The correct answer is that the trained BigQuery ML model can be registered in Vertex AI Model Registry and deployed to an endpoint. This is because Vertex AI Pipelines integrates with BigQuery ML through the BigQueryQueryJob operator, which allows you to execute SQL-based training queries like CREATE MODEL as a pipeline step, enabling a low-code ML workflow. On the Google Professional Machine Learning Engineer exam, this tests your understanding of how to orchestrate model training without writing custom Python code, using BigQuery ML’s SQL syntax within a Vertex AI Pipeline. A common trap is assuming BigQuery ML models must be exported or converted before deployment, but they can be directly registered and deployed via the Model Registry. Remember the mnemonic: “SQL trains, Pipeline registers, Endpoint deploys” to recall the seamless integration flow.
PMLE Architecting low-code ML solutions Practice Question
This PMLE practice question tests your understanding of architecting low-code ml solutions. 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.
A company wants to build a low-code ML pipeline using Vertex AI Pipelines and BigQuery ML. They need to train, evaluate, and deploy a model. Which TWO statements are correct about the integration between Vertex AI Pipelines and BigQuery ML? (Choose TWO.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Vertex AI Pipeline steps can include BigQuery ML training via the BigQueryQueryJob operator.
Option D is correct because Vertex AI Pipelines can integrate with BigQuery ML by using the BigQueryQueryJob operator to execute SQL-based training queries, such as `CREATE MODEL`, as a pipeline step. This allows you to orchestrate BigQuery ML model training within a Vertex AI Pipeline, enabling a low-code ML workflow.
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.
✗
BigQuery ML models are automatically stored in Vertex AI Model Registry after training.
Why it's wrong here
Manual registration is required.
✗
BigQuery ML supports hyperparameter tuning using the CREATE MODEL statement.
Why it's wrong here
BigQuery ML does not natively support hyperparameter tuning.
✗
Vertex AI Pipelines supports automatic retry of failed steps due to transient errors.
Why it's wrong here
Retry logic must be explicitly implemented.
✓
Vertex AI Pipeline steps can include BigQuery ML training via the BigQueryQueryJob operator.
Why this is correct
BigQuery ML training can be invoked as a SQL query step.
Related concept
Read the scenario before looking for a memorised answer.
✓
The trained BigQuery ML model can be registered in Vertex AI Model Registry and deployed to an endpoint.
Why this is correct
Model can be exported and registered.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that BigQuery ML models are automatically registered in Vertex AI Model Registry after training, but in reality, you must explicitly export or upload the model to the registry as a separate step.
Detailed technical explanation
How to think about this question
Under the hood, the BigQueryQueryJob operator in Vertex AI Pipelines submits a BigQuery job asynchronously and waits for completion, allowing you to chain SQL-based ML training (e.g., `CREATE OR REPLACE MODEL`) with downstream steps like model evaluation or deployment. A subtle behavior is that the operator returns a `BigQueryJob` object, which you can use to retrieve the model's metadata, but you must still explicitly register the model in Vertex AI Model Registry using a separate component (e.g., `UploadModel`). In a real-world scenario, this integration is ideal for teams that want to leverage BigQuery's in-database ML capabilities while still using Vertex AI's managed deployment and monitoring services.
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.
Architecting low-code ML solutions — This question tests Architecting low-code ML solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Vertex AI Pipeline steps can include BigQuery ML training via the BigQueryQueryJob operator. — Option D is correct because Vertex AI Pipelines can integrate with BigQuery ML by using the BigQueryQueryJob operator to execute SQL-based training queries, such as `CREATE MODEL`, as a pipeline step. This allows you to orchestrate BigQuery ML model training within a Vertex AI Pipeline, enabling a low-code ML workflow.
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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