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| Section | Objectives |
|---|---|
| Cortex AI Functions & Application | - Built-in LLM functions
|
| RAG & Vector Search with Cortex Search | - Vector types, semantic search, RAG pipelines
|
| Snowflake for Gen AI Overview | - Snowflake Gen AI principles and features
|
| Model Deployment & Management | - Snowpark Container Services & Model Registry
|
| Gen AI Governance & Security | - Privileges, roles, access control
|
| Document Processing | - AI_PARSE_DOCUMENT, AI_EXTRACT pipelines
|
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NEW QUESTION # 35
A Gen AI Specialist has deployed a base model as an LLM in a production environment for a proof of concept. The model is larger and more expensive than other base models.
How can the Specialist lower production costs without affecting performance for the limited tasks needed to support the proof of concept?
Answer: A
Explanation:
In enterprise generative AI architectures, frontier foundation models (such as 70B+ parameter variants) offer strong general reasoning but incur higher credit consumption per token. When supporting a defined, specialized task for a proof of concept, relying on an oversized base model is often cost-inefficient.
Snowflake Cortex Fine-tuning (`SNOWFLAKE.CORTEX.FINETUNE`) allows developers to adapt a smaller, lower-cost foundation model (such as `llama3-8b` or `mistral-7b`) using domain-specific training data. By training on prompt-completion pairs representing the targeted workload, the smaller base model learns the required domain vocabulary, output formats, and task logic.
This specialized model can achieve task performance comparable to larger foundation models while running on a smaller parameter footprint, significantly reducing per-token credit consumption in production.
Cortex Search handles document retrieval rather than adapting model reasoning efficiency; Cortex Agents orchestrate multi-tool reasoning rather than optimizing inference billing; and semantic models support Cortex Analyst text-to-SQL generation rather than general completion cost optimization.
References & Study Guide Topics:
* Snowflake Gen AI Functions: Cortex Fine-Tuning Architecture and Economics
* LLM Optimization: Parameter Sizing, Model Distillation, and Domain Adaptation
NEW QUESTION # 36
Which governance principles does Snowflake guarantee when its Gen AI features and the underlying models are used? (Choose two)
Answer: A,B
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 # 37
Which view in the ACCOUNT_USAGE schema can query token costs for Snowflake Cortex LLMs aggregated in one-hour increments?
Answer: D
NEW QUESTION # 38
The following error is received while attempting to extract details from a document with Snowflake Document AI:
`Request failed for external function DOCUMENT_EXTRACT_FEATURES$V1 with remote service error:
422`
How should this error be resolved?
Answer: B
Explanation:
When training and deploying custom extraction pipelines in Snowflake Document AI, a model build must transition through distinct development phases: drafting, fine-tuning, and publication.
During development, users annotate documents and define extraction values within the Document AI studio.
However, invoking the underlying programmatic scoring engine (which surfaces behind the scenes as
`DOCUMENT_EXTRACT_FEATURES$V1` or ` < model_name > !PREDICT`) against uncommitted builds triggers an HTTP 422 (Unprocessable Entity) remote service error. This occurs because the model build is still in a draft state and has not been compiled into an active, callable deployment artifact.To resolve this issue, the engineer must explicitly **publish** the model build within Snowsight (or complete the build lifecycle). Publishing freezes the annotated schema, provisions the serving endpoints, and registers the operational version so that SQL queries can execute inference. Privilege deficiencies return authorization errors (e.g., 403 or Access Denied) rather than remote service error 422.References & Study Guide Topics:* Snowflake Document Processing: Document AI Model Build Lifecycle* Troubleshooting: Publishing Document AI Models and Resolving 422 Extraction Errors===============
NEW QUESTION # 39
Which commands can be used to display the Snowflake Cortex LLM models that are available? (Choose two)
Answer: A,C
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 # 40
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