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Exam GES-C01 Topic 1 Question 329 Discussion
Actual exam question for Snowflake's GES-C01 exam
Question #: 329
Topic #: 1
Question #: 329
Topic #: 1
A global analytics firm is developing a Retrieval Augmented Generation (RAG) system in Snowflake to answer customer queries across a large repository of technical documentation, which includes documents in English, German, and Spanish. They are looking to use a Snowflake Cortex embedding model to convert document chunks into vector embeddings for their Cortex Search Service. Which of the following considerations are critical when selecting an appropriate embedding model to optimize for both query relevance and cost-efficiency for their multilingual RAG application? (Select all that apply)
Suggested Answer: B,C Vote an answer
Option B is correct because both
model provides an increased context window of 8000 tokens while maintaining the same cost per million tokens (0.05 credits) as the 512-token version of
Option C is correct because Snowflake recommends splitting text into chunks of no more than 512 tokens for best search results with Cortex Search, as research shows this typically leads to higher retrieval precision and improved downstream LLM response quality, even when using longer-context embedding models. Option A is incorrect because
is an English-only embedding model, which does not meet the requirement for multilingual documentation. Option D is incorrect; the cost per million tokens for EMBED_TEXT_1024 models (e.g., 0.05-0.07 credits) is not inherently more cost-efficient than EMBED_TEXT_768 models (e.g., 0.03 credits), and cost-efficiency depends on the specific model and use case, not just output dimensions. Option E is incorrect; the context window of an embedding model refers to the maximum length of a text input (chunk) it can process. The maximum pages a document can have (e.g., 300 pages for Document AI) is a separate document requirement, not directly determined by the embedding model's context window.
model provides an increased context window of 8000 tokens while maintaining the same cost per million tokens (0.05 credits) as the 512-token version of
Option C is correct because Snowflake recommends splitting text into chunks of no more than 512 tokens for best search results with Cortex Search, as research shows this typically leads to higher retrieval precision and improved downstream LLM response quality, even when using longer-context embedding models. Option A is incorrect because
is an English-only embedding model, which does not meet the requirement for multilingual documentation. Option D is incorrect; the cost per million tokens for EMBED_TEXT_1024 models (e.g., 0.05-0.07 credits) is not inherently more cost-efficient than EMBED_TEXT_768 models (e.g., 0.03 credits), and cost-efficiency depends on the specific model and use case, not just output dimensions. Option E is incorrect; the context window of an embedding model refers to the maximum length of a text input (chunk) it can process. The maximum pages a document can have (e.g., 300 pages for Document AI) is a separate document requirement, not directly determined by the embedding model's context window.
by Marina at Feb 24, 2026, 11:48 PM
Here are all the actual test exam dumps for IT exams. Most people prepare for the actual exams with our test dumps to pass their exams. So it's critical to choose and actual test pdf to succeed.
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