GES-C01 Exam Dumps Demo, GES-C01 Latest Braindumps Questions

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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q323-Q328):

NEW QUESTION # 323
An ML engineering team is preparing to log a custom Python model to the Snowflake Model Registry. This model has several Python package dependencies. The team wants to ensure the model can be deployed optimally, either in a Snowflake warehouse or to Snowpark Container Services (SPCS), depending on future needs. They are particularly concerned with how dependency specification impacts deployment eligibility. Which statements accurately describe how Snowflake handles model dependencies and determines deployment eligibility for custom Python models logged in the Model Registry, particularly when considering both Snowflake warehouse and Snowpark Container Services (SPCS) environments? (Select all that apply.)

Answer: A,B,D

Explanation:
Option A is correct. When a model version is logged using 'reg.log_moder , its 'conda_dependencies' are validated against the Snowflake conda channel. If all dependencies are found there, the model is considered eligible to run in a warehouse. Option B is incorrect. Snowpark Container Services models, by default, obtain their 'conda_dependencieS from 'conda-forge' . Therefore, SPCS does not prohibit conda packages from 'conda-forge' . Option C is correct. The Snowflake documentation explicitly states that for models running on Snowpark Container Services (SPCS), 'conda-forge' is the assumed channel for 'conda_dependencies' , while the Snowflake conda channel is for warehouse deployments only. Option D is correct. If the 'WAREHOUSE platform is specified in the 'target_platforms' argument of , and the model is ineligible for warehouse deployment (e.g., due to its size, dependencies, or GPU requirements), the call will fail. Option E is incorrect. Snowflake recommends using 'either' 'conda_dependencieS 'or' , but not both simultaneously. This is because combining both can lead to package conflicts, causing the container image to build successfully but potentially resulting in an unexpected or non-functional container image.


NEW QUESTION # 324
A development team is preparing to deploy a new Retrieval-Augmented Generation (RAG) application written in Python. They intend to use Snowflake AI Observability to capture detailed logs and traces for debugging and performance analysis. Which of the following configurations are essential prerequisites for enabling this logging capability effectively?

Answer: A,B,C,E

Explanation:


NEW QUESTION # 325
A data application developer is building a Streamlit chat application within Snowflake. This application uses a RAG pattern to answer user questions about a knowledge base, leveraging a Cortex Search Service for retrieval and an LLM for generating responses. The developer wants to ensure responses are relevant, concise, and structured. Which of the following practices are crucial when integrating Cortex Search with Snowflake Cortex LLM functions like AI_COMPLETE for this RAG chatbot?

Answer: A,E

Explanation:
Option A is incorrect. The user's query is typically embedded (e.g., using to perform a similarity search against the Cortex Search Service. The 'retrieved documents' (context) are then passed to the 'AI COMPLETE function, not the embedding function itself. Option B is correct because to provide a stateful, conversational experience, all previous user prompts and model responses should be passed in the array to the 'COMPLETE or function. Option C is incorrect. While concatenation is a method, for better accuracy and control, the retrieved context should be integrated into a well-engineered prompt, often using tags or specific instructions, rather than just raw concatenation, to guide the LLM's response. Option D is correct because 'AI_COMPLETE Structured Outputs' allows you to supply a JSON schema that completion responses must follow, reducing the need for post-processing and enabling seamless integration with systems requiring deterministic responses. Option E is incorrect. While keeping processing within Snowflake is good for data governance, complex RAG pipelines often involve multiple distinct steps (query embedding, search, retrieval, LLM completion) that may benefit from a staged approach rather than a single monolithic SQL statement. The optimal approach depends on the specific complexity and performance requirements, and a single 'SELECT for the 'entire' RAG flow might not always be the most efficient or practical solution.


NEW QUESTION # 326
A data engineer is integrating SNOWFLAKE. CORTEX. CLASSIFY_TEXT into an automated data pipeline that uses dynamic tables to process and transform streaming text dat a. They have ensured that the service account used has been granted the necessary SNOWFLAKE. CORTEX_USER database role. After deploying the pipeline, they consistently receive an error whenever CLASSIFY_TEXT is invoked. Which of the following is the most likely cause of the error encountered by the data engineer?

Answer: C

Explanation:
Option A is plausible for a data-specific error, but the question describes a 'consistent error' during pipeline integration. The maximum number of categories is 100. Option B is incorrect because if the text contains non-plain English content like code snippets, the function 'won't return an error, but the results may not be what you expect'. This would lead to inaccurate results, not a consistent error preventing the function's execution. Option C is less likely to be the 'most' likely cause of an error specific to the 'CLASSIFY_TEXT function's invocation, especially since the 'SNOWFLAKE.CORTEX_USER role, which grants access to Cortex AI functions, has already been granted. Missing 'USAGE on the data's database would typically manifest as a more general SQL access error. Option D is correct because a known limitation for Snowflake Cortex functions, including "CLASSIFY _ TEXT , is that they do not support dynamic tables. This is a fundamental incompatibility that would cause consistent errors when integrating into a dynamic table pipeline. Option E is incorrect. While a 'task_description' should be 'no more than about 50 words', this is a recommendation for optimal performance, not a strict limit that is explicitly stated to cause an error when exceeded.


NEW QUESTION # 327
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?

Answer: C,D,E

Explanation:
Option A is incorrect because a higher temperature, such as 0.8, controls the randomness of the output by influencing which possible token is chosen at each step, resulting in more diverse and random output, not deterministic and focused. Option B is correct because the 'max_tokens' option sets the maximum number of output tokens in the response, and small values can result in truncated responses. Option C is correct because the 'guardrails: TRUE option enables Cortex Guard to filter potentially unsafe and harmful responses from a language model. Option D is correct because AI_COMPLETE Structured Outputs allows you to supply a JSON schema that completion responses must follow, and every model supported by AI_COMPLETE supports structured output. Option E is incorrect because to provide a stateful conversational experience, all previous user prompts and model responses should be passed in the 'prompt_or_history' array, but the number of tokens processed increases for each round, and costs increase proportionally. The 'COMPLETE' function is the older version of 'AI_COMPLETE'.


NEW QUESTION # 328
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