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A data analytics team is building a Retrieval Augmented Generation (RAG) application to provide contextual answers from a vast repository of internal documents stored in Snowflake. They are evaluating different strategies for generating and retrieving text embeddings to optimize the overall RAG pipeline's performance and relevance. Which of the following statements accurately describe performance considerations related to embedding generation and retrieval in this RAG context? (Select all that apply)
A. Option E
B. Option C
C. Option D
D. Option A
E. Option B
Answer: B,E
Explanation:
For optimizing RAG pipeline performance and relevance: ' This statement is incorrect. Snowflake's documentation explicitly recommends splitting text into smaller chunks (no more than 512 tokens) for Cortex Search to achieve optimal retrieval and downstream LLM response quality. This holds true even with models that have larger context windows like 'snowflake-arctic-embed-l-v2.0-8k' , because smaller chunks lead to more precise retrieval. * **B:** Deploying custom models like a Hugging Face 'sentenceTransformeN on Snowpark Container Services (SPCS) with GPU compute pools (e.g., *GPU or *GPU NV_M') is optimized for intensive GPU usage scenarios like LLMs/VLMs. This can provide lower latency and higher throughput for embedding generation in very high-volume, custom scenarios, offering more control than managed functions. ' This statement is correct. Snowflake's documentation clearly states that for best search results with Cortex Search, it is recommended to split the text in the search column into chunks of no more than 512 tokens. This strategy typically results in higher retrieval and better quality responses from downstream LLMs. * This statement is incorrect. Snowflake explicitly advises executing queries that call Cortex AI SQL functions (including ' EMBED_TEXT') with a *smaller* warehouse (no larger than MEDIUM), as larger warehouses do not increase performance for these specific functions. * *E:" This statement is incorrect. Cortex Search powers RAG applications by leveraging *semantic search*, which combines both vector and keyword search capabilities, to provide customized, contextualized responses. Relying solely on keyword search would generally yield less contextual relevance for LLM responses than a hybrid approach.
NEW QUESTION # 72
A data analyst is working with a table containing customer feedback text and needs to perform various text analysis tasks efficiently within Snowflake. They want to summarize the reviews, determine their sentiment, and extract specific pieces of information. Which of the following Snowflake Cortex LLM functions, when applied to a text column, will achieve the desired outcome and return the specified output type?
A. To extract a specific answer to a question from each review, the analyst can use
B. To determine the overall sentiment of each review, the analyst should use
C. To categorize reviews into predefined labels, the analyst should use
D. To get a concise overview of each review, the analyst should use
E. The
Answer: C,D
Explanation:
Option A is correct because the 'SUMMARIZE' function takes an English-language input text and returns a string containing a summary of the original text. Option B is incorrect because the 'SENTIMENT function returns a floating-point number from -1 to 1 (inclusive) indicating the level of negative or positive sentiment, not an INTEGER. Option C is incorrect because the 'EXTRACT_ANSWER function returns a string containing an answer to the given question, not a JSON object. Option D is correct because the 'CLASSIFY _ TEXT function classifies free-form text into categories and returns an OBJECT value (VARIANT) with a 'label' field specifying the category. 'AI_CLASSIFY' is the latest version of this function. Option E is incorrect because 'AI_AGG' aggregates a text column and returns insights across multiple rows based on a user-defined prompt, and importantly, it is not subject to context window limitations.
NEW QUESTION # 73
A data engineering team is building an automated pipeline within Snowflake to process newly ingested documents. This pipeline needs to classify each document's sentiment (positive, neutral, negative) and summarise its content using Cortex LLM functions, then store the results in a table. The pipeline is orchestrated using Streams and Tasks. Which considerations are paramount for implementing and monitoring this AI-infused data pipeline?
A. Option E
B. Option A
C. Option C
D. Option D
E. Option B
Answer: B,C,E
Explanation:
NEW QUESTION # 74
A business analyst is using a Cortex Analyst-powered conversational application to query structured data in Snowflake. They initially ask, 'What was the total profit from California last quarter?' and then follow up with, 'What about New York?' The application successfully provides accurate answers to both questions. Which of the following statements explain how Cortex Analyst supports this multi-turn conversational experience and maintains accuracy? (Select all that apply)
A. Cortex Analyst stores the full, verbatim history of all previous user prompts and LLM responses, which are then passed to every subsequent LLM call to ensure complete context retention without any summarization.
B. The accuracy of the SQL queries generated by Cortex Analyst for follow-up questions is significantly enhanced by its integration with a Verified Query Repository (VQR), which stores pre-verified natural language questions and their corresponding SQL queries.
C. To handle follow-up questions, Cortex Analyst leverages an internal LLM summarization agent (e.g., Llama 3.1 70B) to reframe the current-turn question by retrieving context from the conversation history, rather than simply passing the entire history.
D. The semantic model YAML file, which defines logical tables, dimensions, and measures, is crucial for Cortex Analyst to bridge the gap between business terminology and underlying technical schema, thereby improving text-to-SQL conversion accuracy for both initial and follow-up queries.
E. For multi-turn conversations, Cortex Analyst primarily relies on semantic search over sample values defined in the semantic model to infer context and generate SQL, making explicit conversation history management unnecessary.
Answer: B,C,D
Explanation:
Option A is incorrect. Cortex Analyst does not simply pass the full, verbatim history of all previous prompts and responses to every subsequent LLM call. This 'primitive way' could lead to longer inference times, more non-determinism, and degraded performance due to multitasking. Instead, it uses an LLM summarization agent to manage context. Option B is correct. Cortex Analyst supports multi-turn conversations by recognizing follow-up questions and using an LLM summarization agent (such as Llama 3.1 70B, which showed high accuracy in this role) to retrieve context from the conversation history and reframe the current-turn question. Option C is correct. The Verified Query Repository (VQR) is a collection of pre-verified questions and corresponding SQL queries that helps improve the accuracy and trustworthiness of Cortex Analyst's results by using relevant SQL queries for similar questions. Option D is incorrect. While semantic search over sample values can improve literal search for Cortex Analyst, it is not the primary mechanism for managing the context of multi-turn conversations. Context management relies on an LLM summarization agent. Option E is correct. Semantic models, captured in lightweight YAML files, are critical for Cortex Analyst. They provide richer semantic information than basic database schemas, bridging the gap between business user language and technical database definitions, which is essential for accurate text-to-SQL conversions in both initial and follow-up queries.
NEW QUESTION # 75
An ML Engineer is logging a custom PyCaret model to the Snowflake Model Registry, with the intention of deploying it to Snowpark Container Services (SPCS) for GPU-powered inference. The PyCaret model is wrapped in a 'custom_model.ModelContext'. Which of the following statements correctly describe the considerations for the call and the model's environment?
A. Option E
B. Option D
C. Option C
D. Option A
E. Option B
Answer: B,C,E
Explanation:
NEW QUESTION # 76
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