- A
Vertex AI AutoML Natural Language
Why wrong: Requires exporting data from BigQuery; not SQL-based.
- B
Cloud Natural Language API
Why wrong: Cloud Natural Language API is a pre-built API for sentiment/entity analysis, not for custom model training.
- C
Gemini API
Why wrong: Gemini API is for generative AI, not for training classification models on tabular/text data directly in BigQuery.
- D
BigQuery ML
BigQuery ML enables training and prediction using SQL on data already in BigQuery.
Question 492 of 997
Generative AI Leader Google AI Ecosystem and Strategy Practice Question
This Generative AI Leader practice question tests your understanding of google ai ecosystem and strategy. 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 has a large dataset of customer support tickets stored in BigQuery. They want to predict ticket severity (high, medium, low) using SQL queries without moving data out of BigQuery. Which service should they use?
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
✓
BigQuery ML
BigQuery ML (D) is correct because it allows users to create and execute machine learning models directly within BigQuery using SQL, without moving data out of the warehouse. For a classification task like predicting ticket severity, BigQuery ML supports models such as logistic regression, boosted trees, and deep neural networks, all trained and deployed using standard SQL queries on data already in BigQuery.
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.
- ✗
Vertex AI AutoML Natural Language
Why it's wrong here
Requires exporting data from BigQuery; not SQL-based.
- ✗
Cloud Natural Language API
Why it's wrong here
Cloud Natural Language API is a pre-built API for sentiment/entity analysis, not for custom model training.
- ✗
Gemini API
Why it's wrong here
Gemini API is for generative AI, not for training classification models on tabular/text data directly in BigQuery.
- ✓
BigQuery ML
Why this is correct
BigQuery ML enables training and prediction using SQL on data already in BigQuery.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between services that require data movement (like Vertex AI AutoML) versus services that operate directly on the data warehouse (like BigQuery ML), and the trap here is assuming that any ML service in Google Cloud must involve exporting data to a separate AI platform.
Detailed technical explanation
How to think about this question
BigQuery ML uses the `CREATE MODEL` statement with the `OPTIONS(model_type='LOGISTIC_REG')` clause for classification, and training data is referenced directly via `SELECT` queries on BigQuery tables. Under the hood, BigQuery ML leverages distributed TensorFlow and XGBoost for training, and the model is stored as a BigQuery object, enabling batch predictions with `ML.PREDICT` without any data egress. A real-world scenario is a support team that needs to retrain the severity model daily on new tickets; BigQuery ML can automate this with scheduled queries, keeping the entire pipeline inside BigQuery.
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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
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.
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Google AI Ecosystem and Strategy — study guide chapter
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Google AI Ecosystem and Strategy — This question tests Google AI Ecosystem and Strategy — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: BigQuery ML — BigQuery ML (D) is correct because it allows users to create and execute machine learning models directly within BigQuery using SQL, without moving data out of the warehouse. For a classification task like predicting ticket severity, BigQuery ML supports models such as logistic regression, boosted trees, and deep neural networks, all trained and deployed using standard SQL queries on data already in BigQuery.
What should I do if I get this Generative AI Leader 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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jul 4, 2026
This Generative AI Leader 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 Generative AI Leader exam.
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