency as much as possible. What should you do?

Options

  • AContainerize the model prediction logic in Cloud Run, which is invoked by Dataflow.
  • BLoad the model directly into the Dataflow job as a dependency, and use it for prediction.
  • CDeploy the model to a Vertex AI endpoint, and invoke this endpoint in the Dataflow job.
  • DDeploy the model in a TFServing container on Google Kubernetes Engine, and invoke it in the

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Topics

#Real-time inference#Model serving#Vertex AI Endpoints#Dataflow pipeline
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