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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q226-Q231):

NEW QUESTION # 226
A security engineer is developing an application that uses the Snowflake Cortex REST API to interact with LLMs, specifically to obtain structured outputs for text classification and to ensure secure communication. They are focusing on the /api/v2/cortex/ inference : complete endpoint. Which of the following statements correctly describe aspects of this interaction?

A. To strictly enforce a JSON schema for the LLM's response, the response_format parameter must be included in the request body, supplied as a JSON schema object, which helps reduce post-processing efforts.
B. For models like OpenAI (GPT) used via the Cortex REST API with structured output, the JSON schema in the response_format field must include "additionalProperties": false and a "required" field listing all properties at every node.
C. The Cortex REST API for LLM inference always returns the complete LLM response as a single, fully-formed JSON object once generation is finished, regardless of any streaming options.
D. Authentication for Cortex REST API requests is primarily handled through an Authorization: Bearer header, where the token can be a JSON Web Token (JWT), OAuth token, or programmatic access token.
E. To ensure the most consistent and deterministic structured output from the LLM, it is recommended to set the temperature option to a higher value, such as 0.7 or 1.0, in the request payload.

Answer: A,B,D

Explanation:

NEW QUESTION # 227
A data analyst needs to use SNOWFLAKE. CORTEX. EXTRACT_ANSWER to streamline information retrieval from various contract documents. They are new to Cortex functions and want to understand access requirements and optimal usage. Which of the following statements about using SNOWFLAKE .CORTEX. EXTRACT_ANSWER are correct?

A. It is generally recommended to process multiple documents by applying EXTRACT_ANSWER to a table column containing the document texts, allowing for efficient batch processing.
B. For optimal accuracy, the source_document input should be in plain English, and the question should be specific, asking for a single value.
C. The analyst's role must be granted the SNOWFLAKE. CORTEX_USER database role to execute EXTRACT_ANSWER functions.
D. EXTRACT_ANSWER is the most current and recommended function for all text extraction tasks, offering multi-label and image extraction capabilities.
E. If EXTRACT_ANSWER encounters an unresolvable issue during processing, it returns NULL instead of an error, similar to TRY_COMPLETE.

Answer: A,B,C

Explanation:
Option A is correct because users must use a role that has been granted the 'SNOWFLAKE.CORTEX USER database role to access EXTRACT_ANSWER and other Cortex AI functions. Option B is correct because for optimal performance and accurate responses, it is recommended to use plain English text for input and categories, and questions should be specific and ask for a single value. Option C is incorrect; EXTRACT_ANSWER would typically raise an error if an operation cannot be performed. The 'TRY_COMPLETE function is specifically designed to return 'NULL' instead of an error in such cases. Option D is incorrect; 'EXTRACT ANSWER is an older version of this function, and 'AI_EXTRACT' is the latest version, which supports additional capabilities like image and multi-language extraction. Option E is correct; EXTRACT ANSWER can be called on a table column, enabling efficient batch processing of multiple documents or text entries. This is a common pattern for integrating Cortex functions into data pipelines.

NEW QUESTION # 228
A Snowflake developer, 'AI _ ENGINEER , is creating a Streamlit in Snowflake (SiS) application that will utilize a range of Snowflake Cortex LLM functions, including SNOWFLAKE. CORTEX. COMPLETE, SNOWFLAKE .CORTEX.CLASSIFY TEXT, and SNOWFLAKE. CORTEX. EMBED TEXT 768. The application also needs to access data from tables within a specific database and schem a. 'AI _ ENGINEER has created a custom role, for the application to operate under. Which of the following privileges or roles are absolutely necessary to grant to for the successful execution of these Cortex LLM functions and interaction with the specified database objects? (Select all that apply.)

A. The USAGE privilege on the specific database and schema where the Streamlit application and its underlying data tables are located.
B.
C. The CREATE COMPUTE POOL privilege to provision resources for the Streamlit application.
D.
E. The ACCOUNTADMIN role to ensure unrestricted access to all Snowflake Cortex features.

Answer: A,D

Explanation:
To execute Snowflake Cortex AI functions such as 'SNOWFLAKE.CORTEX.COMPLETE , 'SNOWFLAKE.CORTEX.CLASSIFY_TEXT , and (or their 'AL' prefixed counterparts), the role used by the application in this case) must be granted the 'SNOWFLAKE.CORTEX USER database role. Additionally, for the application to access any database or schema objects (like tables for data input/output or storing the Streamlit app itself), the 'USAGE privilege must be granted on those specific database and schema objects. option B, 'CREATE SNOWFLAKE.ML.DOCUMENT_INTELLIGENCE , is a privilege specific to creating Document AI model builds and is not required for general Cortex LLM functions. Option D, 'ACCOUNTADMIN', grants excessive privileges and is not a best practice for application roles. Option E, 'CREATE COMPUTE POOL' , is a privilege related to Snowpark Container Services for creating compute pools, which is not directly required for running a Streamlit in Snowflake application that consumes Cortex LLM functions.

NEW QUESTION # 229
A data analyst is tasked with identifying customers who purchased items with similar feature vectors. They have a table products with an

to measure similarity. Which of the following statements correctly describe aspects of defining and using vector types or functions in this scenario? (Select all that apply)

A. Comparing two
B. The Snowpark Python library provides native support for calling
C. When inserting literal arrays as vectors into a table for comparison, explicit casting, e.g.,
D. To correctly define a column to store 768-dimensional float embeddings, the SQL statement
E. If

Answer: B,C,D

Explanation:
Option A is correct. The syntax for specifying a

) are byte-wise lexicographic and do not produce semantically expected results for number comparisons; dedicated vector similarity functions should be used instead. Option D is correct. The Snowpark Python library supports the VECTOR data type and vector similarity functions. While the sources specifically mention VECTOR_L2_DISTANCE in a Snowpark Python example, VECTOR_L1_DISTANCE is listed as one of the four core vector similarity functions provided by Snowflake Cortex, implying similar support in Snowpark Python. Option E is correct. SQL examples demonstrate the necessity of explicit casting when using array literals as vectors, such as .

NEW QUESTION # 230
A data engineering team is building a Retrieval Augmented Generation (RAG) pipeline that heavily relies on 'SNOWFLAKE.CORTEX.EMBED_TEXT 768' to process millions of documents daily. They need to optimize for both cost and retrieval quality. Which of the following statements are true regarding the cost and performance of 'EMBED_TEXT 768' in Snowflake? (Select all that apply)

A. For optimal retrieval quality in RAG scenarios, text should be split into chunks of no more than 512 tokens before being passed to 'EMBED TEXT 768', even if the model supports a larger context window.
B. The 'EMBED_TEXT 768' function is billed based on the number of 'output tokens' generated by the embedding model, as this represents the computational complexity of the vector.
C. To minimize costs for ' EMBED_TEXT 768 operations, it is recommended to execute queries using a smaller virtual warehouse (no larger than MEDIUM), as larger warehouses do not improve performance for these functions.
D. The 'snowflake-arctic-embed-m-vl .5 model, used by 'EMBED TEXT 768', has a context window of 512 tokens, and texts exceeding this length are truncated before embedding.
E. The 'EMBED TEXT 768' function, regardless of the 768-dimension model used, has a fixed cost of 1.50 Credits per one million Tokens processed.

Answer: A,C,D

Explanation:
Option B is correct because Snowflake recommends executing queries that call Cortex AISQL functions, including , with a smaller warehouse (no larger than MEDIUM). Larger warehouses do not increase performance for these functions. Option C is correct because the 'snowflake-arctic-embed-m-vl .5 model, which can be used with 'EMBED TEXT 768', has a context window of 512 tokens. Input text exceeding this limit is truncated before embedding. Option D is correct because, for best search results with Cortex Search and RAG, Snowflake recommends splitting the text into chunks of no more than 512 tokens. This smaller chunk size typically results in higher retrieval and downstream LLM response quality. Option A is incorrect because for functions, only 'input tokens' are counted towards the billable total, not output tokens. Option E is incorrect because the cost for models (such as 'e5-base-v2 , snowflake-arctic-embed-m' , 'snowflake-arctic-embed-m-v1.5') is 0.03 Credits per one million Tokens processed. The 1.50 Credits per one million Tokens applies to the ' TRANSLATE' function, not 'EMBED_TEXT_768'.

NEW QUESTION # 231
......

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