Based on the question's requirements for capturing word context, sequential QA information, and creating sentence embeddings, and considering the discussion summary, I agree with the suggested answer CE.
Here's a detailed breakdown:

Reasoning for choosing C: Amazon SageMaker Object2Vec is specifically designed for learning embeddings of objects (which can be sentences) based on their context and relationships. This makes it suitable for capturing the relationships between questions and answers in the text series. Object2Vec learns embeddings in such a way that similar objects are located close to each other in the embedding space, which aligns with the goal of differentiating between questions and answers based on context.
Reasoning for choosing E: Combining Amazon SageMaker BlazingText (specifically the Skip-gram mode, though "Batch Skip-gram mode" mentioned in the option isn't standard terminology, Skip-gram is implied) with a custom RNN is a strong approach. BlazingText's Skip-gram creates word embeddings that capture word context, and the RNN can then process the sequence of word embeddings to capture sentence-level dependencies and the sequential QA information. This allows the model to understand the relationships between words in a sentence and between sentences in the series.
Reasoning for not choosing A: Amazon SageMaker seq2seq algorithm is primarily designed for sequence-to-sequence tasks like machine translation or text summarization. While it does produce embeddings as an intermediate step, its primary goal isn't to create general-purpose sentence embeddings for downstream tasks. It's more focused on mapping one sequence to another. The discussion correctly points out seq2seq is not designed for general-purpose embedding.
Reasoning for not choosing B and D: Amazon SageMaker BlazingText in Skip-gram (B) or CBOW (D) mode creates word embeddings, not sentence embeddings. While these word embeddings could be used as input to another model to create sentence embeddings, they don't directly address the question's requirement for ready-to-ingest feature space of *sentence* embeddings.

In summary, Object2Vec directly creates sentence embeddings that capture context and relationships, while the BlazingText + RNN combination creates word embeddings and uses an RNN to then generate sentence embeddings capturing sequential information.