DumpTOP 에서 제공해드리는 Snowflake GES-C02덤프는 아주 우수한 IT인증덤프자료 사이트입니다. IT업계엘리트한 강사들이 퍼펙트한 Snowflake GES-C02 덤프문제집을 제작하여 디테일한 시험문제와 답으로 여러분이 아주 간단히Snowflake GES-C02시험을 패스할 수 있도록 최선을 다하고 있습니다.
| Section | Weight | Objectives |
|---|---|---|
| Snowflake Gen AI Governance | 29% | - Cost Management and Monitoring
|
| Snowflake for Gen AI Overview | 18% | - Generative AI Fundamentals and Snowflake Cortex
|
| Snowflake Gen AI and LLM Functions | 38% | - Production AI Workflows
|
| Snowflake Document Processing | 15% | - Document Processing Pipelines
|
Snowflake인증 GES-C02시험을 통과하여 자격증을 취득하여 IT 업계에서의 자신의 자리를 지키려면 많은 노력이 필요합니다. 회사일도 바쁜데 시험공부까지 스트레스가 장난아니게 싸이고 몸도 많이 상하겠죠. DumpTOP는 여러분을 위해 최신Snowflake인증 GES-C02시험에 대비한Snowflake인증 GES-C02덤프를 발췌하였습니다. Snowflake인증 GES-C02덤프는Snowflake인증 GES-C02시험의 기출문제와 예상문제가 묶어져 있어 시험적중율이 굉장히 높습니다. 쉽게 시험을 통과하려면DumpTOP의 Snowflake인증 GES-C02덤프를 추천합니다.
질문 # 67
Which commands restrict access to Snowflake Cortex functions so that access is provided only to roles with explicitly granted privileges? (Choose two)
정답：B,D
설명：
By default in Snowflake accounts, baseline privileges to access Cortex functions are broadly available because the shared database role SNOWFLAKE.CORTEX_USER is granted directly to the pseudo-role PUBLIC. Since every Snowflake user and custom role automatically inherits privileges assigned to PUBLIC, any user can invoke serverless Cortex LLM functions out of the box.
To eliminate this default public access and enforce a strict least-privilege security model, an administrator must execute:
* REVOKE DATABASE ROLE SNOWFLAKE.CORTEX_USER FROM ROLE PUBLIC; to remove direct access to the Cortex function wrappers.
* REVOKE IMPORTED PRIVILEGES ON DATABASE SNOWFLAKE FROM ROLE PUBLIC; to restrict inherited access to shared metadata, views, and system-defined database objects inside the shared SNOWFLAKE database.
Once these revocations take effect, serverless Cortex functions become inaccessible to general users until the ACCOUNTADMIN explicitly grants DATABASE ROLE SNOWFLAKE.CORTEX_USER to specific, authorized business roles. Options A and B are syntactically incorrect because CORTEX_USER is a database role, not an account-level role. Option D is invalid because Cortex is delivered via shared database roles, not via direct standard schema usage grants on SNOWFLAKE.CORTEX.
References & Study Guide Topics:
* Snowflake Gen AI Governance: Cortex Security Hardening and Least-Privilege Encampment
* Access Control Management: Database Roles vs. Account Roles in System Databases
질문 # 68
Which arguments are required when you use the log_model method to log a model in the Snowflake Model Registry? (Choose two)
정답：C,E
설명：
When managing machine learning models via the Snowflake Snowpark ML API, the `Registry.log_model()` method commits model artifacts, dependencies, and operational metadata as first-class objects into the Snowflake Model Registry.
The method signature strictly mandates two primary arguments:* **`model`:** The supported in-memory model object or model reference to be serialized (such as scikit-learn estimators, PyTorch models, Hugging Face pipelines, or custom Snowpark ML model classes).* **`model_name`:** A valid Snowflake database identifier string specifying the name under which the model will be cataloged within the active database and schema.
The remaining parameters are optional:* `version_name` is optional; if omitted, Snowflake automatically generates a unique version identifier (such as a timestamp-based string).* `code_paths` and `python_version` are optional configurations used to supply supplementary custom code dependencies and pin execution environment runtimes.References & Study Guide Topics:* Snowflake Gen AI Governance: Snowflake Model Registry API Specifications* ML Lifecycle Management: `Registry.log_model` Mandatory Parameters and Serialization===============
질문 # 69
Which commands can be used to display the Snowflake Cortex LLM models that are available? (Choose two)
정답：A,D
설명：
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`
질문 # 70
Which privileges are needed to run a model fine-tuning job? (Choose two)
정답：A,E
설명：
Executing fine-tuning jobs via SNOWFLAKE.CORTEX.FINETUNE requires specific privileges across the data source, processing compute, and target schema. Because the fine-tuning workflow queries existing tables or views containing training and validation data, the executing role must possess active container access, specifically USAGE on the parent database and schema housing those source datasets.
Furthermore, the output of a fine-tuning job is stored as a first-class model entity in the Snowflake Model Registry within a destination schema. Therefore, the executing role must hold either CREATE MODEL or OWNERSHIP on the destination schema where the fine-tuned model will be registered.
Option A is incorrect because serverless fine-tuning outputs to the Model Registry rather than requiring stage access for raw model weights. Option C is incorrect because OPERATE is an administrative warehouse privilege and does not apply to databases. Option D is incorrect because, while USAGE on the destination schema is required to navigate objects, the governing and authoritative model creation privilege is explicitly CREATE MODEL or OWNERSHIP.
References & Study Guide Topics:
* Snowflake Gen AI Governance: Serverless LLM Fine-Tuning Security Matrix
* Model Lifecycle Management: Schema Permissions for Model Registration (CREATE MODEL)
질문 # 71
A Gen AI Specialist uploaded a PDF named Timesheet_report.pdf to a Snowflake stage. The PDF includes a table with the following structure:
| Date | Hours | Activity |
| :--- | :--- | :--- |
| 2025-04-01 | 4 | Debugging issue #321 |
| 2025-04-02 | 1 | Updating documentation |
| 2025-04-03 | 2 | Design for feature #958 |
The Specialist then runs a command to extract the document's content in Markdown format, preserving the table structure:
```sql
SELECT SNOWFLAKE.CORTEX.PARSE_DOCUMENT(
'@document_stage',
'Timesheet_report.pdf',
{'mode': 'LAYOUT'}
):"content";
```
What is the output of this command?
정답：A
설명：
The `SNOWFLAKE.CORTEX.PARSE_DOCUMENT` function extracts text and structural elements from documents stored in stages, supporting modes such as `OCR` and `LAYOUT`.
When invoked with `{'mode': 'LAYOUT'}`, the extraction engine processes visual layout coordinates, headers, paragraphs, and tabular grids, formatting the resulting textual representation as structured GitHub- flavored Markdown within the returned JSON object's `"content"` field. Tables detected in the source document are translated into Markdown pipe tables featuring header delimiters, alignment syntax (`| :---: |`), and cell rows.
Option A reflects plain unstructured text extraction (typical of standard text/OCR extraction without layout preservation). Options C and D represent CSV formatting and JSON arrays, respectively, neither of which represents the native Markdown layout output generated by `PARSE_DOCUMENT`.
References & Study Guide Topics:
* Snowflake Document Processing: Layout-Aware Parsing via `PARSE_DOCUMENT`
* Markdown Output Standards: Preserving Tabular Structures for LLM Ingestion
질문 # 72
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DumpTOP는 여러분의 시간을 절약해드릴 뿐만 아니라 여러분들이 안심하고 응시하여 순조로이 패스할수 있도록 도와주는 사이트입니다. DumpTOP는 믿을 수 있는 사이트입니다. IT업계에서는 이미 많이 알려 져있습니다. 그리고 여러분에 신뢰를 드리기 위하여Snowflake GES-C02관련자료의 일부분 문제와 답 등 샘플을 무료로 다운받아 체험해볼 수 있게 제공합니다. 아주 만족할 것이라고 믿습니다. 우리는DumpTOP제품에 대하여 아주 자신이 있습니다. 우리Snowflake GES-C02도 여러분의 무용지물이 아닌 아주 중요한 자료가 되리라 믿습니다. 여러분께서는 아주 순조로이 시험을 패스하실 수 있을 것입니다. DumpTOP선택은 틀림없을 것이며 여러분의 만족할만한 제품만을 제공할것입니다.
GES-C02적중율 높은 인증덤프자료: https://www.dumptop.com/Snowflake/GES-C02-dump.html