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| Section | Weight | Objectives |
|---|---|---|
| Document Processing | 10% | - Document AI processing
|
| Snowpark Container Services for Gen AI | 10% | - Custom AI application operations
|
| Snowflake Gen AI Governance | 18% | - Monitoring and cost governance
|
| Snowflake Cortex AI and LLM Functions | 38% | - Cortex AI functions
|
| Snowflake for Gen AI Overview | 24% | - Generative AI and large language model fundamentals
|
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NEW QUESTION # 65
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?
Answer: D
NEW QUESTION # 66
A Gen AI Specialist is developing an automated content generator in Snowflake that produces customized product descriptions. The descriptions will be based on columns in a products table, including brand, category, and features.
Which Snowflake Cortex LLM function should be used to generate the descriptions?
Answer: B
Explanation:
Generating dynamic, creative marketing content-such as customized product descriptions tailored from tabular database attributes-requires a general-purpose, prompt-driven large language model.
SNOWFLAKE.CORTEX.COMPLETE accepts a user-defined prompt alongside dynamic column parameters, allowing developers to engineer prompts that combine tabular fields (e.g., 'Write a compelling product description for Brand: ' || brand || ', Category: ' || category || ', Features: ' || features). The foundation model processes these inputs to synthesize fluent, context-aware descriptions.
The alternative functions are not designed for open-ended text synthesis:
* PARSE_DOCUMENT extracts raw text from document files in Snowflake stages.
* EMBED_TEXT_768 converts input strings into high-dimensional numerical vector embeddings for similarity matching.
* SUMMARIZE condenses existing text into a shorter synopsis rather than generating expanded, creative copy from attribute lists.
References & Study Guide Topics:
* Snowflake Gen AI Functions: Text Generation using SNOWFLAKE.CORTEX.COMPLETE
* Prompt Engineering in SQL: Dynamic Prompt Composition over Tabular Data
NEW QUESTION # 67
Which commands can be used to display the Snowflake Cortex LLM models that are available? (Choose two)
Answer: A,D
Explanation:
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`
NEW QUESTION # 68
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: B
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 # 69
A Gen AI Specialist needs to determine whether feedback from a recent survey was generally positive, negative, or neutral. The Specialist wants the assessment reported as either a floating-point number from -1 to
+1 (inclusive) or an object with a categories field.
Which statements will produce the desired results? (Choose two)
Answer: B,E
Explanation:
Snowflake Cortex provides dedicated natural language sentiment analysis functions designed to evaluate textual polarity without requiring custom prompt engineering:
1. **`SNOWFLAKE.CORTEX.SENTIMENT`:** Returns a single floating-point number bounded between
-1.0 and 1.0 (inclusive). A score approaching 1.0 indicates positive sentiment, near -1.0 represents negative sentiment, and values close to 0 denote neutral feedback.
2. **`SNOWFLAKE.CORTEX.ENTITY_SENTIMENT`:** Performs aspect-level sentiment extraction. It accepts the source text alongside an array of target entities (e.g., `['positive', 'negative', 'neutral']`) and returns a semi-structured `OBJECT` containing a `"categories"` field that details sentiment scores and extracted references for each evaluated entity.
Conversely, `CLASSIFY_TEXT` requires predefined taxonomy classes and returns a single label object rather than a polarity score or categories object; `COMPLETE` generates free-form text; and `FINETUNE` executes supervised model training.
References & Study Guide Topics:
* Snowflake Gen AI Functions: Sentiment and Entity-Level Analysis
* Output Data Structures: Float Scoring vs. Semi-Structured Category Objects
NEW QUESTION # 70
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