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PDE BigQuery ML export Practice Question

You are building a real-time fraud detection system using BigQuery streaming and a BQML logistic regression model. The model must be retrained every hour with new labeled data. What is the MOST cost-effective approach to serve predictions with low latency?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Export the model to a Cloud Storage bucket and deploy it to AI Platform Prediction

Exporting the model to Cloud Storage and deploying to AI Platform Prediction is the most cost-effective approach because AI Platform Prediction provides managed, autoscaling prediction serving with pay-per-prediction pricing. It avoids the cost and latency of repeatedly querying BigQuery with ML.PREDICT, which consumes slots and is not designed for real-time serving. Option C (Dataflow with model inference) incurs streaming pipeline costs, while options A and B are inefficient due to repeated BigQuery queries or unsupported materialized views with ML.PREDICT.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Call ML.PREDICT on a BigQuery table that is updated every hour

    Why it's wrong here

    ML.PREDICT is not designed for low-latency real-time serving; it is a batch operation.

  • Use a BigQuery materialized view that refreshes every minute and apply ML.PREDICT

    Why it's wrong here

    Materialized views cannot call ML.PREDICT directly; also not real-time.

  • Stream data into Pub/Sub and use a Dataflow pipeline with Apache Beam's model inference

    Why it's wrong here

    This adds complexity and cost; Dataflow streaming may be overkill for simple fraud detection.

  • Export the model to a Cloud Storage bucket and deploy it to AI Platform Prediction

    Why this is correct

    Exporting to AI Platform Prediction provides low-latency serving with autoscaling, cost-effective for hourly retraining.

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This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.