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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q290-Q295):

NEW QUESTION # 290
A legal department uses Snowflake to manage and review large volumes of contracts. They need to automate the process of finding specific pieces of information, such as the effective_date or involved_parties, from these unstructured contract texts. They are considering using SNOWFLAKE. CORTEX. EXTRACT_ANSWER. Which characteristic correctly describes the primary intent or behavior of SNOWFLAKE. CORTEX. EXTRACT_ANSWER, distinguishing it from other LLM functions?

A. It evaluates a text and returns a numeric score indicating the overall positive or negative sentiment.
B. It classifies an input text into one of several predefined categories provided by the user.
C. It is primarily designed to generate entirely new text based on a given prompt, much like a conversational AI.
D. It transforms an input document into a summarized version, reducing its length while preserving key information.
E. It focuses on identifying and returning a direct answer to a specific question contained within the provided source document.

Answer: E

Explanation:
Option D is correct. The 'SNOWFLAKE.CORTEX.EXTRACT_ANSWER function is specifically designed to extract an answer to a given question from a text document. Option A describes the 'COMPLETE function. Option B describes the 'SENTIMENT function. Option C describes the 'CLASSIFY_TEXT function. Option E describes the 'SUMMARIZE function.

NEW QUESTION # 291
A company wants to ingest and process scanned invoices and digitally-born contracts in Snowflake. They need to extract all text, preserving layout for contracts and just the text content for scanned invoices. Which AI_PARSE_DOCUMENT modes would be most appropriate for this scenario, and what is the primary purpose of the function itself?

A. Primary purpose is to summarize text. For contracts, use OCR mode; for invoices, use LAYOUT mode.
B. Primary purpose is to translate text. Both document types should use LAYOUT mode.
C. Primary purpose is to classify text. For contracts, use LAYOUT mode; for invoices, use OCR mode.
D. Primary purpose is to generate new text. For contracts, use OCR mode; for invoices, use LAYOUT mode.
E. Primary purpose is to extract data and layout. For contracts, use LAYOUT mode; for invoices, use OCR mode.

Answer: E

Explanation:
Option C is correct. AI_PARSE_DOCUMENT is a Cortex AI SQL function designed to extract text, data, and layout elements from documents with high fidelity, preserving structure like tables, headers, and reading order. For digitally-born contracts where layout preservation is needed, the mode is appropriate. For scanned invoices where only text content is needed without layout, the OCR mode, which extracts text LAYOUT from scanned documents and does not preserve layout, is suitable.

NEW QUESTION # 292
A data engineering team is designing a scalable data pipeline in Snowflake that involves processing large text inputs with Cortex AI LLM functions. They want to ensure cost efficiency and prevent queries from failing due to exceeding LLM context window limits. They plan to use SNOWFLAKE. CORTEX. COUNT_TOKENS for pre-validation. Which of the following statements are TRUE about the role and cost of COUNT_TOKENS in this scenario? (Select all that apply)

A. Option B
B. Option D
C. Option C
D. Option E
E. Option A

Answer: C,D,E

Explanation:
Option A is correct. Embedding models like

have a fixed context window of 512 tokens. Using COUNT_TOKENS allows pre-checking if text fits within this limit, preventing truncation that can occur when the input exceeds the context window. Option B is incorrect because COUNT_TOKENS incurs only compute cost to run the function and does not incur additional token-based costs that would add to the billing of subsequent LLM inference calls. Option C is correct. For best search results, Snowflake recommends splitting text into chunks of no more than 512 tokens. This practice generally leads to higher retrieval and downstream LLM response quality in RAG scenarios, and COUNT_TOKENS is a valuable tool for managing these chunk sizes. Option D is incorrect. While COUNT_TOKENS incurs compute cost (not token-based cost), the amount of compute would generally scale with the size of the input text it processes, making the cost not entirely independent of input length, although it's not billed on a per-token basis for its own operation. Option E is correct. The COUNT_TOKENS function is available in all regions for any model, though the models themselves may have specific regional availabilities for other functions.

NEW QUESTION # 293
A machine learning engineer is building a product recommendation system in Snowflake that uses item embeddings and customer query embeddings for similarity matching. They plan to use the VECTOR_L1_DISTANCE function to find the closest products to a user's query. Which statement accurately describes the cost and data type considerations for this approach?

A. Executing the
B. Storing the item and query embeddings in
C. Both
D. The maximum dimension for a
E. The

Answer: C

Explanation:
Option A is incorrect because vector similarity functions, including

, do not incur token-based costs. Option B is incorrect. The VECTOR data type supports elements of either 32-bit integers ( INT ) or 32-bit floating-point numbers ( FLOAT ), and its dimension can be a positive integer up to 4096, regardless of the specific dimension value. Option C is incorrect.

does not incur token-based costs. Conversely, generating embeddings using functions like EMBED_TEXT_768 does incur compute costs based on input tokens. Option D is correct. Vector similarity functions, such as VECTOR_INNER_PRODUCT and VECTOR_L1_DISTANCE , are explicitly stated to not incur token-based costs, unlike embedding functions. Option E is incorrect. The maximum dimension for the VECTOR data type is 4096.

NEW QUESTION # 294
An ML engineer is developing a RAG application in Python and wants to use the TruLens SDK to trace the distinct phases of its execution, specifically the context retrieval and answer generation steps. They aim to clearly differentiate the tracing of the function responsible for retrieving context.

Answer: C

Explanation:
To instrument a function for context retrieval using the TruLens SDK and clearly differentiate its tracing, the decorator should be used with 'span_type=SpanAttributes.SpanType.RETRlEVAL'. This is directly demonstrated in the source for tracing a function with a specific span type. Option B uses a string literal for 'span_type' , which is not the correct way to reference the enum member. Option C uses 'SpanAttributes.SpanType.GENERATlON' , which is intended for LLM inference, not context retrieval. Option D uses the decorator without a specific 'span_type' , which would not clearly differentiate the context retrieval phase. Option E uses non- existent decorators and types(@trace_function', 'spanTypes').

NEW QUESTION # 295
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New GES-C01 Mock Test & GES-C01 Latest Dumps Questions

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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q290-Q295):

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