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| Section | Weight | Objectives |
|---|---|---|
| Snowflake Gen AI Governance | 18% | - Responsible AI and data protection
|
| Snowflake for Gen AI Overview | 24% | - Generative AI and large language model fundamentals
|
| Snowpark Container Services for Gen AI | 10% | - Custom model deployment
|
| Document Processing | 10% | - Document processing pipelines
|
| Snowflake Cortex AI and LLM Functions | 38% | - Cortex AI functions
|
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NEW QUESTION # 78
To submit a request to the Snowflake Cortex Analyst REST API endpoint, which fields are required in the body of the API request? (Choose three)
Answer: C,D,E
Explanation:
Integrating applications with the Snowflake Cortex Analyst REST API endpoint requires submitting a JSON payload that supplies necessary semantic and conversational context.
The API request body requires three mandatory fields:
1. **Path to the semantic model YAML file (`semantic_model_file`):** Specifies the internal or external stage location where the YAML semantic model definition is stored (e.g., `@my_db.my_schema.my_stage
/model.yaml`), which grounds text-to-SQL translation.
2. **Role of the speaker (`role`):** Structured inside each object within the `messages` array, indicating whether the utterance originates from the `'user'` or the `'analyst'`.
3. **Content (`content`):** Located inside each message entry, containing the natural language question, query text, or analyst response.
The remaining fields are not mandatory request parameters:
* `stream` is an optional boolean used to toggle Server-Sent Events (SSE) streaming.
* `request_id` and `suggestions` are output metadata fields returned in the API response payload, rather than mandatory input fields.
References & Study Guide Topics:
* Snowflake Gen AI Functions: Cortex Analyst REST API Request Specifications
* Semantic Modeling: Ingestion and Parameter Configuration for Cortex Analyst
NEW QUESTION # 79
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 # 80
Review this query:
```sql
SELECT SNOWFLAKE.CORTEX.CLASSIFY_TEXT(
'I love morning walks',
[
{
'label': 'travel',
'description': 'Hobbies related to going from one place to another',
'examples': ['I like flying to Europe', 'I love beaches']
},
{
'label': 'cooking',
'examples': ['I like learning about new ingredients', 'You must bring your soul to the recipe', 'Baking is my therapy']
},
{
'label': 'fitness',
'description': 'Hobbies related to being active and healthy'
}
],
{ 'task_description': 'Return a classification of the Hobby identified in the text' } );
```
What will be the result of this query?
Answer: A
Explanation:
The `SNOWFLAKE.CORTEX.CLASSIFY_TEXT` function supports advanced classification using structured category objects that include descriptions and few-shot examples.
In this query, the input text is `'I love morning walks'`. The classification engine evaluates this string against the candidate categories:
* `'travel'`: Defined as hobbies related to going from one place to another.
* `'cooking'`: Illustrated with recipes and baking examples.
* `'fitness'`: Defined as hobbies related to being active and healthy.
Walking for personal enjoyment directly matches the active, healthy lifestyle definition of `'fitness'`.
Regarding the return format: `CLASSIFY_TEXT` returns a semi-structured JSON `OBJECT` containing only the winning category label formatted as `{"label": " < category_name > "}`. It does not return a plain scalar string (eliminating Option C), nor does it echo back the category definition metadata (eliminating Option D).
Therefore, the result is `{"label": "fitness"}`.
References & Study Guide Topics:
* Snowflake Gen AI Functions: Advanced `CLASSIFY_TEXT` Syntax
* Semantic Categorization: JSON Schema Outputs and Structural Label Matching
NEW QUESTION # 81
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,D,E
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 # 82
Which authentication methods are supported for accessing the Snowflake Cortex LLM REST API? (Choose two)
Answer: A,C
Explanation:
The Snowflake Cortex LLM REST API enables external applications to execute text completions and embeddings programmatically outside of the native SQL worksheet or Snowpark environments. Because REST endpoints service headless, programmatic HTTP calls, interactive authentication mechanisms cannot be utilized.
Snowflake officially supports two programmatic authentication protocols for its Cortex REST API endpoints:
* Key-Pair Authentication (JWT): Clients sign a JSON Web Token (JWT) using a locally managed private key corresponding to an RSA public key assigned to the Snowflake service user.
* OAuth 2.0: Utilizing Snowflake-managed or external OAuth authorization servers to supply scoped bearer access tokens.
Interactive authentication workflows, such as SAML 2.0, browser-based Single Sign-On (SSO), and interactive Multi-Factor Authentication (MFA), are designed for human user sessions (e.g., logging into Snowsight) and are not supported for authenticating direct HTTP requests to the Cortex REST API.
References & Study Guide Topics:
* Snowflake Gen AI Governance: Cortex REST API Security Architecture
* Programmatic Connectivity: Key-Pair JWT vs. OAuth 2.0 Client Authentication
NEW QUESTION # 83
......
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