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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowflake for Gen AI Overview | 24% | - Generative AI and large language model fundamentals
|
| Topic 2: Snowpark Container Services for Gen AI | 10% | - Custom AI application operations
|
| Topic 3: Document Processing | 10% | - Document ingestion and preparation
|
| Topic 4: Snowflake Gen AI Governance | 18% | - Access control and security
|
| Topic 5: Snowflake Cortex AI and LLM Functions | 38% | - Building generative AI applications
|
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NEW QUESTION # 76
A company is organizing an event and asked participants about their food restrictions. The responses have been saved in a table named EVENT_REGISTRATIONS:
| ID | FOOD_RESTRICTIONS |
| :--- | :--- |
| 1 | allergic to nuts |
| 2 | no gluten |
| 3 | no milk or dairy products |
| 4 | no meat |
Which query will compile a list of foods that should not be served?
```sql
Answer: C
Explanation:
Inferring specific culinary items to exclude based on high-level dietary constraints (such as deriving wheat, barley, and rye from "no gluten") requires domain reasoning and generative text synthesis.
`SNOWFLAKE.CORTEX.COMPLETE` accepts dynamic string concatenations that combine instructional prompts with tabular column values. In Option A, the query instructs an advanced foundation model (`llama3.
3-70b`) to evaluate the customer-provided restrictions and synthesize an explicit list of prohibited foods.
The alternative functions cannot perform this task:
* `SENTIMENT` returns a floating-point polarity score between -1 and 1.
* `CLASSIFY_TEXT` assigns the string to one of the provided static labels (`'foods'` or `'diets'`) rather than generating a list.
* `SUMMARIZE` condenses existing source text; it cannot infer new domain knowledge that is not already explicitly written in the source column.
References & Study Guide Topics:
* Snowflake Gen AI Functions: Generative Reasoning with `COMPLETE`
* Prompt Engineering: Dynamic SQL Concatenation for In-Database Inference
NEW QUESTION # 77
Which view in the ACCOUNT_USAGE schema can query token costs for Snowflake Cortex LLMs aggregated in one-hour increments?
Answer: D
Explanation:
Snowflake provides account-level consumption views in the shared `SNOWFLAKE.ACCOUNT_USAGE` schema to help organizations monitor, audit, and forecast cloud credit usage across Generative AI workloads.
The `CORTEX_FUNCTIONS_USAGE_HISTORY` view records serverless credit and token consumption for Cortex LLM functions (such as `COMPLETE`, `SUMMARIZE`, `TRANSLATE`, and
`EMBED_TEXT_768`) aggregated in **one-hour windows**. Each record in this view tracks the service type, model name, credits consumed, and input/output token counts processed during that hourly window.
(Snowflake also maintains `CORTEX_AI_FUNCTIONS_USAGE_HISTORY` as its modern counterpart).
The other views serve different operational scopes:
`CORTEX_DOCUMENT_PROCESSING_USAGE_HISTORY` tracks Document AI processing volumes;
`CORTEX_SEARCH_SERVING_USAGE_HISTORY` meters serving index charges for search services; and
`CORTEX_FUNCTIONS_QUERY_USAGE_HISTORY` does not exist as an hourly aggregated cost view.
References & Study Guide Topics:
* Snowflake Gen AI Governance: Account Usage and LLM Metering Views
* Financial Governance: Hourly Consumption Tracking via `CORTEX_FUNCTIONS_USAGE_HISTORY`
NEW QUESTION # 78
A Gen AI Specialist used the ACCOUNTADMIN role to invoke the ` < model_build_name > !PREDICT` method on a Document AI model build and received this error:
`{ "processingErrors": [ "File extension does not match actual mime type. Mime-Type: application/octet- stream" ] }` What is the MOST LIKELY cause?
Answer: D
Explanation:
Snowflake Document AI enforces input validation rules to ensure files can be processed by the underlying Arctic-TILT model. Supported document formats are primarily restricted to PDF, DOCX, DOC, and supported image formats (such as PNG, JPEG, TIFF).
When `!PREDICT` returns the processing error `File extension does not match actual mime type. Mime- Type: application/octet-stream`, the input document failed format validation checks. This occurs when a file's extension (e.g., `.pdf`) conflicts with its true binary MIME header, or when an unsupported, malformed, or generic binary payload (`application/octet-stream`) is uploaded. Because Document AI cannot parse non- compliant or corrupted binaries into visual and textual tokens, it flags the document as failing input requirements.
Expired URLs return HTTP expiration or stage access errors; missing stage encryption attributes generate encryption mismatch errors; and privilege deficiencies throw authorization errors rather than runtime MIME validation errors.
References & Study Guide Topics:
* Snowflake Document Processing: Document AI File Ingestion Requirements
* Troubleshooting Extraction: MIME Type Validation and Binary Format Errors
NEW QUESTION # 79
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: B,C,D
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 # 80
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 # 81
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