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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q30-Q35):

NEW QUESTION # 30
A marketing team is analyzing social media comments using Snowflake and wants to categorize them into predefined campaign sentiments (e.g., 'Positive Campaign Engagement', 'Negative Campaign Feedback', 'Neutral Discussion'). They decide to use the SNOWFLAKE. CORTEX. CLASSIFY TEXT function for this task. Which of the following statements about its usage are correct?

A. To provide more context and potentially improve classification accuracy, categories within the can be defined as SQL objects, including 'description' and 'examples' fields.
B. The argument must contain exactly two string values for effective binary classification, otherwise an error is returned.
C. CLASSIFY_TEXT can return a JSON object with a 'label' field, where the value of this field indicates the classified category of the input text.
D. If the input text exceeds a model-specific token limit, CLASSIFY_TEXT will automatically truncate the text before processing without raising an error.
E. The input string to CLASSIFY_TEXT is case-insensitive, meaning 'Great product!' and 'great product!' will yield identical classification results due to automatic normalization.

Answer: A,C

Explanation:
Option A is incorrect because both the input string and categories for 'CLASSIFY TEXT are case sensitive, meaning different capitalizations can lead to different results. Option B is incorrect because the argument must contain at least two and at most 100 unique categories. Option C is correct as returns an OBJECT (VARIANT) whose 'label' field specifies the category to which the input prompt belongs. Option D is correct because categories can be simple strings or SQL objects, allowing for a description and examples to be provided, which can improve accuracy. Option E is incorrect because the documentation for 'CLASSIFY _ TEXT does not mention automatic truncation of input text based on a token limit, although LLMs typically have context windows. The source only mentions that for non- plain English text, results may not be what you expect, not that the input would be truncated.

NEW QUESTION # 31
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?

A. For performance and cost optimization, it is always recommended to query Cortex Search and the LLM function within a single
B. The
C. Using the
D. To maintain conversational context in a multi-turn chat, the developer should pass all previous user prompts and model responses in the
E. The retrieved context from Cortex Search should be directly concatenated with the user's prompt as input to the

Answer: C,D

Explanation:
Option A is incorrect. The user's query is typically embedded (e.g., using 'EMBED_TEXT 768') 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 'AI_COMPLETE' 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 # 32
A financial data team is implementing a Snowflake Cortex AI solution to summarize regulatory documents using SNOWFLAKE.CORTEX.TRY_COMPLETE They aim for both cost efficiency and high reliability, especially when dealing with documents that might occasionally exceed model context limits or result in malformed output. Which of the following statements about the cost and operational behavior of TRY_COMPLETE are TRUE in this context? (Select all that apply)

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

Answer: B,D,E

Explanation:
Option A is correct because

option is used), the number of tokens processed (and billed) increases with schema complexity. Larger and more complex schemas generally consume a larger number of input and output tokens.

NEW QUESTION # 33
A Snowflake account is located in the AWS US East 1 (N. Virginia) region. The 'ACCOUNTADMIN' has set the 'CORTEX MODELS ALLOWLIST' to "mistral-7b" and 'CORTEX ENABLED CROSS REGION' to 'ANY REGION'. A data scientist, whose role has only the 'SNOWFLAKE.CORTEX USER database role, performs several 'AI COMPLETE calls. Which of the following statements correctly describe the behavior of these calls under the given configuration?

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

Answer: A,D

Explanation:
The 'CORTEX MODELS_ALLOWLIST parameter restricts which models can be used for Cortex LLM functions, including 'AI_COMPLETE. The parameter allows inference requests to be processed in a different region from the default, which may incur increased latency. Option A is correct: 'mistral-7b' is in the It is natively available in AWS US East 1 (N. Virginia). Thus, an 'AI_COMPLETE call using this model will execute successfully and locally, with standard latency. option B is incorrect: 'claude-3-5-sonnet' is not in the The applies to 'AI_COMPLETE. Therefore, the call would return 'NULL" (if using 'TRY_COMPLETE) or raise an error (if using 'AI_COMPLETE) due to the allowlist restriction, not solely because of 'ANY_REGION'. Option C is correct: is not in the current = ANY_REGION' allows models to be processed in other regions. However, this parameter does not bypass the Therefore, for 'llama3.1-8b' to be used successfully (even via cross-region inference), it 'must' be included in the 'CORTEX MODELS ALLOWLIST. If it were included, it would execute via cross-region inference with potential latency, as it is available cross-cloud. The statement accurately describes the condition for its successful execution. Option D is incorrect: The Cortex LLM Playground respects the 'CORTEX MODELS ALLOWLIST. The 'ANY REGION' setting does not bypass this governance control. Option E is incorrect: The parameter enables cross-region processing for 'allowed' features and models; it does not implicitly modify the 'CORTEX MODELS_ALLOWLIST to include all models.

NEW QUESTION # 34
An ML Engineer has successfully deployed a custom text embedding model, 'my_embedder model', to a Snowpark Container Service named 'text embedding_service' within their Snowflake account. This model has an 'encode' method that accepts a string and returns a vector. They now need to integrate inference calls from this deployed model into various applications. Which of the following are valid ways to invoke this model for inference?

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

Answer: A,C,D

Explanation:
Option A is correct because the 'run' method of a 'ModelVersion' object is used to perform inference on models deployed to Snowpark Container Services via the Python API. Option B is correct because Snowflake Model Serving creates SQL service functions, named as 'service_name!method_name', to act as a bridge from SQL to the model running in SPCS. Option C is correct because if ingress is enabled during service creation, the model can be invoked via a dedicated HTTP endpoint. Option D is incorrect because this approach would bypass the deployed Snowpark Container Service entirely, and models deployed to SPCS are intended to be run there, not re-loaded into a standard UDF runtime, which might have different environments or resource constraints. Option E is incorrect because is a built-in Snowflake Cortex LLM function, not a mechanism to call a custom model deployed via the Model Registry and Snowpark Container Services. The model argument for Cortex functions expects specific pre-defined model names, not custom service names.

NEW QUESTION # 35
......

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