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| Section | Objectives |
|---|---|
| Model Deployment & Management | - Snowpark Container Services & Model Registry
|
| Document Processing | - AI_PARSE_DOCUMENT, AI_EXTRACT pipelines
|
| Snowflake for Gen AI Overview | - Snowflake Gen AI principles and features
|
| RAG & Vector Search with Cortex Search | - Vector types, semantic search, RAG pipelines
|
| Gen AI Governance & Security | - Privileges, roles, access control
|
| Cortex AI Functions & Application | - Built-in LLM functions
|
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NEW QUESTION # 80
Which costs are associated with Snowflake Cortex Search Services? (Choose two)
Answer: A,C
Explanation:
Snowflake Cortex Search Services incur consumption costs across multiple managed compute and storage categories to provide low-latency hybrid search. The two primary cost components identified in Snowflake's architectural documentation are serving compute and cloud services compute.
Serving compute accounts for the dedicated, serverless query execution resources required to keep the distributed search index in memory and immediately responsive to low-latency retrieval requests. This cost is metered continuously per gigabyte of uncompressed indexed data (including raw text and calculated vector embeddings) for as long as the search service remains in a resumed state, regardless of whether incoming search queries are executed. Cloud services compute is consumed during background index maintenance to inspect underlying source tables, detect data modifications (inserts, updates, deletes), and orchestrate refresh workflows. As with general Snowflake workloads, cloud services compute is charged only when daily usage exceeds the account's standard 10% tolerance threshold against virtual warehouse compute.
Mounting volumes, image repositories, and container data transfer fees are specific to Snowpark Container Services (SPCS) and do not apply to Cortex Search.
References & Study Guide Topics:
* Snowflake Gen AI Functions: Cortex Search Services Architecture and Consumption
* Cost Governance: Managing Serving Compute and Cloud Services Refresh Overhead
NEW QUESTION # 81
A Gen AI Specialist must prepare long documents for LLM ingestion. Which Snowflake Cortex function supports this requirement by returning an array of text chunks?
Answer: B
Explanation:
Snowflake Cortex provides native text-preprocessing functions engineered specifically to support scalable Retrieval-Augmented Generation (RAG) pipelines. When processing large unstructured documents whose length exceeds the optimal context window or chunk size required for dense vector search indexing, SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER must be used.
This function takes an input string and recursively divides it into smaller overlapping or non-overlapping segments based on a prioritized hierarchy of structural separators (such as double line breaks, paragraph boundaries, sentences, and whitespace). It parameterizes target chunk size and chunk overlap, returning the partitioned segments as a Snowflake ARRAY of strings. This structural chunking ensures semantic coherence within each segment while guaranteeing that individual passages remain within token boundaries prior to vectorization.
In contrast, EMBED_TEXT_768 computes vector embeddings from input strings rather than performing token segmentation; FINETUNE executes supervised parameter adaptation for supported base models; and PARSE_DOCUMENT extracts raw text, OCR data, or Markdown layouts from staged documents without segmenting that content into chunk arrays.
References & Study Guide Topics:
* Snowflake Gen AI Functions: Text Chunking and Token Management
* Retrieval-Augmented Generation (RAG): Preprocessing Unstructured Documents via SPLIT_TEXT_RECURSIVE_CHARACTER
NEW QUESTION # 82
When creating a Snowflake Document AI model, which object must have the CREATE SNOWFLAKE.ML.
DOCUMENT_INTELLIGENCE privilege?
Answer: C
Explanation:
Snowflake Document AI leverages zero-shot and fine-tuned multimodal models (specifically the Arctic-TILT architecture) to extract discrete data attributes from unstructured documents. Within Snowflake's object hierarchy, Document AI model builds (DOCUMENT_INTELLIGENCE objects) are managed as first-class, schema-level database entities.
Consequently, to initiate, build, and register a new Document AI pipeline within a target namespace, the user's active role must hold the CREATE SNOWFLAKE.ML.DOCUMENT_INTELLIGENCE privilege on the specific schema where the model build is instantiated (GRANT CREATE SNOWFLAKE.ML.
DOCUMENT_INTELLIGENCE ON SCHEMA < database > . < schema > TO ROLE < role > ;).
Securing database-level permissions provides container traversal (USAGE), but schema-level authority is required to instantiate schema-scoped ML assets. Virtual warehouses provide execution compute rather than metadata creation privileges, and stages serve as raw file storage repositories where stage-level privileges (READ, WRITE) govern file I/O rather than machine learning object creation. Without this explicit schema grant, attempts to initialize a Document AI build via Snowsight or SQL fail with authorization errors.
References & Study Guide Topics:
* Snowflake Document Processing: Document AI Workspace Setup and Permissions
* Object-Level Access Control: Schema Privileges for DOCUMENT_INTELLIGENCE
NEW QUESTION # 83
Which commands can be used to display the Snowflake Cortex LLM models that are available? (Choose two)
Answer: A,D
Explanation:
Foundation models available for serverless Cortex inference are cataloged as first-class model objects within Snowflake's shared system database under the `SNOWFLAKE.MODELS` schema.
To view the complete inventory of available foundation models, developers and administrators can issue:
1. `SHOW MODELS IN SNOWFLAKE.MODELS;`: This directly queries the system-managed models schema, displaying all foundation model identifiers, default versions, and descriptive metadata.
2. `SHOW MODELS;`: When executed without a specific scope (or within the context of the shared system database), this standard metadata command lists the accessible model objects available to the active user role.
The alternative choices are syntactically or functionally invalid:
* `LS @...` lists raw unstructured files stored inside stages, not model registry entities.
* `SHOW VERSIONS LIKE ... IN MODEL LLM` attempts to query versions inside a non-existent individual model object named `LLM`.
* `CALL SNOWFLAKE.MODELS.CORTEX_BASE_MODELS_REFRESH()` triggers an internal system procedure that synchronizes catalog definitions behind the scenes rather than returning a display grid of available models.
References & Study Guide Topics:
* Snowflake Gen AI Functions: Model Discovery and System Catalog Exploration
* Snowflake Model Registry: Managing and Listing System Models via `SHOW MODELS`
NEW QUESTION # 84
Which governance principles does Snowflake guarantee when its Gen AI features and the underlying models are used? (Choose two)
Answer: A,E
Explanation:
Snowflake Cortex AI operates strictly inside Snowflake's secure Data Cloud governance boundary, adhering to comprehensive enterprise trust principles. Under Snowflake's core AI governance framework, customer data, prompts, and completions are never utilized to train or fine-tune third-party foundation models, nor are they ever shared with model providers or other Snowflake accounts. All inference operations execute completely within Snowflake's managed trust perimeter.
Furthermore, Snowflake natively enforces its enterprise Role-Based Access Control (RBAC) across all Generative AI infrastructure and models. Access to specific Cortex LLM functions, proprietary models, and Model Registry artifacts requires explicit object and database-level grants (such as SNOWFLAKE.
CORTEX_USER and designated model roles), ensuring unified administrative oversight across all AI workloads.
Option C is incorrect because fine-tuned models remain private to the host Snowflake account and cannot be shared across disparate consumer accounts. Option D is incorrect because foundation models are deployed as serverless functions or containerized services without requiring direct account-level ingestion of external models exclusively via the Model Registry. Option E is incorrect because Cortex Guard is an optional evaluation parameter, not a mandatory feature.
References & Study Guide Topics:
* Snowflake Gen AI Governance: Data Privacy, Security Boundaries, and Trust Architecture
* Enterprise Security: Model Access Governance and Role-Based Access Control (RBAC)
NEW QUESTION # 85
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