Learn with Snowflake : GES-C01 training material for 100% pass

Snowflake GES-C01 Exam Overview:

Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data application developer is using the Snowflake Cortex COMPLETE function to power a multi-turn conversational AI application. They want to ensure responses are creative but not excessively long, adhere to a specific JSON structure, and are filtered for safety. Given the following SQL query snippet, which statements accurately describe the impact of the specified options?

A) Setting

B) The

C) The

D) For a multi-turn conversation, previous user prompts and model responses should be passed in the

E) Including a

2. A global enterprise has Snowflake accounts in various regions, including a US East (Ohio) account where a critical application is deployed. They need to use AI_COMPLETE with the claude-3-5-sonnet model for real-time customer support, but this model is not natively available in US East (Ohio) for direct AI_COMPLETE usage. The Snowflake administrator considers enabling cross-region inference. Which statements accurately reflect the considerations and characteristics of cross-region inference in Snowflake Cortex?

A) Setting the CORTEX_ENABLED_CROSS_REGION account parameter to 'ANY_REGION' in the US East (Ohio) account would enable inference requests for claude-3-5- sonnet to be processed in any region where it is natively available.
B) Cross-region inference is not supported in U.S. SnowGov regions for either inbound or outbound inference requests.
C) Cross-region inference automatically caches user inputs and generated outputs to reduce latency for subsequent requests to the same model.
D) Latency between regions for cross-region inference is negligible and consistently low, irrespective of cloud provider infrastructure.
E) The CORTEX_ENABLED_CROSS_REGION parameter can be configured at the session level to temporarily enable cross-region inference for specific workloads.

3. A Data Engineer is responsible for deploying machine learning models using Snowpark Container Services. They need to ensure that a specific role, model_deployer_role, has the appropriate permissions to create a Snowpark Container Service that uses an image from an existing image repository named my_inferenc_ images. Which of the following SQL commands grant the necessary privileges 'on the image repository' for this purpose?

A)

B)

C)

D)

E)

4. A development team is constructing a Gen AI application using Snowflake Cortex LLM functions, particularly for conversational and text generation tasks. They are concerned about potential high costs due to token consumption. Which of the following strategies would most effectively help minimize token usage and optimize costs when working with these Cortex LLM functions?

A) For multi-turn conversational experiences using SNOWFLAKE .CORTEX.COMPLETE, only send the most recent user prompt in each API call, as the model automatically retains previous context.
B) To encourage more succinct LLM responses and reduce completion_tokens, configure the temperature option to a higher value (e.g., 0.7) in COMPLETE function calls.
C) Utilize the COUNT_TOKENS (SNOWFLAKE .CORTEX) helper function to pre-validate the prompt length against the model's context window, thereby preventing truncation errors and subsequent re-runs.
D) In multi-turn conversations within Cortex Analyst, integrate a dedicated LLM summarization agent to rephrase follow-up questions, which reduces the total conversational history passed as context to the main LLM.
E) When employing AI_COMPLETE for structured output tasks, providing concise and highly descriptive explanations for each field within the JSON schema will reduce the input tokens required for the LLM to understand and adhere to the schema accurately.

5. A developer is refining a Document AI extraction process using the '!PREDICT' method and is meticulously examining the JSON output for invoices, which include 'invoice number', 'invoice items', 'tax amount', and 'vendor name'. They also have a detailed internal table of 'product details' to be extracted. To ensure optimal data quality and accurate interpretation of the extracted information, which of the following best practices or characteristics of Document AI's output should the developer consider?

A) For table extraction, such as the extracted values for each column (e.g., 'tablel litem', 'tablel Igross) are ordered consistently with the rows of the original table, facilitating direct joining of columns.
B) To maximize accuracy when defining data values, questions should be broadly generic (e.g., 'What is the amount?) to allow the Document AI model to infer the most relevant context, especially for fields like 'tax_amount' where multiple numbers might be present.
C) If the 'vendor_name' field cannot be confidently identified in a document, the model will include '"vendor_name": [ { "score": O.X, "value": "NOT FOUND" } l' in the JSON output.
D) When extracting lists of values, such as 'invoice_items', the Document AI model returns them as an array in the JSON output, preserving the original order of items as they appear in the document.
E) The 'ocrScore' provided in the '_documentMetadata' object for each document indicates the model's confidence in the content of specific extracted values, rather than the overall quality of the optical character recognition process.

Solutions: