The answer is to copy the new_data table to the US region using the BigQuery UI or CLI. This is correct because BigQuery ML enforces a strict location requirement: the model and the prediction data must reside in the same multi-region or regional location, and a region mismatch error occurs when they do not. By copying the table to the US region via `bq cp` or the UI, you align the data with the model without retraining or exporting, making it the simplest no-code fix. On the Google Professional Machine Learning Engineer exam, this tests your understanding of BigQuery ML’s regional constraints and operational efficiency—a common trap is attempting to export the model or change the table’s region via ALTER, which is unsupported. Remember the memory tip: “Model and data must share a zip code—copy the data, not the model.”
PMLE Solving business challenges with ML Practice Question
This PMLE practice question tests your understanding of solving business challenges with ml. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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
Option B is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Redundant; the model can be used in US if data is moved.
✓
Copy the new_data table to the US region using the BigQuery UI or CLI
Why this is correct
Copying the table to the same region resolves the mismatch with minimal effort.
Related concept
Read the scenario before looking for a memorised answer.
✗
Enable cross-region query in BigQuery settings
Why it's wrong here
Cross-region queries are possible but not the simplest; copying data is easier.
✗
Export the model from US and import it to EU
Why it's wrong here
Model export/import is more complex and unnecessary.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
BigQuery ML enforces strict regional isolation for models and data to ensure data locality and compliance. When you run a prediction query, both the model and the input table must be in the same location (e.g., US or EU multi-region). Copying a table between regions uses BigQuery's internal replication, which is efficient and does not require manual data export/import. In real-world scenarios, this is often the fastest resolution when a data scientist accidentally creates a table in the wrong region.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Solving business challenges with ML — This question tests Solving business challenges with ML — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Copy the new_data table to the US region using the BigQuery UI or CLI — Option B is correct because 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.
What should I do if I get this PMLE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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