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GES-C01 Certificate Exam - GES-C01 Best Study Material

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GES-C01 Certificate Exam - GES-C01 Best Study Material

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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q78-Q83):NEW QUESTION # 78
A data engineering team is setting up a new Cortex Search Service named to power a RAG application over their table, which stores historical ticket text and metadata. They need to ensure proper setup, cost efficiency, and data integrity. Which of the following statements are true regarding the creation and initial configuration of this Cortex Search Service? (Select all that apply)
  • A. If the service is created using the Snowsight AI & ML Studio, its name will be double-quoted, and thus must be double-quoted when referenced in subsequent SQL queries.
  • B. Columns intended to be filterable in search queries must be explicitly listed in the 'ATTRIBUTES' field during service creation and must also be included in the source query for the service.
  • C. The 'CREATE CORTEX SEARCH SERVICE command should specify a Snowpark-optimized warehouse for optimal performance, as it is designed for memory-intensive ML workloads.
  • D. The role used to create the Cortex Search Service must be granted the 'SNOWFLAKE.CORTEX_USER database role.
  • E. To enable continuous updates of the search index as new tickets are added,

Answer: A,B,D,E
Explanation:
Option A is incorrect. Snowflake recommends using a dedicated warehouse of size no larger than MEDIUM for a Cortex Search Service, as larger warehouses do not necessarily increase performance for these functions. Snowpark-optimized warehouses are primarily for ML training workloads with large memory requirements. Option B is correct. Change tracking is required on the base table to allow continuous updates of the search service, especially if the role creating the service does not own the table. Option C is correct. The role used to create a Cortex Search Service must be granted the "SNOWFLAKE.CORTEX_USER database role. Option D is correct. Any columns specified in the 'ATTRIBUTES' field for filtering must also be included in the source query that defines the search service. Option E is correct. When a Cortex Search Service is created from Snowsight, its name is double-quoted, meaning it must be referenced using double-quotes in SQL queries.

NEW QUESTION # 79
A Snowflake administrator is tasked with ensuring that a specific data science team can only use approved LLMs (mistral-7b, llama3.1-8b) for generative AI tasks within a particular schema, and also needs to enable the use of an LLM in a non-native region due to specific project requirements. Which combination of configurations would meet these requirements?

  • A. Option B
  • B. Option C
  • C. Option A
  • D. Option E
  • E. Option D
Answer: A,C
Explanation:


NEW QUESTION # 80
data scientist is designing a Retrieval Augmented Generation (RAG) system in Snowflake for querying a large knowledge base of internal documents. They plan to store document embeddings and use vector similarity for retrieval. Which statement accurately describes the use of vector functions and associated costs in this RAG architecture?
  • A.
  • B. The
  • C. When using
  • D. Snowpark Python fully supports all Snowflake vector similarity functions, including
  • E. Storing vector embeddings, created using functions like

Answer: C
Explanation:
Option A is incorrect because

is a vector similarity function, not an embedding generation function. Embedding generation functions like

incur compute costs based on input tokens. Option B is incorrect. While there are storage costs associated with storing data in Snowflake, the sources do not state that storing vector embeddings themselves incurs 'token-based costs for the storage itself; rather, the EMBED_TEXT functions incur compute costs per input token during the embedding creation process. Option C is correct. Vector similarity functions, including

, do not incur token-based costs. This contrasts with embedding creation which does incur such costs. Option D is incorrect. Snowflake Cortex provides four vector similarity functions:

; no single one is stated as the 'only recommended' or universally superior. Option E is incorrect. The Snowpark Python library explicitly states it does not support the

function, making the claim of full support for all similarity functions false.

NEW QUESTION # 81
An enterprise is deploying a Cortex Analyst application and needs to manage its cost, ensure data security, and understand its operational behavior within Snowflake. Which of the following statements are true regarding the deployment, cost, and security of Cortex Analyst and its semantic models?
  • A. The CORTEX_ANALYST_USER database role is sufficient for making requests to Cortex Analyst, and the cost incurred is solely based on the number of tokens processed by the underlying LLMs, not per message.
  • B. Cortex Analyst applications are fully managed, and by default, all data, including metadata and prompts, remain within Snowflake's governance boundary when using Snowflake-hosted LLMs from Mistral and Meta.
  • C. Enabling the account parameter is the recommended approach for using Azure OpenAI models with Cortex Analyst to ensure the highest performance and adherence to RBAC restrictions.
  • D. Administrators can monitor Cortex Analyst requests, including the user, question asked, generated SQL, and errors, by querying the SNOWFLAK LOCAL. CORTEX ANALYST_REQUESTS function.
  • E. Semantic models for Cortex Analyst, stored as YAML files in a Snowflake stage, should have their stage access controlled by RBAC to implicitly control access to the underlying tables referenced in the semantic model.
Answer: B,D
Explanation:
Option A is incorrect. While stage access is controlled by RBAC, roles granted access to a stage must also have SELECT access on all tables referenced in the semantic models on that stage; stage access alone does not implicitly grant table access. Option B is incorrect. The 'CORTEX_ANALYST_USER' database role is sufficient for making requests to Cortex Analyst, but credit usage is based on the number of messages processed (67 Credits per 1 ,000 messages), not the number of tokens in each message. Option C is correct. Cortex Analyst is a fully managed service, and when using Snowflake-hosted LLMs from Mistral and Meta (the default), all data, including metadata and prompts, remains within Snowflake's governance boundary. Option D is incorrect. Snowflake strongly discourages the use of the 'ENABLE_CORTEX_ANALYST_MODEL_AZURE_OPENAI' parameter and advises migration to Snowflake-hosted OpenAI models. Additionally, when this parameter is enabled, Azure OpenAI models do not respect RBAC restrictions. Option E is correct. Cortex Analyst logs requests to an event table, which administrators can query using to view details such as the user, question, generated SQL, and any errors or warnings.

NEW QUESTION # 82
A company is planning to process a large volume of legal documents to generate summaries using SNOWFLAKE. CORTEX. SUMMARIZE. Given the scale, they are acutely focused on managing costs and optimizing performance. Which of the following statements are true regarding the cost and performance characteristics of using SNOWFLAKE. CORTEX. SUMMARIZE? (Select all that apply)
  • A. For SUWARIZE, Snowflake adds an internal prompt to the user's input text, which increases the total input token count for billing purposes beyond the raw text length.
  • B. Snowflake recommends using a larger warehouse (e.g., L or XL) for SUMMARIZE function calls to significantly improve processing performance for high-volume tasks.
  • C. The SUWARIZE function is billed primarily based on the number of output tokens generated in the response, not input tokens.
  • D. The context window for the SWIMARIZE function is 4,096 tokens, ensuring efficiency for short documents only.
  • E. The fixed billing rate for the SUMMARIZE function is 0.10 Credits per one million Tokens processed.
Answer: A,E
Explanation:
Options B and D are correct. - is correct": For 'SUMMARIZE, Snowflake adds an internal prompt to the input text in order to generate the response, which results in a higher input token count for billing than the raw text provided. - is correct'': The cost for the Summarize' function is 0.10 Credits per one million Tokens processed. -A is incorrect: For functions that generate new text in the response, such as 'SUMMARIZE' , both input and output tokens are billable. - C is incorrect: Snowflake recommends executing queries that call a Snowflake Cortex AISQL function, including SUMMARIZE , with a smaller warehouse (no larger than MEDIUM), as larger warehouses do not increase performance for these operations. - E is incorrect: The context window for the Snowflake managed model from the 'SUMMARIZE function is 32,000 tokens, not 4,096 tokens.

NEW QUESTION # 83
......
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