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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A machine learning engineering team is evaluating two different configurations of a Retrieval Augmented Generation (RAG) application. uses for generation, while uses 'mistral-7b' with a refined prompt for the same task. They aim to compare the and 'groundedness' of the generated responses, as well as the efficiency of context retrieval. Which of the following steps are crucial for setting up AI Observability in Snowflake to facilitate a meaningful side-by-side comparison and assess these specific metrics?

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

3. A data engineering team is designing a pipeline in Snowflake to translate a continuous stream of multi-language customer support tickets into English using 'SNOWFLAKE.CORTEX.TRANSLATE. They are concerned about potential language identification issues and the overall cost implications. Which of the following statements are true regarding the use of 'SNOWFLAKE.CORTEX.TRANSLATE for this scenario? (Select all that apply)

A) For cost efficiency, Snowflake recommends using a larger warehouse (e.g., XL or 2XL) for executing queries that call 'TRANSLATE functions, as this significantly reduces the per-token processing cost.
B) Snowflake Cortex functions, including 'TRANSLATE, add an internal prompt to the user's input text, which increases the total input token count for billing purposes beyond the raw text length.
C) The fixed billing rate for the 'TRANSLATE function is 1.50 Credits per one million Tokens processed.
D) If the source language of a ticket is unknown or contains mixed languages (e.g., 'Spanglish'), the function can still process it by specifying an empty string ') for the source _ language argument.
E) The 'TRANSLATE' function is exclusively billed based on the number of input tokens, as it primarily analyzes existing text rather than generating new content.

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) 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.
B) 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.
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) 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.
E) 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.

5. A data engineer is tasked with defining a semantic model for Cortex Analyst to enable natural language queries over sales dat a. They are creating a YAML file to describe the logical structure. Which of the following statements correctly describe the configuration of this semantic model? (Select all that apply)

A) To ensure high accuracy for specific common questions, 'verified_querieS can be included, where the 'sqr field must reference the underlying physical table and column names (ee.g., not the logical names defined in the semantic model.
B) A 'metric' like 'total_revenue' can be defined using an 'expr' that references other logical columns, such as 'sum(profity from a logical 'sales_data' table, within the semantic model.
C) The 'VARIANT data type is a supported data type for columns defined as 'dimensions' or 'facts' within the semantic model, allowing flexible storage of complex attributes.
D) Dimensions such as 'product_category' can include a configuration, which can specify an optional 'literal_column' that, if omitted, defaults to the search index column.
E) The 'tables' section must define logical tables that directly map to Snowflake physical tables or views, with 'base_table' specifying the fully qualified name (database, schema, table).

Solutions: