MLS-C01 Question #202: Real Exam Question with Answer & Explanation

Question

A machine learning (ML) specialist needs to extract embedding vectors from a text series. The goal is to provide a ready-to-ingest feature space for a data scientist to develop downstream ML predictive models. The text consists of curated sentences in English. Many sentences use similar words but in different contexts. There are questions and answers among the sentences, and the embedding space must differentiate between them. Which options can produce the required embedding vectors that capture word context and sequential QA information? (Choose two.)

Options

• AAmazon SageMaker seq2seq algorithm
• BAmazon SageMaker BlazingText algorithm in Skip-gram mode
• CAmazon SageMaker Object2Vec algorithm
• DAmazon SageMaker BlazingText algorithm in continuous bag-of-words (CBOW) mode
• ECombination of the Amazon SageMaker BlazingText algorithm in Batch Skip-gram mode

Explanation

{"question_number": 3, "correct_answer": "A, C", "explanation": "Two requirements must be met: capturing word context AND sequential QA structure. (A) SageMaker seq2seq is an encoder-decoder architecture that processes sequences, making it ideal for capturing sequential relationships like questions followed by answers - it encodes the full sequence context. (C) SageMaker Object2Vec can generate embeddings for pairs of objects (e.g., question-answer pairs), directly modeling QA relationships. BlazingText Skip-gram (B) and CBOW (D) are word-level embedding methods (similar to Word2Vec) that capture semantic similarity but lack sequential/contextual awareness and cannot distinguish QA structure. Batch Skip-gram (E) is also a word-level method without sequential modeling capability.", "generated_by": "claude-sonnet", "llm_judge_score": 3}

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