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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q68-Q73):

NEW QUESTION # 68
An organization enforces strict LLM access control. The has configured 'CORTEX MODELS_ALLOWLIST ='mistral-large2" and executed 'CALL SNOWFLAKE.MODELS.CORTEX BASE MODELS REFRESH()'. A developer, whose role DEV ROLE has been granted 'SNOWFLAKE.CORTEX USER' and 'SNOWFLAKE."CORTEX-MODEL-ROLE-CLAUDE-3-5- SONNET"' , attempts to make a REST API call to 'api/v2/cortex/inference:complete' using 'claude-3-5-sonnet' as the model name in the request body. Which of the following statements are true regarding this scenario?

A. Option C
B. Option A
C. Option B
D. Option D
E. Option E

Answer: A,C

Explanation:
Option A is incorrect. When the model is specified as a plain string '"claude-3-5-sonnet"' in the REST API request, Cortex first attempts to match it as a schema-level model object by that plain name. If this fails (as is typical for built-in models which often use quoted identifiers like "'CLAUDE-3-5-SONNET"'), it then checks the plain name against the 'CORTEX_MODELS_ALLOWLIST. Since 'claude-3-5- sonnet' is not in the allowlist, the call will be denied despite the user's role having the application role for the model object 'SNOWFLAKE.MODELS."CLAUDE-3-5-SONNET"'. option B is correct because the parameter explicitly limits which models can be used by the 'Cortex LLM REST API'. Option C is correct. To use 'claude-3-5-sonnet' via the REST API, it either needs to be added to the account-level as a plain name, or the request must explicitly use the fully-qualified object identifier 'SNOWFLAKE.MODELS."CLAUDE-3-5-SONNET"' which would then be subject to RBAC on that model object. Option D is incorrect. An HTTP '403 Not Authorized' error typically indicates that the account is not enabled for the REST API or the calling user's default role lacks the 'SNOWFLAKE.CORTEX USER database role. For 'AI_COMPLETE (which 'COMPLETE' maps to), an error related to an unallowed model would contain information about how to modify the allowlist, implying a different error message than a generic '403'. Option E is incorrect. The 'ENABLE CORTEX ANALYST MODEL AZURE_OPENAF parameter is specifically for Cortex Analyst and determines if it can use Azure OpenAl models, but it is incompatible with model-level RBAC for Cortex Analyst. It does not affect generic 'COMPLETE (SNOWFLAKE.CORTEX)' REST API calls.

NEW QUESTION # 69
A data scientist has successfully deployed a Hugging Face sentence transformer model to Snowpark Container Services (SPCS) for GPU-powered inference, making it accessible via an HTTP endpoint. To ensure secure and proper programmatic access to this service from an external application, which of the following statements correctly describe the authentication and access control considerations for calling this public endpoint?

A. Applications must use key pair authentication to generate a JSON Web Token (JWT), exchange it with Snowflake for an OAuth token, and then use this OAuth token to authenticate requests to the public endpoint.
B. The default role for the calling user must have the 'SNOWFLAKCORTEX USER database role granted to access the SPCS service via its public endpoint.
C. The Python API for calling the service requires the Snowflake session object directly, bypassing HTTP endpoint authentication.
D. The public endpoint of the SPCS service can be accessed directly without any authentication, as it's a public endpoint.
E. The 'Authorization' header with a 'Snowflake Token=""' value is a valid method for authenticating requests to the public endpoint programmatically.

Answer: A,E

Explanation:
When using a model deployed to SPCS via its public HTTP endpoint, applications authenticate requests using key pair authentication to generate a JSON Web Token (JWT), then exchange the JWT for an OAuth token, which is subsequently used for authentication. An example shows generating headers including authorization using a session token, formatted as 'Authorization: Snowflake Token=""'. Therefore, options B and D are correct. Option A is incorrect because public endpoints still require authentication. Option C describes internal access via the Snowpark Python API, not external HTTP endpoint authentication. Option E, while relevant for Snowflake Cortex LLM functions accessed via the REST API generally, is not the primary mechanism for authenticating to a SPCS public HTTP endpoint, which uses tokens derived from key pair authentication or session tokens.

NEW QUESTION # 70
A Snowflake administrator needs to configure Snowflake Copilot for a team distributed across different geographical regions, some of which are not natively supported for Copilot. Additionally, the team requires Copilot to adopt a specific tone in its responses. Which of the following correctly outlines the configuration steps for these requirements?

A. Option A
B. Option B
C. Option C
D. Option D
E. Option E

Answer: B

Explanation:

NEW QUESTION # 71
A Gen AI specialist is designing a RAG pipeline utilizing Cortex Search for an application that queries a large repository of unstructured text documents. To optimize the quality of retrieval and subsequent LLM responses, what are the critical best practices and understanding of Cortex Search's mechanisms that the specialist should consider regarding text processing and tokenization?

A. When text input exceeds an embedding model's context window, Cortex Search truncates the text for both semantic embedding and keyword-based retrieval, potentially losing critical information.
B. Cortex Search operates solely on vector embeddings for semantic search; keyword-based retrieval is handled by a separate, less efficient mechanism outside the core search service.
C. The SNOWFLAKE .CORTEX. COUNT TOKENS function is a helper function that can be used to accurately determine the token count for a given string based on a specified model, aiding in adherence to context window limits.
D. For best search results, text in the search column should be split into chunks of no more than 512 tokens, as smaller chunks generally lead to more precise retrieval and relevant LLM context.
E. Embedding models with larger context windows, such as snowflake-arctic-embed-1-v2.e-8k (8000 tokens), are always superior as they allow the RAG system to process the entire document as a single, highly relevant chunk.

Answer: C,D

Explanation:
Option A is correct; Snowflake recommends splitting text into chunks of no more than 512 tokens for optimal search results and higher retrieval/LLM response quality, even with longer-context embedding models. Option B is incorrect because while Cortex Search truncates strings exceeding the context window for embedding, it uses the full body of text for keyword-based retrieval. Option C is incorrect as research shows smaller chunk sizes typically result in higher retrieval and downstream LLM response quality, despite the availability of longer-context models. Option D is correct, as COUNT _ TOKENS is a helper function used for this purpose. Option E is incorrect because Cortex Search is a hybrid (vector and keyword) search engine.

NEW QUESTION # 72
A data engineer is working with Snowflake Cortex Analyst to improve its ability to answer natural language questions by precisely identifying product names for filtering. They have decided to integrate a Cortex Search Service with their semantic model to enhance literal search for the 'product_name' dimension. Which of the following configurations within the semantic model's YAML file are valid and effective for this purpose?

A. Adding a 'cortex_search_service' entry to the 'product_name' dimension with only the 'service' field:
B. Adding a entry to the 'product_name' dimension, including 'literal_column' and ensuring the search service is configured to index the physical column:
C. Setting true' for the 'product_name' dimension and providing an exhaustive list of to restrict the model to only those values.
D. Only specifying 'sample_valueS for the 'product_name' dimension without a entry.
E. Including in the semantic model's 'metrics' section, referencing 'product_name'.

Answer: B,C

Explanation:
Option C is correct because integrating a Cortex Search Service with Cortex Analyst for literal search improvement involves adding a entry to the relevant dimension. Specifying the 'literal_column' explicitly helps map the semantic dimension to the underlying column indexed by the search service, ensuring the correct values are used for semantic search. Option D is also correct. For dimensions with relatively low-cardinality values, setting 'is_enum: true and providing 'sample_values' tells the model to choose only from that predefined list, which is an effective way to improve literal usage without requiring an external Cortex Search Service. Option A is incorrect because while 'sample_valueS are used for semantic similarity search for low-cardinality dimensions, they don't leverage the full capabilities of a Cortex Search Service for 'fuzzy' search over potentially high-cardinality data. Option B is incomplete. While specifying the 'service' name is a start, it's beneficial to explicitly define the 'literal_column' if it differs from the dimension's expression or if precision is critical. Option E is incorrect because is a configuration for a 'dimension', not a 'metric'.

NEW QUESTION # 73
......

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