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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q236-Q241):

NEW QUESTION # 236
A data analyst is working with a table named ARTICLE_CONTENT that contains a column (VARCHAR) storing lengthy English articles. They need to generate a concise summary for each article. The analyst plans to use the SNOWFLAKE. CORTEX. SUMMARIZE function. Which of the following accurately describes the syntax and the expected data type of the result for a single article summary?

A. The query
B. The query
C. The query
D. The query
E. The query

Answer: A

Explanation:
Option B is correct. The 'SNOWFLAKE.CORTEX.SUMMARIZE function takes a single string argument, which is the English- language input text to be summarized. It returns a string containing a summary of the original text. Options A, C, D, and E describe incorrect syntax, return types, or additional arguments not supported by the 'SUMMARIZE' function.

NEW QUESTION # 237
A data engineer is building a robust pipeline to process customer feedback. They need to extract specific sentiment categories (food_quality, food_taste, wait_time, food _cost) from text reviews and ensure the output is always a valid JSON object matching a predefined schema, even for complex reviews. They also want to control the determinism of the LLM responses. Which of the following SQL statements or considerations are correct for achieving this using Snowflake Cortex AI functions?

A. For the most consistent structured output, especially in complex reasoning tasks, setting the temperature option to 0 when calling AI_COMPLETE is recommended.
B. To ensure the model explicitly attempts to extract all specified fields, the 'required' array in the JSON schema is critical; AI_COMPLETE will raise an error if any required field cannot be extracted.
C. The response_format argument with a JSON schema is primarily for OpenAl (GPT) models; for other models like Mistral, a strong prompt instruction such as 'Respond in strict JSON' is generally more effective.
D. Using AI_COMPLETE with response_format incurs additional compute cost for the overhead of verifying each token against the supplied JSON schema, in addition to standard token costs.
E. The following SQL statement uses the response_format argument and temperature setting to achieve structured output and determinism:

Answer: A,B,E

Explanation:
Option A is correct because it demonstrates the proper use of the 'AI_COMPLETE function with the 'response_format' argument to specify a JSON schema and sets 'temperature' to 0 for consistent output, as per the documentation. Option C is correct as the "required'' field in the JSON schema ensures that specific fields must be extracted, and 'COMPLETE (or 'AI_COMPLETE) will raise an error if these fields cannot be found. Option E is correct because for the most consistent results, setting the 'temperature* option to O is recommended when calling 'COMPLETE (or "AI_COMPLETE) with structured outputs, regardless of the task or model. Option B is incorrect because all models supported by support structured output, and specifying the 'response_format' is the direct mechanism to enforce a schema, although for complex tasks, adding 'Respond in JSON' to the prompt can improve accuracy. Option D is incorrect as 'AI_COMPLETE Structured Outputs incurs compute cost based on the number of tokens processed, but it does not incur additional compute cost for the overhead of verifying each token against the supplied JSON schema.

NEW QUESTION # 238
A Gen AI Specialist in Snowflake Cortex aims to fine-tune an LLM for enhanced task-specific performance. When creating a fine-tuning job using SNOWFLAKE. CORTEX. FINETUNE( 'CREATE', ... ) , which statement accurately describes the required training data format and a supported base model, aligning with Snowflake's Gen AI principles for leveraging LLMs?

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

Answer: D

Explanation:
To create a fine-tuning job, the 'SNOWFLAKE.CORTEX.FINETUNE('CREATE', ...y function requires the training data query to return columns named 'prompt' and 'completion'. The 'mistral-7b& model is listed as a supported base model for fine-tuning. Models like 'llama4- maverick' , 'openai-gpt-4.1', 'snowflake-arctic' , and 'claude-4-opuS are not listed as base models available for fine-tuning using this function.

NEW QUESTION # 239
A data engineer is designing a new feature for a Retrieval Augmented Generation (RAG)-based application in Snowflake. They plan to store document embeddings and perform semantic similarity searches to retrieve relevant context for an LLM. Which of the following statements about using the VECTOR data type and related functions in Snowflake are true? (Select all that apply.)

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

Answer: A,C,E

Explanation:

NEW QUESTION # 240
A data engineer is working with Snowflake Cortex Analyst to improve its ability to answer natural language questions by precisely identifying product names for filtering. They have decided to integrate a Cortex Search Service with their semantic model to enhance literal search for the 'product_name' dimension. Which of the following configurations within the semantic model's YAML file are valid and effective for this purpose?

A. Adding a entry to the 'product_name' dimension, including 'literal_column' and ensuring the search service is configured to index the physical column:
B. Adding a 'cortex_search_service' entry to the 'product_name' dimension with only the 'service' field:
C. Including in the semantic model's 'metrics' section, referencing 'product_name'.
D. Only specifying 'sample_valueS for the 'product_name' dimension without a entry.
E. Setting true' for the 'product_name' dimension and providing an exhaustive list of to restrict the model to only those values.

Answer: A,E

Explanation:
Option C is correct because integrating a Cortex Search Service with Cortex Analyst for literal search improvement involves adding a entry to the relevant dimension. Specifying the 'literal_column' explicitly helps map the semantic dimension to the underlying column indexed by the search service, ensuring the correct values are used for semantic search. Option D is also correct. For dimensions with relatively low-cardinality values, setting 'is_enum: true and providing 'sample_values' tells the model to choose only from that predefined list, which is an effective way to improve literal usage without requiring an external Cortex Search Service. Option A is incorrect because while 'sample_valueS are used for semantic similarity search for low-cardinality dimensions, they don't leverage the full capabilities of a Cortex Search Service for 'fuzzy' search over potentially high-cardinality data. Option B is incomplete. While specifying the 'service' name is a start, it's beneficial to explicitly define the 'literal_column' if it differs from the dimension's expression or if precision is critical. Option E is incorrect because is a configuration for a 'dimension', not a 'metric'.

NEW QUESTION # 241
......

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