Snowflake SnowPro® Specialty: Gen AI Certification - GES-C01 Exam Practice Test

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A. Tracing primarily focuses on aggregating performance metrics like accuracy and latency across multiple application runs for comparison, rather than step-by-step execution details.

B. It allows debugging individual records by showing every step of application executions, including input prompts, retrieved context, and tool use.

C. Tracing requires manual logging of each application step through custom SQL INSERT statements into an event table.

D. It enables refinement of the application for improved accuracy, latency, and cost.

E. Tracing records only the final output of an LLM inference call, not the intermediate steps.

A. The Snowflake account will be temporarily locked for violating the acceptable use policy, and all subsequent LLM calls will fail until manual intervention.

B. The LLM, through Cortex Guard's internal mechanisms, will automatically rephrase or redact the harmful parts of its response to comply with safety guidelines before returning it.

C. The 'COMPLETE function will return a NULL value in the response, signifying that no safe response could be generated, similar to error handling.

D. The 'COMPLETE function will return a generic, pre-defined filtered message, such as 'Response filtered by Cortex Guard', in place of the harmful content.

E. The 'COMPLETE function will raise a SQL exception, indicating a 'PROHIBITED_CONTENT error, which the calling application must handle with a 'TRY...CATCH' block.

A. The fine-tuning process requires the explicit provisioning and management of a Snowpark-optimized warehouse with GPU resources by the institution.

B. Fine-tuned LLMs built with Cortex Fine-tuning are fully managed through the Snowflake Model Registry API, allowing for programmatic deployment, version control, and comprehensive lifecycle management.

C. The fine-tuned model, being of type CORTEX_FINETUNED, can be shared with other Snowflake accounts using secure data sharing capabilities.

D. The financial reports used for fine-tuning the llama3.1-7b model are securely isolated and are not used by Snowflake to train or re-train models for other customers.

E. The resulting fine-tuned model (e.g., my_sentiment_model) is the exclusive property of the financial institution and cannot be accessed or used by any other Snowflake customer.

A. To minimize costs for ' EMBED_TEXT 768 operations, it is recommended to execute queries using a smaller virtual warehouse (no larger than MEDIUM), as larger warehouses do not improve performance for these functions.

B. The 'EMBED TEXT 768' function, regardless of the 768-dimension model used, has a fixed cost of 1.50 Credits per one million Tokens processed.

C. The 'EMBED_TEXT 768' function is billed based on the number of 'output tokens' generated by the embedding model, as this represents the computational complexity of the vector.

D. The 'snowflake-arctic-embed-m-vl .5 model, used by 'EMBED TEXT 768', has a context window of 512 tokens, and texts exceeding this length are truncated before embedding.

E. For optimal retrieval quality in RAG scenarios, text should be split into chunks of no more than 512 tokens before being passed to 'EMBED TEXT 768', even if the model supports a larger context window.

A. The 'response_format' correctly defines the expected JSON structure, using 'enum' for 'severity_lever and 'required' to ensure 'event_name' and severity_lever are always present if extracted.

B. Using 'TRY COMPLETE instead of would allow the pipeline to gracefully handle cases where the model fails to generate a valid JSON response by returning 'NULL' instead of an error.

C. For all models supported by 'AI_COMPLETE' Structured Outputs, the 'additionalPropertieS field must be set to 'false' in every node of the schema, and the 'required' field must contain all property names.

D. The complexity of the JSON schema, particularly deep nesting, does not impact the number of tokens processed and billed for 'AI_COMPLETE Structured Outputs.

E. Setting 'temperature 'to '0.7 ' is optimal for ensuring the most consistent and deterministic JSON outputs, especially for complex extraction tasks.

A. CHANGE_TRACKING

B. The primary cost driver for Cortex Search is the number of search queries executed against the service, with the volume of indexed data (GB/month) having a minimal impact on overall billing.

C. For optimal performance and cost efficiency, Snowflake recommends using a dedicated warehouse of size no larger than MEDIUM for each Cortex Search Service.

D. For embedding text, selecting a model like

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