PMLE Architecting Low-Code ML Solutions Practice Question
A company has an existing TensorFlow model for fraud detection that they want to use for predictions in BigQuery. They want to call the model from SQL queries without moving data out of BigQuery. How should they deploy the model?
⚠ Common exam trap
The trap here is that candidates often overcomplicate the solution by choosing Vertex AI or AI Platform, not realizing that BigQuery ML has native TensorFlow support, which is the most direct and low-code way to meet the requirement of keeping data in BigQuery.
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
✓
Import the TensorFlow model directly into BigQuery ML
BigQuery ML (BQML) natively supports importing TensorFlow models directly, allowing you to use them for predictions via SQL without moving data out of BigQuery. This is the simplest and most efficient approach because it eliminates the need for external services or data export, leveraging BQML's built-in `CREATE MODEL` statement with the `OPTIONS(model_type = 'TENSORFLOW')` clause.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PMLE 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 PMLE exam.