Snowflake
GES-C01
SnowPro® Specialty: Gen AI Certification
Exam Latest Version: 6.1 Practice Exam
Newest 2026.
Question: 1
A data application developer is tasked with building a multi-turn conversational AI application using
Streamlit in Snowflake (SiS) that leverages the COMPLETE (SNOWFLAKE. CORTEX) LLM function. To
ensure the conversation flows naturally and the LLM maintains context from previous interactions,
which of the following is the most appropriate method for handling and passing the conversation
history?
,A. Option A
B. Option B
C. Option C
D. Option D
E. Option E
Answer: C
Question: 2
A Streamlit application developer wants to use AI_COMPLETE (the latest version of COMPLETE
(SNOWFLAKE. CORTEX)) to process customer feedback. The goal is to extract structured information,
such as the customer's sentiment, product mentioned, and any specific issues, into a predictable JSON
format for immediate database ingestion. Which configuration of the AI_COMPLETE function call is
essential for achieving this structured output requirement?
A. Option A
B. Option B
C. Option C
D. Option D
E. Option E
Answer: C
Explanation:
'AI_COMPLETE Structured Outputs' (and its predecessor ‘COMPLETE Structured Outputs’) specifically
allows supplying a JSON schema as the 'response_format’ argument to ensure completion responses
follow a predefined structure. This significantly reduces the need for post-processing in AI data pipelines
and enables seamless integration with systems requiring deterministic responses. The JSON schema
object defines the structure, data types, and constraints, including required fields. While prompting the
model to 'Respond in JSON' can improve accuracy for complex tasks, the ‘response_format’ argument is
the direct mechanism for enforcing the schema. Setting ‘temperature’ to 0 provides more consistent
results for structured output tasks. Option A is a form of prompt engineering, which can help but does
not guarantee strict adherence as response_format’ does. Option B controls randomness and length, not
, output structure. Option D is less efficient for extracting multiple related fields compared to a single
structured output call. Option E's ‘guardrails' are for filtering unsafe or harmful content, not for
enforcing output format.
Question: 3
A. Option A
B. Option B
C. Option C
D. Option D
E. Option E
Answer: A,C
Explanation:
To execute Snowflake cortex AI functions such as 'SNOWFLAKE.CORTEX.COMPLETE ,
‘SNOWFLAKE.CORTEX.CLASSIFY_TEXT, and ‘EMBED_TEXT_768' (or their SAE prefixed counterparts), the
role used by the application in this case) must be granted the 'SNOWFLAKE.CORTEX_USER database role.
Additionally, for the Streamlit application to access any database or schema objects (like tables for data
input/output, or for the Streamlit app itself if it is stored as a database object), the USAGE privilege must
be granted on those specific database and schema objects. Option B, 'CREATE
SNOWFLAKE.ML.DOCUMENT_INTELLIGENCE, is a privilege specific to creating Document AI model builds
and is not required for general Cortex LLM functions. Option D, ‘ACCOUNTADMIN’, grants excessive
privileges and is not a best practice for application roles. Option E, 'CREATE COMPUTE POOL' , is a
privilege related to Snowpark Container Services for creating compute pools, which is not directly
required for running a Streamlit in Snowflake application that consumes Cortex LLM functions.
Question: 4
A data application developer is tasked with building a multi-turn conversational AI application using
Streamlit in Snowflake (SiS) that leverages the COMPLETE (SNOWFLAKE. CORTEX) LLM function. To
ensure the conversation flows naturally and the LLM maintains context from previous interactions,
which of the following is the most appropriate method for handling and passing the conversation
history?
GES-C01
SnowPro® Specialty: Gen AI Certification
Exam Latest Version: 6.1 Practice Exam
Newest 2026.
Question: 1
A data application developer is tasked with building a multi-turn conversational AI application using
Streamlit in Snowflake (SiS) that leverages the COMPLETE (SNOWFLAKE. CORTEX) LLM function. To
ensure the conversation flows naturally and the LLM maintains context from previous interactions,
which of the following is the most appropriate method for handling and passing the conversation
history?
,A. Option A
B. Option B
C. Option C
D. Option D
E. Option E
Answer: C
Question: 2
A Streamlit application developer wants to use AI_COMPLETE (the latest version of COMPLETE
(SNOWFLAKE. CORTEX)) to process customer feedback. The goal is to extract structured information,
such as the customer's sentiment, product mentioned, and any specific issues, into a predictable JSON
format for immediate database ingestion. Which configuration of the AI_COMPLETE function call is
essential for achieving this structured output requirement?
A. Option A
B. Option B
C. Option C
D. Option D
E. Option E
Answer: C
Explanation:
'AI_COMPLETE Structured Outputs' (and its predecessor ‘COMPLETE Structured Outputs’) specifically
allows supplying a JSON schema as the 'response_format’ argument to ensure completion responses
follow a predefined structure. This significantly reduces the need for post-processing in AI data pipelines
and enables seamless integration with systems requiring deterministic responses. The JSON schema
object defines the structure, data types, and constraints, including required fields. While prompting the
model to 'Respond in JSON' can improve accuracy for complex tasks, the ‘response_format’ argument is
the direct mechanism for enforcing the schema. Setting ‘temperature’ to 0 provides more consistent
results for structured output tasks. Option A is a form of prompt engineering, which can help but does
not guarantee strict adherence as response_format’ does. Option B controls randomness and length, not
, output structure. Option D is less efficient for extracting multiple related fields compared to a single
structured output call. Option E's ‘guardrails' are for filtering unsafe or harmful content, not for
enforcing output format.
Question: 3
A. Option A
B. Option B
C. Option C
D. Option D
E. Option E
Answer: A,C
Explanation:
To execute Snowflake cortex AI functions such as 'SNOWFLAKE.CORTEX.COMPLETE ,
‘SNOWFLAKE.CORTEX.CLASSIFY_TEXT, and ‘EMBED_TEXT_768' (or their SAE prefixed counterparts), the
role used by the application in this case) must be granted the 'SNOWFLAKE.CORTEX_USER database role.
Additionally, for the Streamlit application to access any database or schema objects (like tables for data
input/output, or for the Streamlit app itself if it is stored as a database object), the USAGE privilege must
be granted on those specific database and schema objects. Option B, 'CREATE
SNOWFLAKE.ML.DOCUMENT_INTELLIGENCE, is a privilege specific to creating Document AI model builds
and is not required for general Cortex LLM functions. Option D, ‘ACCOUNTADMIN’, grants excessive
privileges and is not a best practice for application roles. Option E, 'CREATE COMPUTE POOL' , is a
privilege related to Snowpark Container Services for creating compute pools, which is not directly
required for running a Streamlit in Snowflake application that consumes Cortex LLM functions.
Question: 4
A data application developer is tasked with building a multi-turn conversational AI application using
Streamlit in Snowflake (SiS) that leverages the COMPLETE (SNOWFLAKE. CORTEX) LLM function. To
ensure the conversation flows naturally and the LLM maintains context from previous interactions,
which of the following is the most appropriate method for handling and passing the conversation
history?
