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| Section | Weight | Objectives |
|---|---|---|
| Snowflake Cortex AI and LLM Functions | 38% | - Cortex AI functions
|
| Snowflake for Gen AI Overview | 24% | - Generative AI solution architecture
|
| Document Processing | 10% | - Document processing pipelines
|
| Snowpark Container Services for Gen AI | 10% | - Custom AI application operations
|
| Snowflake Gen AI Governance | 18% | - Monitoring and cost governance
|
現代の競争が激しくても、受験者がGES-C02参考書に対するニーズを止めることができません。GES-C02参考書についてもっと具体的な情報を得るために、CertJuken会社のウエブサイトを訪問していただきます。そうすれば、実際のGES-C02試験についての情報と特徴を得ることができます。興味を持つお客様はSnowflake会社のウエブサイトから無料でデモをダウンロードできます。
質問 # 52
A Gen AI Specialist is creating metadata for Snowflake Cortex Analyst by creating a semantic model. The relationship between the PRODUCTS and PRODUCT_SALES tables is defined as:
```yaml
relationships:
- name: sales_to_products
join_type: left_outer
relationship_type: many_to_one
left_table: PRODUCT_SALES
relationship_columns:
- left_column: PRODUCT_ID
right_column: ID
right_table: PRODUCTS
```
Which join will Cortex Analyst generate in a query that uses this model?
```sql
正解：A
解説：
Snowflake Cortex Analyst relies on the `relationships` section of a semantic model YAML specification to determine how logical tables must be joined when generating SQL.
The relationship definition contains key parameters that control join construction:
* `left_table: PRODUCT_SALES` designates the base table to be preserved in an outer join.
* `right_table: PRODUCTS` designates the lookup table joined to the preserved dataset.
* `join_type: left_outer` instructs Cortex Analyst to emit ANSI `LEFT OUTER JOIN` syntax preserving all records from the left table.
* `relationship_columns` maps `left_column: PRODUCT_ID` to `right_column: ID`, defining the join equality condition: `ON S.PRODUCT_ID = P.ID`.
Based on these metadata declarations, whenever a natural language query requires attributes spanning both tables, Cortex Analyst constructs the query starting from `PRODUCT_SALES AS S`, applies a `LEFT OUTER JOIN` to `PRODUCTS AS P`, and predicates the join condition using `ON S.PRODUCT_ID = P.ID`.
Options B, C, and D are incorrect because they invert table positions, employ `FULL OUTER JOIN` syntax, or place join conditions into a `WHERE` clause.
References & Study Guide Topics:
* Snowflake Gen AI Functions: Cortex Analyst Semantic Modeling Specifications
* Semantic Relationships: Defining Joins, Dimensional Cardinality, and Foreign Keys
質問 # 53
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?
正解：B
解説：
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
質問 # 54
Which Snowflake feature enables application integration to answer questions using structured data in Snowflake?
正解：B
解説：
Snowflake Cortex Analyst is the purpose-built service enabling external software applications, portals, and conversational interfaces to execute text-to-SQL workflows against structured relational databases. Designed for programmatic integration, Cortex Analyst exposes a secure REST API that enterprise applications can query directly.
The service utilizes developer-defined semantic models (expressed in YAML syntax) that map enterprise business logic, dimensional hierarchies, verified queries, synonym mappings, and table relationships. When an integrated application submits a natural language question through the REST API, Cortex Analyst uses the semantic model to translate the query into semantically verified SQL, executes the query against Snowflake tables, and delivers deterministic structured data back to the application.
In contrast, Document AI is engineered to extract key-value pairs and tabular data from unstructured files (PDFs, images); Snowflake Copilot is an integrated assistant within the Snowsight interface focused on assisting developers with SQL and Python authoring; and Cortex Search provides hybrid lexical and semantic retrieval over unstructured text data for RAG applications.
References & Study Guide Topics:
* Snowflake Gen AI Functions: Cortex Analyst REST API Integration
* Enterprise Architectures: Natural Language Querying over Relational Data
質問 # 55
A user needs to enable cross-region inference in a Snowflake account that is located in the AWS us-east-1 (N.
Virginia) region, so that they can access LLM models available in other AWS regions.
What considerations should be made before enabling cross-region inference in the Snowflake account?
(Choose three)
正解：A、B、C
解説：
Cross-region inference enables Snowflake Cortex users to leverage advanced foundation models that may not yet be locally hosted in their account's primary deployment region.
When configuring and utilizing cross-region inference, several architectural considerations apply:
* Privilege Requirements: Modifying account-level inference routing parameters (such as CORTEX_ENABLED_CROSS_REGION) requires the ACCOUNTADMIN role via the ALTER ACCOUNT command; ORGADMIN is not used for account-level parameter management.
* Consumption Rates: Billed credits for model inference are consistently metered according to the service consumption pricing schedule of the requesting account's primary region (in this scenario, AWS us-east-
1), regardless of the remote region servicing the inference.
* Data Transit: Data transmitted for remote processing does not traverse the public internet; it remains encrypted within the cloud provider's private global backbone network (e.g., AWS global infrastructure) during transit.
Option D is incorrect because billing is pegged to the consumer account's home region, not the processing node's region. Option F is incorrect because cross-region processing by definition moves transient payload data outside the origin region.
References & Study Guide Topics:
* Snowflake Gen AI Governance: Cross-Region Inference Configuration & Security
* Cloud Cost Management: Service Consumption Pricing & Network Transit Boundaries
質問 # 56
A Gen AI Specialist is developing a Gen AI application in Snowflake and must debug a particular record that produced an inaccurate response. The Specialist needs to see, step by step, how the input prompt was processed, what context was retrieved, which tools were called, and how the LLM responded.
Which Snowflake AI observability feature should be used?
正解：A
解説：
Snowflake AI Observability incorporates native telemetry tools-built on OpenTelemetry standards-to provide full runtime visibility across complex Generative AI and agentic pipelines.
When diagnosing compound Gen AI workflows (such as multi-step agent execution, tool-calling sequences, or Retrieval-Augmented Generation retrieval flows), Tracing is the required capability. Tracing records the end-to-end execution path of an individual request as a structured collection of spans within an Event Table.
Each trace span captures granular, step-by-step metadata, including:
* The raw input prompt and intermediate prompt transformations.
* The specific documents, chunks, and similarity scores returned by retrieval engines (e.g., Cortex Search).
* External functions or tools invoked by reasoning agents, including input arguments and returned payloads.
* The raw completion, token counts, and latency produced by the underlying LLM.
In contrast, Logging captures discrete, point-in-time diagnostic text messages emitted by application code; Metrics aggregate numeric operational counters over time (such as token rates or invocation throughput); and Monitoring provides dashboard-level health alerting rather than step-by-step contextual call graphs.
References & Study Guide Topics:
* Snowflake Gen AI Governance: Cortex AI Observability & OpenTelemetry Telemetry
* Operational Debugging: Distributed Tracing and Span Analysis in Event Tables
質問 # 57
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