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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q126-Q131):

NEW QUESTION # 126
A developer is integrating a Cortex Fine-tuning pipeline into an automated data workflow and needs to ensure structured outputs and monitor the process effectively. They are also aware of certain architectural limitations within Snowflake. Which of the following statements regarding advanced usage or limitations of Snowflake Cortex Fine-tuning and related LLM functions are accurate? (Select all that apply)

Answer: A,C,D

Explanation:
Option A is correct. For the most consistent results from
COMPLETE
(which is used for inference with fine-tuned models), it is explicitly recommended to set the temperature option to 0, irrespective of the task or model used. Option B is incorrect. Fine-tuning jobs are designed as long-running processes that are tied to a specific worksheet session, allowing users to check their status independently after initiation. Option C is correct. The FINETUNE function has specific regional availability, meaning that the creation of fine-tuning jobs is restricted to accounts in those supported regions. Cross-region inference primarily applies to COMPLETE function calls for LLM inference, not the fine-tuning training process itself. Option D is incorrect. Snowflake Cortex functions, including those for fine-tuning, do not support dynamic tables. Furthermore, dynamic tables have limitations regarding non-deterministic code and stored procedures, which are often involved in complex AI pipelines. Option E is correct. Fine-tuning jobs are long-running, and their status and progress can be monitored by calling the FINETUNE function with the 'DESCRIBE' argument, providing the generated job ID.


NEW QUESTION # 127
A Snowflake team observes consistently high token costs from 'SNOWFLAKE.ACCOUNT_USAGE.CORTEX_FUNCTIONS_QUERY_USAGE_HISTORY' for a summarization task using the 'mistral- large2' 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?

Answer: C

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 with Cortex Search. 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 'TRY COMPLETE 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 # 128
A development team is preparing to deploy a new Retrieval-Augmented Generation (RAG) application written in Python. They intend to use Snowflake AI Observability to capture detailed logs and traces for debugging and performance analysis. Which of the following configurations are essential prerequisites for enabling this logging capability effectively?

Answer: A,B,D,E

Explanation:


NEW QUESTION # 129

Which of the following SQL snippets, when executed against a single invoice file like "invoice001 .pdf", correctly extracts and transforms the desired data, assuming 'json_content' holds the raw Document AI output?

Answer: A

Explanation:
Option B correctly uses a Common Table Expression (CTE) to retrieve the raw JSON output from (which is a Document AI method for extracting information from documents in a stage), leveraging to access the document. It then accesses the 'invoice_number' and 'vendor_name' using .value' syntax, appropriate for values returned as an array containing a single object with a 'value' field, as shown in Document AI output examples. The 'LATERAL FLATTEN' clause is correctly applied to expand the array of line items, and 'ARRAY_AGG' combined with 'ARRAY _ TO STRING' converts these items into a comma-separated string. Finally, it groups by the single-value extracted fields.
Option A attempts to flatten the result multiple times or in an incorrect way within the SELECT statement without a proper FROM' clause for the flattened data, leading to inefficient or incorrect aggregation. Option C directly references a staged file path (@invoice_docs_stage/invoice001.pdf) without the necessary GET PRESIGNED URL' function, which is required when calling '!PREDICT' with a file from a stage. It also incorrectly assumes direct .value' access for array-wrapped single values and does not correctly transform the 'invoice_itemS array into a string. Option D's subquery for 'ARRAY AGG' is syntactically problematic for direct column access from the outer query without explicit 'LATERAL FLATTEN' at the top level. Option E only extracts the 'ocrScore' from the document metadata and does not perform the requested data transformations.


NEW QUESTION # 130
An engineering team is building an advanced analytics pipeline where daily customer activity vectors (each 512 dimensions) are compared against a set of known activity patterns using VECTOR_L2_DISTANCE The pipeline is orchestrated using Snowflake Tasks. Which operational best practice or limitation should the team consider when processing these vector distances at scale?

Answer: A

Explanation:
Option A is incorrect. Snowflake recommends executing queries that call Cortex AI SQL functions with a smaller warehouse (no larger than MEDIUM), as larger warehouses do not increase performance. Snowpark-optimized warehouses are generally recommended for workloads with large memory requirements or dependencies on specific CPU architectures, not as a general performance booster for Cortex AI functions. Option B is incorrect.
TRY COMPLETE
is a function designed for LLM completions (like
COMPLETE
) to return
NULL
instead of an error, and it is not applicable to vector distance functions like VECTOR_L2_DISTANCE Option C is incorrect. The VECTOR data type is not supported for use with dynamic tables. Option D is incorrect. Bind variables are not supported with the VECTOR data type. Option E is correct. Snowflake's documentation explicitly states that queries calling Cortex AI SQL functions, which includes vector similarity functions, perform optimally on smaller warehouses (no larger than MEDIUM) because larger warehouses do not increase performance for these specific functions.


NEW QUESTION # 131
......

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