- A
GPT-3
Why wrong: GPT-3 is autoregressive (unidirectional), not bidirectional.
- B
AlphaGo
Why wrong: AlphaGo is a reinforcement learning model for the game of Go, not a language model.
- C
Transformer (the paper)
Why wrong: The Transformer paper introduced the architecture but was not a pre-trained model; BERT built on it.
- D
BERT
BERT is a bidirectional transformer pre-trained on a large corpus, setting new benchmarks for language understanding.
Question 619 of 997
Generative AI Leader Generative AI Concepts and Technologies Practice Question
This Generative AI Leader practice question tests your understanding of generative ai concepts and technologies. 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.
Which Google AI model was the first to demonstrate that transformers could be pre-trained bidirectionally on a large corpus, leading to major improvements in language understanding?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"first"Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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
✓
BERT
BERT (Bidirectional Encoder Representations from Transformers) was the first model to demonstrate that transformers could be pre-trained bidirectionally on a large corpus (BooksCorpus and English Wikipedia). By using a masked language model (MLM) objective, BERT conditions on both left and right context simultaneously, unlike previous unidirectional models, leading to significant improvements on 11 NLP benchmarks at its release.
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.
- ✗
GPT-3
Why it's wrong here
GPT-3 is autoregressive (unidirectional), not bidirectional.
- ✗
AlphaGo
Why it's wrong here
AlphaGo is a reinforcement learning model for the game of Go, not a language model.
- ✗
Transformer (the paper)
Why it's wrong here
The Transformer paper introduced the architecture but was not a pre-trained model; BERT built on it.
- ✓
BERT
Why this is correct
BERT is a bidirectional transformer pre-trained on a large corpus, setting new benchmarks for language understanding.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
In Google exams, candidates often confuse the original Transformer paper (introducing the architecture) with BERT's specific contribution of bidirectional pre-training, leading to selection of Option C instead of D.
Detailed technical explanation
How to think about this question
BERT's bidirectional pre-training uses a masked language model where 15% of input tokens are randomly masked, and the model predicts the original token based on the full context. This is complemented by a next-sentence prediction (NSP) task that helps with downstream tasks like question answering and natural language inference. In practice, BERT's bidirectional attention mechanism allows it to capture richer contextual representations than unidirectional models, which is why it became the foundation for many subsequent models like RoBERTa and ALBERT.
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.
Visual reference
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Generative AI Concepts and Technologies — This question tests Generative AI Concepts and Technologies — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: BERT — BERT (Bidirectional Encoder Representations from Transformers) was the first model to demonstrate that transformers could be pre-trained bidirectionally on a large corpus (BooksCorpus and English Wikipedia). By using a masked language model (MLM) objective, BERT conditions on both left and right context simultaneously, unlike previous unidirectional models, leading to significant improvements on 11 NLP benchmarks at its release.
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.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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