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1. A data engineer is setting up an automated pipeline to process incoming documents using Snowflake's AI_PARSE DOCUMENT function. To ensure the pipeline functions without errors, which of the following input requirements or limitations must be strictly adhered to?
A) To optimize performance, queries calling the Cortex AI_PARSE_DOCUMENT function should be executed in a large warehouse (e.g., XL or larger), as bigger warehouses increase processing speed for this function.
B) All documents must be stored in an internal or external stage, but only internal stages support server-side encryption with the SNOWFLAKE_SSE type.
C) Supported file formats for processing include PDF, PPTX, DOCX, JPEG, JPG, PNG, TIFF, TIF, HTML, and TX T, covering a wide range of common document and image types.
D) For HTML and TXT files, billing for AI_PARSE_DOCUMENT is based on each chunk of 3,000 characters, with the last chunk also being billed even if shorter.
E) The maximum file size for any document processed by is AI_PARSE_DOCUMENT 100 MB, and it can handle up to 300 pages per document.
2. A data scientist is working on a new feature that involves querying a Cortex Search Service and integrating the results into various downstream processes. They are concerned about potential data type compatibility issues and limitations within the Snowflake environment. Which of the following statements accurately describe how to query a Cortex Search Service or the limitations of the 'VECTOR data type and Cortex Search itself? (Select all that apply)
A) Cortex Search Services can only be queried using the 'SNOWFLAKE.CORTEX.SEARCH_PREVIEW' SQL function and do not offer a programmatic interface for applications.
B) The 'VECTOR data type, which stores the output of embedding models like those used by Cortex Search, is explicitly not supported in 'VARIANT columns.
C) To retrieve the support ticket most relevant to a query about 'internet issues', filtered to return results only in the 'North America' region, one can use the 'filter' parameter in the 'SEARCH PREVIEW function's JSON argument like so:
D) The "VECTOR data type, used to store embeddings generated for Cortex Search, is fully supported as a primary key in Snowflake's hybrid tables to accelerate similarity searches.
E) Cortex Search Services can be configured as a source for Snowflake dynamic tables, enabling continuous and automated synchronization of the search index with base data.
3. An organisation is deploying a Snowflake Cortex Agent to assist business users with data insights. To enable users to interact with this agent via the agent : run API, which of the following database roles or privileges must be granted to their account role?
A) CREATE EXTERNAL AGENT
B) The
C) The
D) The
E) The
4. A team is designing a complex Gen AI application in Snowflake, which includes components for training a custom LLM, running batch inference, and providing a real-time conversational interface. They plan to leverage Snowpark Container Services (SPCS) for these workloads. Which of the following statements accurately describe the suitable SPCS service design models and important considerations for these different application components? (Select all that apply.)
A) GPU-accelerated LLM training, which is a finite and often resource-intensive task, is best implemented as a ''job'' in SPCS, invoked via "EXECUTE JOB SERVICE', as it is designed to run to completion and then spin down.
B) Container images for SPCS deployments are typically pushed to a public Docker Hub repository, and Snowflake pulls them as needed during service creation and scaling, simplifying image management.
C) Real-time LLM inference for a conversational interface is ideally deployed as a ' 'Service' ' in SPCS, which is long-running and accessible via an HTTP endpoint, ensuring continuous availability and responsiveness.
D) For batch inference on Snowflake data where data locality and efficiency are key, using ''Service Functions'' is highly efficient because data is passed as input parameters directly from SQL queries, and this design ensures the data never leaves the Snowflake network boundary.
E) When deploying LLMs to SPCS, it's generally most cost-efficient to use generic CPU instance types like 'CPU X64 XS' for all tasks, as GPU instances (e.g., are exclusively for highly specialized computer vision tasks and not optimized for LLMs.
5. A data engineering team is building a pipeline in Snowflake that uses a SQL task to call various Snowflake Cortex LLM functions (e.g., AI_COMPLETE, AI EMBED) on large datasets of customer interaction logs. The team observes fluctuating costs and occasional query failures, which sometimes halt the pipeline. To address these issues and ensure an efficient, robust, and monitorable pipeline, which of the following actions or considerations are essential? (Select all that apply.)
A) Option B
B) Option E
C) Option C
D) Option D
E) Option A
Solutions:
| Question # 1 Answer: C,D,E | Question # 2 Answer: B,C | Question # 3 Answer: B | Question # 4 Answer: A,C,D | Question # 5 Answer: A,B,E |
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