You must produce embedding vectors from an English sentence collection so downstream models can use them. Sentences often reuse words in different contexts and include question–answer pairs; the embeddings should capture word context and sequence/QA information. Which SageMaker options can generate embeddings that meet these needs? (Choose two.)

The Amazon SageMaker seq2seq algorithm is designed for sequence-to-sequence tasks, which inherently capture word context and order, making it suitable for question-answer pairs and understanding how words relate in a sequence. The Amazon SageMaker Object2Vec algorithm is a highly flexible, unsupervised learning algorithm that can learn embeddings for arbitrary objects, including sentences. It can be configured to capture contextual information and relationships within sequences, making it effective for the given requirements. BlazingText in Skip-gram or CBOW modes primarily focuses on learning word embeddings based on local context windows, but it treats sentences as bags of words, losing sequence information and the ability to capture complex contextual relationships or question-answer structures. While BlazingText can be combined with custom RNNs, the question asks for SageMaker options that can generate embeddings, implying built-in algorithms or direct configurations.