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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