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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q218-Q223):

NEW QUESTION # 218
An administrator has configured the 'CORTEX MODELS ALLOWLIST parameter to only permit the 'mistral-large? model at the account level. A user with the 'PUBLIC' role, which has been granted 'SNOWFLAKE.CORTEX USER and 'SNOWFLAKE."CORTEX- MODEL-ROLE-LLAMA3.1-70B"' , attempts to execute several 'COMPLETE (SNOWFLAKE.CORTEX)' queries. Which of the following queries will successfully execute?

Answer: C,E

Explanation:
Option A is correct because the query directly references 'MISTRAL-LARGE2', which is explicitly in the account-level , so it will succeed. Option B is correct because when a model name is provided as a fully-qualified string argument (e.g., Cortex treats it as an identifier for a schema-level model object. If found, role-based access control (RBAC) is applied. The user's role has granted, which provides access to the 'LLAMA3.1-70B' model object in 'SNOWFLAKE.MODELS, regardless of the setting for plain model names. Option C is incorrect. When 'llama3.1-70b' is provided as a plain model name string, Cortex first attempts to match it as a schema-level model object (e.g., 'SNOWFLAKE.MODELS.llama3.1-70b). If this direct match fails (which is typical for a plain, unquoted name not exactly matching a model object identifier), it then checks the plain name 'llama3.1-70b' against the 'CORTEX MODELS ALLOWLIST. Since 'llama3.1- 70b' is not in the allowlist, this query will fail, even though the user has access to the fully-qualified model object. Option D is incorrect as 'snowflake-arctic' is neither in the 'CORTEX MODELS ALLOWLIST' nor does the user have a specific application role granting access to a 'snowflake-arctic' model object. Option E is incorrect because 'ALTER ACCOUNT operations can only be performed by the 'ACCOUNTADMIN' role, not by a user with the FUBLIC' role.

NEW QUESTION # 219
A Snowflake team observes consistently high token costs from 'SNOWFLAKE.ACCOUNT USAGE.CORTEX_FUNCTIONS_QUERY_USAGE_HISTORY' for a summarization task using the 'mistral- large? model. The task involves summarizing legal documents, which often exceed the context window of common LLMs. To optimize these token-based costs, which strategy should the team prioritize?

A. Switch from using the COMPLETE function to TRY_COMPLETE to automatically avoid billing for queries that fail due to context window limits, thereby reducing costs.
B. Increase the virtual warehouse size (e.g., from X-SMALL to MEDIUM) used for running the summarization queries to boost performance and reduce overall cost per query.
C. Set the temperature parameter to 0 in the COMPLETE function options to ensure more deterministic and thus more cost-efficient summarization outputs.
D. Enable Cortex Guard for the COMPLETE function calls, as its filtering capabilities automatically reduce the number of tokens processed for unsafe content.
E. Implement a text splitting mechanism, potentially using SPLIT_TEXT_RECURSIVE_CHARACTER, to break down lengthy documents into smaller chunks before passing them to the summarization function, then aggregate the summaries.

Answer: E

Explanation:
Option C is correct. For summarization of lengthy documents, exceeding the context window or using large inputs significantly increases token consumption. Text splitting, for example using can break documents into smaller, more manageable chunks. This reduces the number of input tokens per LLM call, directly leading to cost optimization, and is recommended for best search results and LLM response quality. Option A is incorrect because for Cortex AISQL functions, Snowflake recommends using a smaller warehouse (no larger than MEDIUM) as larger warehouses do not increase performance but can result in unnecessary costs associated with keeping the warehouse active. The compute cost for Cortex LLM functions is based on tokens processed, not warehouse size performance. Option B is incorrect because only prevents costs for 'failed' operations by returning NULL instead of an error. It does not optimize the token consumption of 'successful' summarization tasks. Option D is incorrect; Cortex Guard processes additional tokens for its filtering, thus 'increasing' token consumption, not reducing it. Option E is incorrect because setting 'temperature' to 0 makes the output more deterministic, which might improve consistency but does not directly reduce the number of input or output tokens processed for a summarization task.

NEW QUESTION # 220
A data team has implemented a Snowflake data pipeline using SQL tasks that process customer call transcripts daily. This pipeline relies heavily on SNOWFLAKE. CORTEX. COMPLETE() (or its updated alias) for various text analysis tasks, such as sentiment analysis and summary generation. Over time, they observe that the pipeline occasionally fails due to LLM-related errors, and the compute costs are higher than anticipated. What actions should the team take to improve the robustness and cost-efficiency of this data pipeline? (Select all that apply.)

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

Answer: B,C,E

Explanation:
A). ''Correct." performs the same operation as 'COMPLETE(Y but returns 'NULC on failure instead of raising an error when the LLM operation cannot be performed. This is critical for building robust data pipelines, as it prevents pipeline halts due to transient or specific LLM failures. B. ''Incorrect." Snowflake recommends executing queries that call Cortex AISQL functions with a smaller warehouse (no larger than MEDIUM). Larger warehouses do not necessarily increase performance but can lead to unnecessary costs, as the LLM inference itself runs on Snowflake-managed compute, not solely on the user's virtual warehouse compute size. C. "Correct." The 'SNOWFLAKE.ACCOUNT USAGE.CORTEX_FUNCTIONS USAGE HISTORY view provides detailed information on token consumption and credit usage for Snowflake Cortex LLM functions. Monitoring this view is essential for understanding cost drivers and optimizing expenditure within AI pipelines. D. ' 'Correct." Snowflake Cortex AI functions incur compute costs based on the number of tokens processed (both input and output). Optimizing prompt engineering to be concise and effective directly contributes to reducing the number of tokens consumed and, therefore, the associated costs. E. "Incorrect." Setting the 'temperature' parameter to 1.0 makes the LLM's output more diverse and random. While useful for creativity, it does not guarantee a reduction in token usage or a lower error rate. For the most consistent results, setting 'temperature' to 0 is generally recommended.

NEW QUESTION # 221
A development team is constructing a Gen AI application using Snowflake Cortex LLM functions, particularly for conversational and text generation tasks. They are concerned about potential high costs due to token consumption. Which of the following strategies would most effectively help minimize token usage and optimize costs when working with these Cortex LLM functions?

A. When employing AI_COMPLETE for structured output tasks, providing concise and highly descriptive explanations for each field within the JSON schema will reduce the input tokens required for the LLM to understand and adhere to the schema accurately.
B. In multi-turn conversations within Cortex Analyst, integrate a dedicated LLM summarization agent to rephrase follow-up questions, which reduces the total conversational history passed as context to the main LLM.
C. Utilize the COUNT_TOKENS (SNOWFLAKE .CORTEX) helper function to pre-validate the prompt length against the model's context window, thereby preventing truncation errors and subsequent re-runs.
D. To encourage more succinct LLM responses and reduce completion_tokens, configure the temperature option to a higher value (e.g., 0.7) in COMPLETE function calls.
E. For multi-turn conversational experiences using SNOWFLAKE .CORTEX.COMPLETE, only send the most recent user prompt in each API call, as the model automatically retains previous context.

Answer: A,B,C

Explanation:
Option B is correct because while schema verification itself doesn't incur extra cost, a large or complex schema can increase token consumption. Providing precise and concise descriptions for schema fields helps the LLM understand and adhere to the desired format more efficiently, potentially reducing the overall tokens consumed for accurate responses. Option C is correct as the "COUNT _ TOKENS' function allows developers to determine the token count of an input prompt for a specific model, enabling them to pre-emptively avoid exceeding the model's context window, thus preventing errors and wasted compute from re-runs. Option E is correct because for multi-turn conversations in Cortex Analyst, a summarization agent is specifically used to rephrase follow-up questions by incorporating previous context, without passing the entire, potentially long, conversation history. This significantly reduces the 'prompt_tokens' sent to the main LLM for each turn and optimizes inference times. Option A is incorrect because 'COMPLETE' (and 'TRY_COMPLETE) functions are stateless; to maintain conversational context, all previous user prompts and model responses must be included in the array, which increases token count proportionally. Simply sending the latest prompt would lose context. Option D is incorrect as setting a higher 'temperature' value (e.g., 0.7) increases the 'randomness and diversity' of the LLM's output, not necessarily its conciseness for cost optimization. For the most consistent (and often direct) results, a 'temperature' of 0 is recommended.

NEW QUESTION # 222
A data engineer is building an AI data pipeline to automatically extract specific sentiment categories from customer reviews using 'AI_COMPLETE. They want the output to be a structured JSON object containing 'food_quality', 'food_taste', 'wait_time', and 'food cost' with their respective sentiments (e.g., 'positive', 'negative', 'neutral'). The engineer aims for high accuracy and ensures that all these fields are present in the output. Which of the following statements correctly describe the approach to achieve this?

A. The 'temperature' option should be set to a high value (e.g., 0.9) in the 'CompleteOptionS to encourage the model to strictly adhere to the specified JSON schema and reduce non-deterministic responses.
B. For optimal accuracy in structured output, it is always best to explicitly instruct the LLM within the prompt to 'Respond in JSON' and provide a detailed description of the schema, regardless of task complexity.
C. When using OpenAl (GPT) models for structured output, the 'additionalPropertieS field must be set to 'true' in every node of the schema to allow for flexibility in the generated JSON.
D. To improve accuracy, the JSON schema should include detailed descriptions for each field (e.g., 'food_quality', 'food_taste') to guide the model more precisely in identifying and extracting the sentiments.
E. The 'response_format' argument in 'AI COMPLETE should specify a JSON schema, and to ensure all fields are present, the 'required' field must be explicitly set for all desired properties in the schema.

Answer: D,E

Explanation:
Option A is correct. The argument is used to supply a JSON schema that completion responses must follow, and including the 'required' field in the schema ensures that specified fields are present in the output, with raising an error if a required field cannot be extracted. Option B is incorrect. While prompting the model to 'Respond in JSON' can improve accuracy for complex tasks, it is not always necessary for simple tasks, as 'AI_COMPLETE Structured OutputS already understands that its response should conform to the specified schema. Option C is incorrect. For OpenAl (GPT) models, the 'additionalPropertieS field must be set to 'false' in every node of the schema. Option D is correct. Providing detailed descriptions of the fields to be extracted helps the model more accurately identify them, thereby improving the quality of the structured output. Option E is incorrect. For the most consistent results, it is recommended to set the 'temperature' option to 0 when calling 'COMPLETE (or regardless of the task or model, to make the output more deterministic and focused.

NEW QUESTION # 223
......

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2026 Realistic Snowflake GES-C01 Interactive Practice Exam Free PDF Quiz

Exams GES-C01 Torrent | New GES-C01 Test Notes

Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q218-Q223):

Free PDF 2026 GES-C01: SnowPro® Specialty: Gen AI Certification Exam High Hit-Rate Interactive Practice Exam

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