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How does Snowflake Cortex Analyst support multi-turn conversations to maintain context across user interactions?

A By storing previous interactions in a separate database
B By using a session-based memory feature to track user queries
C By passing full conversation histories to each LLM agent in the workflow
D By using a summarization agent to rewrite questions based on conversation histories

A Gen AI Specialist needs to prepare lengthy documents for LLM ingestion.
Which Snowflake Cortex function will support this requirement by returning an array of text chunks?

A EMBED_TEXT_768
B FINETUNE
C PARSE_DOCUMENT
D SPLIT_TEXT_RECURSIVE_CHARACTER

Which Snowflake Cortex feature or function supports the ability of users to ask questions in natural language and receive direct responses without the need to write SQL code?

A Cortex Search
B Cortex Analyst
C The Cortex CLASSIFY_TEXT function
D The Cortex PARSE_DOCUMENT function

What governance principles does Snowflake guarantee when using its Gen AI features and the underlying models? (Choose two.)

A Snowflake customer data is never made available to other customers or model developers.
B Snowflake enforces Role-Based Access Control (RBAC) o ensure appropriate model access.
C Snowflake allows customers to create fine-tuned models that can be shared with other customers.
D Snowflake only allows third-party models to access Snowflake accounts using the Snowflake Model Registry.
E Snowflake mandates the use of Cortex Guard in the Cortex COMPLETE function to filter unsafe responses from a model.

Which commands will restrict access to Snowflake Cortex functions providing access only to roles that have been explicitly-granted privileges? (Choose two.)

A REVOKE ROLE SNOWELAKE.CORTEX_FUNCTIONS FROM ROLE PUBLIC;
B REVOKE ROLE SNOWFLAKE.CORTEX_USER FROM ROLE PUBLIC;
C REVOKE DATABASE ROLE SNOWFLAKE.CORTEX_USER FROM ROLE PUBLIC;
D REVOKE USAGE ON SCHENA SNOWFLAKE.CORTEX FROM ROLE PUBLIC;
E REVOKE IMPORTED PRIVILEGES ON DATABASE SNOWFLAKE FROM ROLE PUBLIC;

Which function is used to calculate the Manhattan distance between the two points in a vector space?

A VECTOR_INNER_PRODUCT
B VECTOR_L1_DISTANCE
C VECTOR_L2_DISTANCE
D VECTOR_COSINE_SIMILARITY

Which workflow should be used to perform data analysis on unstructured documents using Snowflake Cortex AI features, including Retrieval-Augmented Generation (RAG)?

A
1. Use EMBED_TEXT_768 to extract text from documents.2. Apply SUMMARTZE directly to the PDF files.3. Use TRANSLATE to retrieve the data.
B
1. Use SENTIMENT on binary files.2. Apply TRANSLATE to generate embedding vectors.3. Use CLASSIFY_TEXT to extract layout information from the PDF files.
C
1. Use COMPLETE to summarize documents directly from external URLs.2. Use COUNT_TOKENS to validate the output size.3. Store the output in a text file.
D
1. Use PARSE_DOCUMENT to extract text from documents.2. Store and vectorize the content using EMBED_TEXT_7683. Retrieve data using vector similarity, then use COMPLETE to analyze the data.

What cost categories are incurred by the Cortex Search Services and are tracked in the CORTEX_SEARCH_DAILY_USAGE_HISTORY view? (Choose two.)

A Serving compute
B Cloud services compute
C Virtual warehouse compute
D TOKEN_CREDITS
E EMBED_TEXT_TOKENS

Which object must have the CREATE SNOWFLAKE.ML.DOCUMENT_INTELLIGENCE privilege when building a Snowflake Document AI model?

A Virtual warehouse
B Database
C Schema
D Stage

A Snowflake ACCOUNTADMIN needs to limit LLM usage by granting access only to individual roles. To do this, they disabled LLM usage for all models in the account.
Which commands will enable the GENAI_DEVELOPER role to use the COMPLETE function with the llama3.1–70b model?

A
B
C
D

Which proprietary LLM is used by Snowflake Document AI?

A Arctic-TILT
B deepseek-rl
C jamba-instruct
D mistral-large2

Which Snowflake feature allows application integration for answering questions using structured data within Snowflake?

A Document AI
B Copilot
C Cortex Search
D Cortex Analyst

Which parameters can be tuned in a SNOWFLAKE.CORTEX.COMPLETE function call to control for the randomness and diversity of an LLM response? (Choose two.)

A guardrails
B max_tokens
C response_format
D temperature
E top_p

Use of which Snowflake feature will simplify the generation of vector embeddings and the calculation of vector similarities, thereby reducing the number of retrieval steps required in Retrieval Augmented Generation (RAG) pipelines?

A Cortex Analyst
B Cortex Search
C Cortex Fine-tuning
D Cortex COMPLETE function

How can the costs directly associated with calling the Snowflake Cortex COMPLETE function be minimized? (Choose two.)

A Set max_tokens to limit the number of input tokens.
B Set max_tokens to limit the number of output tokens in the response.
C Reduce the size of the virtual warehouse used to run the function.
D Use an LLM that has more parameters than the current LLM being used.
E Use an LLM that has fewer parameters than the current LLM being used.

Which key factors should be considered when using the Snowflake Model Registry to manage models? (Choose two.)

A Once a model is registered, it cannot be updated or deleted.
B Models need to be exported to external storage before they are registered.
C Models can be deployed and invoked directly using SQL commands in Snowflake.
D Models registered in the Model Registry can only be used with Snowpark for Python.
E The Model Registry supports versioning so that multiple versions of a model can be tracked.

How are suggested questions generated in Snowflake Cortex Analyst? (Choose two.)

A Cortex Analyst only returns questions that are guaranteed to return results.
B Onboarding questions flagged in the Verified Query Repository (VQR) are only shown if the user provides no input.
C When no Verified Query Repository (VQR) is present, Cortex Analyst uses the underlying LLMs to generate up to three suggested questions.
D When a Verified Query Repository (VQR) is defined, Cortex Analyst suggests up to five questions based on semantic similarity to the user's input.
E Cortex Analyst prioritizes queries in the Verified Query Repository (VQR) as suggestions based on the queries that reference the most-frequently used logical columns.

Which Snowflake Gen AI feature or service offers connectivity through an exposed REST API?

A Copilot
B Cortex LLM Playground
C A SNOWFLAKE.CORTEX.COMPLETE SQL function
D A snowflake.cortex.complete Python function

A Gen AI Specialist is building an internal chatbot for a company that will use Streamlit and Snowflake Cortex Analyst to interpret and respond to user queries against Snowflake data.
Team members need to be able to ask a question like:
Can you provide the sales trend for the last 6 months?
The tool needs to be able to answer follow-up questions like:
Can you break that down by region?
What factors contributed to the dip in February?
To ensure the tool interprets the follow-up questions correctly, what must be included in the API request to Cortex Analyst?

A The most recent user message
B A session token to maintain session state across API requests
C The entire conversation history in the messages field of the API request
D A Snowflake session parameter to maintain conversation context among the previous API requests

Which action will minimize costs for Snowflake Cortex Search Services?

A Make frequent but small updates to the source data.
B Split the text search columns into chunks of no more than 612 tokens.
C Reduce the target tag from 5 minutes to 1 minute for real-time indexing.
D Suspend serving when it is not necessary to run the service.

A table in pipeline contains a VARCHAR column containing free-form text responses. A Ger AI Specialist needs to generate three new columns from the text responses.
How should this requirement be met? (Choose two.)

A Use the SNOWFLAKE.CORTEX.COMPLETE function specifying a RESPONSE_FORMAT in the options argument, and use dot notation to extract the data to put into the tree new columns.
B Use the SNOWFLAKE.CORTEX.COMPLETE function specifying a TOP_P in the options argument. and parse the value of the CHOICES response key.
C Use the SNOWFLAKE.CORTEX.ENTITY_SENTIMENT function in a SELECT statement once for each of the three new columns.
D Call the SNOWFLAKE.CORTEX.EXTRACT_ANSWER function in a SELECT statement, and use dot notation to extract data to put into the three new columns,
E Call the SNOWFLAKE.CORTEX.EXTRACT_ANSWER function in a SELECT statement once for each of the three new columns.

This command is executed:

Question Image

This error is received:

Question Image

What is causing this error?

A The temperature option cannot be used with the guardrails option,
B The options argument must be the second parameter in the function call
C The max_tokens option must be included when using the guardrails option.
D The prompt_or_history argument must be an array of objects when using the options argument.

Which arguments are required when using the SNOWFLAKE.CORTEX.COMPLETE function? (Choose two.)

A model
B top_p
C response_format
D max_tokens
E prompt_or_history

Review this query:
SELECT DEMO_DB.DEMO_SCHEMA.EXM_DOC_AI_BUILD!PREDICT(
GET_PRESIGNED_URL(@<stage_name>, '<relative_file_path>'), 9);
What will be the output?

A The query will produce structured data from a document stored in a Snowflake internal stage using Document AI with a model-version identifier
B The query will produce structured data from a document stored in a Snowflake external stage using Document AI with a model-version identifier
C The query will produce semi-structured data from a document stored in a Snowflake internal stage using Document AI with a model-version identifier
D The query will produce semi-structured data from a document stored in a Snowflake external stage using Document AI with a model version identifier

A Gen AI Specialist creates this table:

Question Image

Which function will measure the relationship between vectors without considering their magnitudes?

A VECTOR_L1_DISTANCE
B VECTOR_L2_DISTANCE
C VECTOR_INNER_PRODUCT
D VECTOR_COSINE_SIMILARITY

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