Question 4 of 1,000
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 uses BigQuery ML with a remote model calling Vertex AI's pre-trained image classification model. They need to classify images stored in Cloud Storage buckets. What is the correct approach?
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
✓
Create a remote model with model_type='VERTEX_AI' and use ML.PREDICT with image URIs.
Option A is correct because BigQuery ML remote models allow you to invoke Vertex AI pre-trained models via the `model_type='VERTEX_AI'` setting. You can then use `ML.PREDICT` directly on Cloud Storage image URIs without needing to export or transform the image data, as BigQuery ML handles the URI resolution and passes the image to Vertex AI for classification.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the misconception that BigQuery ML can handle unstructured data like images natively, leading candidates to choose options that involve base64 encoding or direct IMAGE data types, when the correct approach is to use remote models with URI references.
Detailed technical explanation
How to think about this question
Under the hood, BigQuery ML remote models use a service account to authenticate and call Vertex AI's prediction endpoint via gRPC or HTTP. The `ML.PREDICT` function serializes the URI reference and sends it to Vertex AI, which fetches the image from Cloud Storage, performs inference, and returns the classification result. A subtle behavior is that the Cloud Storage bucket must be in the same region as the BigQuery dataset and Vertex AI endpoint to avoid cross-region latency or access errors.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Questions learners often ask
What does this PMLE question test?
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: Create a remote model with model_type='VERTEX_AI' and use ML.PREDICT with image URIs. — Option A is correct because BigQuery ML remote models allow you to invoke Vertex AI pre-trained models via the `model_type='VERTEX_AI'` setting. You can then use `ML.PREDICT` directly on Cloud Storage image URIs without needing to export or transform the image data, as BigQuery ML handles the URI resolution and passes the image to Vertex AI for classification.
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.
About these practice questions
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Last reviewed: Jul 4, 2026
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.
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