PMLE - 6

What are the major stages of an end-to-end workflow to build an NLP project with Vertex AI?

• Data preparation, model training, and model serving

• Model deployment, model monitoring, and model serving

• Model training, model evaluation, and model deployment

• Dataset upload, feature engineering, and model training

What is the difference between continuous bag-of-words (CBOW) and skip-gram, the two primary techniques of word2vec?

• CBOW uses a center word to predict surrounding words, whereas skip-gram uses surrounding words to predict a center word.

• CBOW uses surrounding words to predict a center word, whereas skip-gram uses a center word to predict surrounding words.

• CBOW uses previous words to predict the next word, whereas skip-gram uses the next word to predict previous words.

• CBOW uses the next word to predict previous words, whereas skip-gram uses previous words to predict the next word.

What are the benefits of using word embedding (such as word2vec) compared to basic vectorization (such as one-hot encoding) when you convert text to vectors?

• Compared to basic vectorization, which converts text to vectors without semantic meaning, word embeddings represent words in a vector space where the distance between them indicates semantic similarity and difference.

• All of the above.

• Compared to basic vectorization, which converts text to sparse vectors, word embedding converts text to dense vectors.

• You can use pre-trained word-embedding to represent text.

What are the major gates in a standard LSTM (long short-term memory) cell?

• A standard LSTM cell includes three gates: the forget gate to forget irrelevant information, the input gate to remember relevant information, and the update gate to update new information.

• A standard LSTM cell includes three gates: the input gate to input information, the hidden gate to remember information, and the output gate to output information.

• A standard LSTM cell includes two gates: the remember gate to remember relevant information and the forget gate to forget irrelevant information.

• A standard LSTM cell includes two gates: the input gate to input information and the output gate to output information.

What is the problem that an encoder-decoder mainly solves?

• None of the above.

• One-to-sequence problems such as image captioning, where you generate a few sentences based on one image

• Sequence-to-sequence problems such as machine translation where you translate sentences to another language

• Sequence-to-one problems such as email spam detection, where you use sequence of text to predict if an email is a spam

How does end-to-end MLOps help ML practitioners with the machine learning life cycle?

• End-to-end MLOps helps ML practitioners efficiently and responsibly manage, monitor, govern, and explain ML projects throughout the entire development lifecycle.

• End-to-end MLOPs lets ML practitioners only train and tune ML models.

• End-to-end MLOps lets ML practitioners only perform exploratory data analysis (EDA) and prototyping.

• End-to-end MLOPs lets ML practitioners only monitor ML models.