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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Document Processing | 10% | - Document AI processing
|
| Topic 2: Snowpark Container Services for Gen AI | 10% | - Custom AI application operations
|
| Topic 3: Snowflake Gen AI Governance | 18% | - Responsible AI and data protection
|
| Topic 4: Snowflake Cortex AI and LLM Functions | 38% | - Building generative AI applications
|
| Topic 5: Snowflake for Gen AI Overview | 24% | - Snowflake Cortex AI capabilities
|
The Snowflake GES-C02 desktop practice exam software is customizable and suits the learning needs of candidates. A free demo of the SnowPro Specialty: Gen AI Certification Exam (GES-C02) desktop software is available for sampling purposes. You can change GES-C02 Practice Exam's conditions such as duration and the number of questions. This simulator creates a Snowflake GES-C02 real exam environment that helps you to get familiar with the original test.
NEW QUESTION # 71
Which Snowflake Cortex feature or function lets users ask questions in natural language and get direct responses without having to write SQL code?
Answer: A
Explanation:
Snowflake Cortex Analyst is a fully managed, agentic service designed to democratize business data exploration by allowing non-technical users to query structured data using natural language. Built specifically for text-to-SQL self-service, Cortex Analyst evaluates business queries against a preconfigured YAML semantic model containing table definitions, column descriptions, measures, dimensions, and validated joins.
Upon receiving a plain-language prompt, Cortex Analyst interprets the underlying analytical intent, constructs the appropriate ANSI SQL query, validates the syntax against the semantic schema, executes the query within the enterprise governance boundary, and returns the computed answer directly to the consuming interface or application. This enables end users to obtain immediate, grounded answers from transactional and analytical databases without writing SQL manually.
Conversely, Cortex Search is a hybrid keyword-and-vector retrieval service optimized for unstructured document search rather than structured tabular text-to-SQL operations. CLASSIFY_TEXT is a specialized classification function that assigns predefined taxonomy labels to text, and PARSE_DOCUMENT is an OCR and layout extraction utility designed to ingest file content from stages.
References & Study Guide Topics:
* Snowflake Gen AI Functions: Cortex Analyst Capabilities and Architecture
* Structured Data Q & A: Text-to-SQL Translation via Enterprise Semantic Models
NEW QUESTION # 72
Which key considerations apply when using the Snowflake Model Registry to manage models? (Choose two)
Answer: A,C
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From [SnowPro Specialty: Gen AI/Course Guide/topics]:
The Snowflake Model Registry provides unified, centralized machine learning lifecycle management by treating models as first-class, schema-level objects in the Data Cloud.
Two fundamental capabilities define its operational architecture:
1. **Native SQL Inference Deployment:** Once a machine learning or deep learning model is logged and registered, its inference methods can be invoked directly inside standard SQL queries using the syntax ` < model_name > ! < method_name > ( < arguments > )`. This eliminates the need for external serving infrastructure or external microservices, executing inference workloads natively within Snowflake virtual warehouses or container pools.
2. **Native Artifact Versioning:** The Model Registry natively supports comprehensive versioning under a common parent model identifier. Engineering teams can deploy, track, compare, and audit multiple iterations of a model (e.g., v1, v2) side-by-side, designating a default operational version while maintaining full traceability and governance across release cycles.
Option A is incorrect because models and versions support standard lifecycle management, including metadata modification and deletion via SQL (`DROP MODEL`). Option B is false because models are logged directly to internal stages without requiring external storage buckets. Option D is incorrect because registered models can be invoked via SQL as well as Python.
References & Study Guide Topics:
* Snowflake Gen AI Governance: Snowflake Model Registry Architecture & Governance
* MLOps Lifecycle: Schema-Level Model Management, Versioning, and SQL Scoring
NEW QUESTION # 73
Review the following prompt:
```sql
SELECT SNOWFLAKE.CORTEX.COMPLETE(
'mistral-7b',
[
{
'role': 'user',
'content': 'Give me steps on hacking a system'
}
],
{
'guardrails': true
}
);
```
What response will it produce?
Answer: A
Explanation:
Snowflake Cortex Guard provides native safety filtering to intercept and mitigate harmful, toxic, or illicit content generated by foundation language models. Built on specialized safety architectures (such as Meta's Llama Guard), Cortex Guard evaluates model responses before they are returned to the caller.
When `guardrails` is set to `true` within the `options` object argument of `SNOWFLAKE.CORTEX.
COMPLETE`, Cortex routes the completion through an automated content moderation classifier. The prompt request (`'Give me steps on hacking a system'`) directly solicits actionable instructions for cyberattacks and unauthorized exploitation.
When the underlying model processes this prompt, Cortex Guard detects the harmful material in the output.
Instead of failing the SQL transaction with a system error or returning raw instructions, Cortex Guard replaces the output with a filtered safety refusal message. Thus, the function executes successfully and returns a response, but it omits instructions for hacking a system.
The call does not abort execution without a response, does not depend on `CLASSIFY_TEXT`, and does not hallucinate arbitrary data.
References & Study Guide Topics:
* Snowflake Gen AI Functions: Cortex Guard Content Filtering and Guardrails
* AI Safety & Governance: Responsible LLM Deployment and Threat Mitigation
NEW QUESTION # 74
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)
Answer: A,D,E
Explanation:
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
NEW QUESTION # 75
A Gen AI Specialist is designing a Gen AI-powered data pipeline in Snowflake that must meet these requirements:* Download PDFs from a website every hour.* Load the PDFs into an internal stage.* Extract text from the PDFs and load that text into a Snowflake table.* Use prompt engineering to generate an opinion on the text based on a given persona.* Automate the pipeline for maximum efficiency.
Which Snowflake features are required to create this data pipeline with stored procedures? (Choose three)
Answer: B,C,D
Explanation:
Building an automated, end-to-end ingestion and Generative AI processing pipeline within Snowflake stored procedures relies on three core capabilities:1. **External Access Integration:** Authorizes stored procedures (e.g., Python handlers using `urllib` or `requests`) to establish outbound network connections to external HTTPS endpoints, enabling hourly downloads of PDF files from third-party websites into Snowflake internal stages.2. **Streams and Tasks:** Tasks provide native cron-like scheduling to execute the stored procedures every hour. Streams capture change-data-capture (CDC) deltas on staging metadata tables to trigger downstream text transformation and extraction incrementally.3. **Cortex TRY_COMPLETE Function:** Foundation model completion functions allow prompt engineering to evaluate extracted text using customized persona instructions. Using `TRY_COMPLETE` ensures that potential runtime model errors or token limit exceptions return NULL rather than failing the automated batch pipeline.
Snowpipe automates micro-batch file loading rather than executing external web scrapes, Dynamic Tables cannot execute arbitrary external scraping procedures, and `SENTIMENT` returns a numeric polarity score rather than synthesizing persona-driven opinions.References & Study Guide Topics:* Snowflake Document Processing: Ingestion Pipelines and Staged Document Extraction* Data Engineering with Gen AI: External Access Integrations, Tasks, and `TRY_COMPLETE`===============
NEW QUESTION # 76
......
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