- A
BigQuery ML supports binary logistic regression models
Binary logistic regression is supported for classification tasks using SQL.
- B
BigQuery ML supports deep neural network models like CNNs and RNNs
Why wrong: BigQuery ML does not directly support complex deep learning architectures; those are built in Vertex AI.
- C
BigQuery ML requires data to be exported to Cloud Storage before training
Why wrong: BigQuery ML trains directly on data in BigQuery tables without requiring export.
- D
BigQuery ML supports creating and evaluating linear regression models using SQL
BigQuery ML can create linear regression models with CREATE MODEL statements and evaluate with ML.EVALUATE.
- E
BigQuery ML can be used for recommendation systems using matrix factorization
BigQuery ML provides matrix factorization for building recommendation models.
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 data scientist wants to run ML models directly on their BigQuery data without moving data out. Which THREE statements about BigQuery ML are correct? (Choose 3)
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 supports binary logistic regression models
BigQuery ML supports binary logistic regression models because it provides the `CREATE MODEL` statement with `OPTIONS(model_type='LOGISTIC_REG')` for classification tasks. This allows data scientists to train and evaluate models directly on data stored in BigQuery using standard SQL syntax, without needing to export or move the data.
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 supports binary logistic regression models
Why this is correct
Binary logistic regression is supported for classification tasks using SQL.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
BigQuery ML supports deep neural network models like CNNs and RNNs
Why it's wrong here
BigQuery ML does not directly support complex deep learning architectures; those are built in Vertex AI.
- ✗
BigQuery ML requires data to be exported to Cloud Storage before training
Why it's wrong here
BigQuery ML trains directly on data in BigQuery tables without requiring export.
- ✓
BigQuery ML supports creating and evaluating linear regression models using SQL
Why this is correct
BigQuery ML can create linear regression models with CREATE MODEL statements and evaluate with ML.EVALUATE.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
BigQuery ML can be used for recommendation systems using matrix factorization
Why this is correct
BigQuery ML provides matrix factorization for building recommendation models.
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 misconception that BigQuery ML supports all types of ML models, including deep neural networks, when in fact it only supports a curated set of simpler, SQL-friendly model types like linear and logistic regression, matrix factorization, and boosted trees.
Detailed technical explanation
How to think about this question
BigQuery ML leverages the underlying BigQuery infrastructure to execute model training as a SQL query, using distributed processing for scalability. For example, matrix factorization for recommendation systems uses the `MATRIX_FACTORIZATION` model type with implicit or explicit feedback, and the training process runs entirely within BigQuery's compute engine, avoiding data egress costs. This integration is key for operationalizing ML workflows in a data warehouse environment.
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
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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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 supports binary logistic regression models — BigQuery ML supports binary logistic regression models because it provides the `CREATE MODEL` statement with `OPTIONS(model_type='LOGISTIC_REG')` for classification tasks. This allows data scientists to train and evaluate models directly on data stored in BigQuery using standard SQL syntax, without needing to export or move the data.
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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