easyMultiple Choice
PMLE Practice Question: A data scientist runs a BigQuery ML prediction…
Network Topology
A data scientist runs a BigQuery ML prediction query and gets a region mismatch error. The model is in the US region, but the new_data table is in the EU region. What is the simplest way to resolve this?
⚠ Common exam trap
The trap here is that candidates may overthink the solution and choose to recreate the model or export/import it, not realizing that the simplest and most efficient fix is to copy the data table to the model's region.
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
✓
Copy the new_data table to the US region using the BigQuery UI or CLI
The simplest fix is to move the new_data table to the same region as the model (US). BigQuery ML requires that the model and the data used for predictions reside in the same multi-region or regional location. Copying the table via the BigQuery UI or CLI (e.g., `bq cp`) is a straightforward, no-code operation that avoids retraining or exporting the model.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Recreate the model in the EU region using the same training data
Why it's wrong here
Recreating the model in the EU duplicates training cost and effort; BigQuery ML requires model and data in the same region, and copying the new_data table to the US is the direct fix. It is tempting because co-locating the model with EU data seems tidy, but the stem asks for the simplest resolution.
- ✓
Copy the new_data table to the US region using the BigQuery UI or CLI
Why this is correct
BigQuery ML requires the model and the input data to reside in the same region, so copying new_data into the US region places both objects together and the prediction query runs. This is the simplest fix, avoiding model retraining or dataset recreation.
- ✗
Enable cross-region query in BigQuery settings
Why it's wrong here
Enabling cross-region querying is tempting as it directly addresses region mismatches. However, this feature primarily facilitates standard SQL queries across datasets in different regions, such as joining tables. BigQuery ML prediction queries, conversely, mandate that the machine learning model and the input data reside within the *same geographic region* for inference to occur. Cross-region querying does not extend to BigQuery ML model inference, meaning it cannot resolve the region mismatch for a prediction operation.
- ✗
Export the model from US and import it to EU
Why it's wrong here
Exporting and re-importing a BigQuery ML model between regions does not resolve the region mismatch error because the prediction query must reference both the model and the new_data table in the same location; moving the model to EU still leaves the original US model unused, and the query will fail unless the data is also moved. This option is tempting because exporting and importing is a standard method for physically relocating ML artefacts between projects or regions when the goal is to run inference entirely in the new region, but here the stem requires the simplest fix—which is to copy the new_data table to the US region instead.
Visual reference
Go deeper
Related to this question
About these practice questions
This PMLE question is part of Courseiva's 775-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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.