20 Google BERT Quiz Questions and Answers

1. What does BERT stand for in the context of Google’s language model?
A) Bidirectional Encoder Representations from Transformers
B) Basic Encoder Retrieval Tool
C) Bidirectional Embedding Recognition Technique
D) Binary Encoder and Retrieval Transformer
Answer: A
Explanation: BERT stands for Bidirectional Encoder Representations from Transformers, which highlights its use of bidirectional context in processing text sequences.

2. Which architecture is BERT based on?
A) Recurrent Neural Networks (RNN)
B) Transformers
C) Convolutional Neural Networks (CNN)
D) Long Short-Term Memory (LSTM)
Answer: B
Explanation: BERT is built on the Transformer architecture, specifically using the encoder part to capture bidirectional relationships in text.

3. How does BERT handle context in sentences?
A) Unidirectionally, from left to right
B) Bidirectionally, considering both left and right contexts
C) Only from right to left
D) It ignores context entirely
Answer: B
Explanation: BERT processes text bidirectionally, allowing it to understand the full context of a word by looking at surrounding words on both sides.

4. What is the primary pre-training task used in BERT?
A) Masked Language Modeling (MLM)
B) Next Sentence Prediction (NSP)
C) Both MLM and NSP
D) Sentiment Analysis
Answer: C
Explanation: BERT uses two pre-training tasks: Masked Language Modeling, where some words are masked and predicted, and Next Sentence Prediction, which determines if two sentences are consecutive.

5. In BERT, what is the purpose of the [CLS] token?
A) To separate sentences
B) To represent the entire input sequence for classification tasks
C) To mask words during pre-training
D) To denote the end of a sentence
Answer: B
Explanation: The [CLS] token is added at the beginning of every input sequence and its final hidden state is used as the aggregate representation for classification tasks.

6. Which version of BERT has 12 layers, 768 hidden units, and 12 attention heads?
A) BERT-Base
B) BERT-Large
C) BERT-Tiny
D) BERT-Mini
Answer: A
Explanation: BERT-Base is the standard smaller model with 12 layers, 768 hidden units, and 12 self-attention heads.

7. What type of attention mechanism does BERT use?
A) Self-attention
B) Global attention
C) Cross-attention only
D) No attention mechanism
Answer: A
Explanation: BERT employs self-attention mechanisms within the Transformer encoder to weigh the importance of different words in the input sequence.

8. How is BERT typically fine-tuned for specific tasks?
A) By training from scratch on new data
B) By adding a task-specific layer and continuing training on labeled data
C) By only using pre-trained weights without further training
D) By replacing the entire architecture
Answer: B
Explanation: Fine-tuning BERT involves adding a simple output layer for the specific task and training the model further on task-specific datasets.

9. What is the input format for BERT when processing two sentences?
A) [CLS] Sentence A [SEP] Sentence B [SEP]
B) Sentence A Sentence B
C) [SEP] Sentence A Sentence B [CLS]
D) Sentence B [CLS] Sentence A
Answer: A
Explanation: BERT’s input format for two sentences includes the [CLS] token at the start, followed by the first sentence, a [SEP] token, the second sentence, and another [SEP] token.

10. Which Google AI paper introduced BERT?
A) “Attention is All You Need”
B) “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”
C) “Efficient Transformers”
D) “Language Models are Unsupervised Multitask Learners”
Answer: B
Explanation: The paper titled “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding” by Devlin et al. introduced the BERT model.

11. What is the maximum sequence length BERT can handle by default?
A) 128 tokens
B) 512 tokens
C) 1024 tokens
D) Unlimited
Answer: B
Explanation: BERT is designed to handle sequences up to 512 tokens by default, which includes the special tokens like [CLS] and [SEP].

12. How does BERT differ from traditional word embeddings like Word2Vec?
A) BERT is unidirectional
B) BERT captures contextual embeddings, while Word2Vec uses static embeddings
C) BERT requires less data for training
D) BERT does not use neural networks
Answer: B
Explanation: Unlike Word2Vec, which produces static embeddings for words, BERT generates contextual embeddings that vary based on the surrounding words.

13. In BERT’s Masked Language Modeling, what percentage of words are typically masked?
A) 10%
B) 15%
C) 25%
D) 50%
Answer: B
Explanation: During pre-training, about 15% of the words in the input are masked, and the model predicts them based on the context.

14. What downstream task is BERT particularly effective for?
A) Image recognition
B) Question answering
C) Speech synthesis
D) Video analysis
Answer: B
Explanation: BERT excels in question answering tasks, as demonstrated by its performance on benchmarks like SQuAD, due to its deep understanding of context.

15. Which token is used to separate segments in BERT’s input?
A) [MASK]
B) [SEP]
C) [PAD]
D) [UNK]
Answer: B
Explanation: The [SEP] token is used to separate different segments or sentences in the input sequence for BERT.

16. What is the role of the [SEP] token in BERT?
A) To indicate the start of a sequence
B) To separate segments and denote the end of a sentence
C) To mask words
D) To pad sequences
Answer: B
Explanation: The [SEP] token separates input segments and helps the model distinguish between different parts of the input, such as two sentences.

17. How many parameters does the BERT-Large model have?
A) 110 million
B) 340 million
C) 1 billion
D) 24 million
Answer: B
Explanation: BERT-Large has approximately 340 million parameters, with 24 layers, 1024 hidden units, and 16 attention heads.

18. What technique does BERT use to avoid the “curse of sentence boundary”?
A) Bidirectional processing
B) Next Sentence Prediction
C) Masked Language Modeling only
D) Unidirectional encoding
Answer: B
Explanation: Next Sentence Prediction helps BERT understand relationships between sentences, addressing issues related to sentence boundaries in language understanding.

19. In what year was BERT first released?
A) 2017
B) 2018
C) 2019
D) 2020
Answer: B
Explanation: BERT was introduced in a paper published in 2018, marking a significant advancement in natural language processing.

20. Why is BERT considered a breakthrough in NLP?
A) It uses less computational power
B) It achieves state-of-the-art results on many benchmarks with pre-trained bidirectional representations
C) It eliminates the need for fine-tuning
D) It only works with monolingual data
Answer: B
Explanation: BERT’s ability to provide high-quality bidirectional representations through pre-training leads to state-of-the-art performance on a wide range of NLP tasks after fine-tuning.