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| Section | Weight | Objectives |
|---|---|---|
| Snowflake Gen AI and LLM Functions | 38% | - Cortex AI Functions
|
| Snowflake Document Processing | 15% | - Document Processing Pipelines
|
| Snowflake for Gen AI Overview | 18% | - Generative AI Fundamentals and Snowflake Cortex
|
| Snowflake Gen AI Governance | 29% | - AI Quality and Observability
|
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NEW QUESTION # 24
Which commands restrict access to Snowflake Cortex functions so that access is provided only to roles with explicitly granted privileges? (Choose two)
Answer: A,B
Explanation:
By default in Snowflake accounts, baseline privileges to access Cortex functions are broadly available because the shared database role SNOWFLAKE.CORTEX_USER is granted directly to the pseudo-role PUBLIC. Since every Snowflake user and custom role automatically inherits privileges assigned to PUBLIC, any user can invoke serverless Cortex LLM functions out of the box.
To eliminate this default public access and enforce a strict least-privilege security model, an administrator must execute:
* REVOKE DATABASE ROLE SNOWFLAKE.CORTEX_USER FROM ROLE PUBLIC; to remove direct access to the Cortex function wrappers.
* REVOKE IMPORTED PRIVILEGES ON DATABASE SNOWFLAKE FROM ROLE PUBLIC; to restrict inherited access to shared metadata, views, and system-defined database objects inside the shared SNOWFLAKE database.
Once these revocations take effect, serverless Cortex functions become inaccessible to general users until the ACCOUNTADMIN explicitly grants DATABASE ROLE SNOWFLAKE.CORTEX_USER to specific, authorized business roles. Options A and B are syntactically incorrect because CORTEX_USER is a database role, not an account-level role. Option D is invalid because Cortex is delivered via shared database roles, not via direct standard schema usage grants on SNOWFLAKE.CORTEX.
References & Study Guide Topics:
* Snowflake Gen AI Governance: Cortex Security Hardening and Least-Privilege Encampment
* Access Control Management: Database Roles vs. Account Roles in System Databases
NEW QUESTION # 25
When working with Snowflake Cortex, which design factors have the MOST effect on model performance per credit? (Choose two)
Answer: A,D
Explanation:
Achieving optimal cost-to-performance efficiency (performance per credit) in Snowflake Cortex depends on right-sizing the chosen foundation model to the technical demands of the specific workload. The two design factors exerting the greatest influence on this trade-off are:
* The Size of the Data Set (Token Volume): Cortex serverless LLM functions meter consumption based on the cumulative volume of input and output tokens. Processing extensive volumes of data through massive foundation models rapidly consumes credits; pairing large datasets with compact models (e.g., mistral-7b or llama3-8b) significantly optimizes throughput per credit.
* The Complexity of the Queries (Task Difficulty): Simple operational tasks-such as entity extraction, sentiment scoring, and basic categorization-do not require frontier models and can be handled cost- effectively by smaller parameter models. Conversely, complex multi-step reasoning, advanced mathematical derivation, or deep semantic synthesis justify the higher credit consumption of large foundation models (such as llama3.1-70b or mistral-large2).
Compute capacity is managed automatically by Snowflake's serverless fabric rather than provisioned directly, and regional placement or user concurrency do not determine the underlying architectural efficiency of model selection per unit of credit consumed.
References & Study Guide Topics:
* Snowflake for Gen AI Overview: Cost Governance and Architectural Sizing
* Model Sizing Economics: Balancing Model Parameter Tiers against Workload Complexity
NEW QUESTION # 26
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 # 27
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: C,D
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 # 28
This use case retrieves auto-part details from two data systems:
Datasource A:
Table Name - Parts_A
Columns Name - Parts_description, part_id
Datasource B:
Table Name - Parts_B
Columns Name - Parts_description, part_id
Code snippet
Which query compares the part descriptions across the two data sources?
```sql
Answer: B
Explanation:
Snowflake vector math functions-such as `VECTOR_COSINE_SIMILARITY`,
`VECTOR_L1_DISTANCE`, and `VECTOR_L2_DISTANCE`-require input arguments of data type
`VECTOR` (specifically matching dimensions of `VECTOR(FLOAT, dim)`). They cannot operate directly on raw strings or `VARCHAR` columns.
In this scenario, `Parts_description` in both `Parts_A` and `Parts_B` is a standard string column. Attempting to pass raw text columns directly into vector similarity functions (as seen in Options A, B, and C) fails during compilation with an invalid argument data type error.
Option D correctly implements the required two-phase pipeline:
* It utilizes inline subqueries to transform the text descriptions into 768-dimensional float vectors using
`SNOWFLAKE.CORTEX.EMBED_TEXT_768('e5-base-v2', Parts_description)`.
* It then passes the generated vector representations into `VECTOR_COSINE_SIMILARITY(a.
parts_description_vector, b.parts_description_vector)` while joining matching records on `part_id`, accurately computing the semantic similarity score.
References & Study Guide Topics:
* Snowflake Gen AI Functions: Text Embedding Pipelines and Vector Type Requirements
* Function Syntax: Comparing Embeddings with `VECTOR_COSINE_SIMILARITY` and
`EMBED_TEXT_768`
NEW QUESTION # 29
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