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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A Data Scientist has a pre-trained PyCaret model and wants to log it into the Snowflake Model Registry for inference. The model requires specific versions of and 'scipy' , and a configuration file 'my_config.json' needs to be packaged with the model for use during inference. Assuming 'sp_session' is an active Snowpark Session, is an instance of 'PyCaretModel' , and 'train_features' is a Pandas DataFrame for which of the following code snippets correctly logs this custom PyCaret model into the Snowflake Model Registry?

A)

B)

C)

D)

E)

2. A data analyst is working with a table named ARTICLE_CONTENT that contains a column (VARCHAR) storing lengthy English articles. They need to generate a concise summary for each article. The analyst plans to use the SNOWFLAKE. CORTEX. SUMMARIZE function. Which of the following accurately describes the syntax and the expected data type of the result for a single article summary?

A) The query

B) The query

C) The query

D) The query

E) The query

3. A Gen AI Specialist is developing a conversational analytics application using Cortex Analyst, aiming to provide a seamless multi-turn conversation experience for business users querying structured dat a. The team observes that follow-up questions are sometimes misinterpreted, especially when the conversation history is long. Which of the following statements accurately describe how Cortex Analyst handles multi-turn conversations and key considerations for optimizing this functionality?

A) Cortex Analyst incorporates an additional LLM summarization agent before its original workflow to rewrite current-turn questions based on conversation history, with Llama 3.1 70B being a recommended model for this task due to its performance in evaluating summarization quality.
B) When a user shifts intent frequently in a multi-turn conversation, Cortex Analyst automatically resets the conversation history to prevent misinterpretations and improve accuracy.
C) To address misinterpretation in long conversations, the max_tokens parameter for the Cortex Analyst REST API should be significantly increased to ensure the LLM receives the complete historical context without truncation.
D) Cortex Analyst simply passes the entire conversation history to all subsequent LLM calls, and optimizing this requires manually truncating the array in messages the REST API request.
E) Multi-turn conversation in Cortex Analyst is primarily handled by the CORTEX_ANALYST_MODEL AZURE_OPENAI parameter, which, when enabled, allows Azure OpenAl models to manage conversational context more effectively.

4. A financial analytics team is using AI_COMPLETE to extract specific financial metrics (e.g., revenue, profit margin) from quarterly reports and requires the output in a strict JSON format for automated ingestion into a data warehouse. They've encountered issues where the LLM sometimes generates malformed JSON or includes extraneous text. Which of the following approaches will help ensure deterministic, schema-compliant JSON outputs and mitigate these 'hallucinations' related to format?

A) Set the 'temperature' option to 0 when calling 'AI_COMPLETE' to obtain the most consistent and deterministic JSON outputs.
B)

C)

D)

E) Include a prompt instruction such as 'Respond in JSON' to improve adherence, especially for complex tasks, even though 'response_format' is provided.