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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A new ML Engineer, 'data_scientist_role' , has been assigned to a project involving custom machine learning models in Snowflake. They need to gain the necessary permissions to perform the following actions related to Snowflake Model Registry and Snowpark Container Services: 1. Log a custom model into a specified schem a. 2. Deploy that model to an existing Snowpark Container Service compute pool. 3. Call the deployed model for inference using SQL. Which of the following SQL commands grant the 'minimal' required privileges to the for these actions, assuming the compute pool and image repository already exist and are appropriately configured?

A)

B)

C)

D)

E)

2. A multi-national corporation uses Snowflake across several AWS regions. Their primary operational Snowflake account is in AWS US East (Ohio), but they need to leverage a specific AI_COMPLETE model, llama4-maverick, which is natively available in AWS US East 1 (N. Virginia) but not in US East (Ohio). To address this, the Snowflake administrator enables cross-region inference for their US East (Ohio) account.

A) The query latency for cross-region inference with AI_COMPLETE is consistently low and predictable, as Snowflake's architecture is designed to completely negate the impact of geographical distance and network variations.
B) The llama4-maverick model is listed as natively available in AWS US East 1 (N. Virginia) and is supported for cross-region inference (AWS US Cross-Region), validating it as a suitable target for inference from US East (Ohio).
C) Cross-region inference is fully supported for AI_COMPLETE in U.S. SnowGov regions for both inbound and outbound inference requests, provided the target model is available in the respective SnowGov region.
D) To enable cross-region inference for the US East (Ohio) account, the administrator would execute the command: ALTER ACCOUNT SET AWS_US' ; to allow inference requests to be processed in any AWS US region where the model is available. CORTEX_ENABLED_CROSS REGION =AWS_US' ;
E) User inputs, service-generated prompts, and the generated outputs from cross-region AI_COMPLETE calls are automatically stored or cached in the remote processing region to optimize performance for subsequent identical requests.

3.

A) D If 'audit_status' is not found, the 'audit_status' key will be entirely absent from the JSON output for that document.
B) The '_documentMetadata.ocrScore' field will always be present in the root of the JSON output, indicating the confidence in the optical character recognition process for the entire document.
C) For successfully extracted entities like 'net_profit' , the JSON output typically includes an array of objects, each containing both 'score' and 'value' keys.
D) If a 'quantity' cell in is empty, the JSON output for that specific cell will include a 'score' key but omit the 'value' key.
E) The 'audit_statuS field, if not found, will appear in the JSON output as Taudit_status": [{"score": 0.0, "value": null}]}'.

4. A data engineering team needs to implement a highly accurate, low-latency solution for classifying specialized technical documents into 50 distinct categories. They are considering fine-tuning a Large Language Model (LLM) within Snowflake Cortex for this task. Which of the following considerations are critical for optimizing the fine-tuned model's performance and minimizing inference latency for production use? (Select all that apply)

A) Option E
B) Option B
C) Option C
D) Option A
E) Option D

5. A data engineer is building a Snowflake data pipeline to ingest customer reviews from a raw staging table into a processed table. For each review, they need to determine the overall sentiment (positive, neutral, negative) and store this as a distinct column. The pipeline is implemented using SQL with streams and tasks to process new dat a. Which Snowflake Cortex LLM function, when integrated into the SQL task, is best suited for this sentiment classification and ensures a structured, single-label output for each review?

A) Option E
B) Option B
C) Option C
D) Option A
E) Option D

Solutions: