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NEW QUESTION # 194
A data engineering team wants to deploy a proprietary PyCaret classification model, saved as pycaret_best_model.pkl, for inference within Snowpark Container Services (SPCS). They need to register this custom model in the Snowflake Model Registry. Which of the following is a correct and essential step in this process?
Answer: C
Explanation:
NEW QUESTION # 195
An AI development team is deploying a new Cortex Agent and needs to ensure optimal performance and adherence to Snowflake's Gen AI principles regarding data governance. Which of the following statements accurately reflect the models supported by Cortex Agents and the data governance considerations?
Answer: A,C,E
Explanation:
NEW QUESTION # 196
A multi-national corporation uses Snowflake across several AWS regions. Their primary operational Snowflake account is in AWS US East (Ohio), but they need to leverage a specific AI_COMPLETE model, llama4-maverick, which is natively available in AWS US East 1 (N. Virginia) but not in US East (Ohio). To address this, the Snowflake administrator enables cross-region inference for their US East (Ohio) account.
Answer: A,B
Explanation:
Option A is correct because the CORTEX_ENABLED_CROSS_REGION account parameter is used to enable cross-region inference. Setting it CORTEX_ENABLED_CROSS_REGION would permit inference requests to be processed in any AWS US region, such as N. Virginia, from a local AWS US region like Ohio. to 'Aws us' Option B is incorrect because user inputs, service generated prompts, and outputs are explicitly not stored or cached during cross-region inference. Option C is incorrect as cross-region inference is not supported in U.S. SnowGov regions for either inbound or outbound inference requests. Option D is correct because the sources indicate that AI COMPLETE (llama4-maverick) is natively available in AWS US East 1 (N. Virginia) and is supported for cross-region inference within AWS US regions, making it a valid target for the account in US East (Ohio). Option E is incorrect because latency between regions depends on the cloud provider infrastructure and network status, and Snowflake recommends testing specific use cases with cross-region inference enabled.
NEW QUESTION # 197
A team is designing a complex Gen AI application in Snowflake, which includes components for training a custom LLM, running batch inference, and providing a real-time conversational interface. They plan to leverage Snowpark Container Services (SPCS) for these workloads. Which of the following statements accurately describe the suitable SPCS service design models and important considerations for these different application components? (Select all that apply.)
Answer: C,D,E
Explanation:
Options A, B, and C are correct descriptions of SPCS service design models and their applications. Option A is correct: Jobs in SPCS are containerized tasks that execute and run to completion, making them ideal for finite operations like GPU-accelerated machine learning model training. Option B is correct: Services are designed for long-running applications, offering continuous availability and accessibility via internal and external endpoints, which is suitable for real-time inference in conversational interfaces. Option C is correct: Service Functions are callable computations that accept data as input, often from SQL queries. A key advantage is that data processing occurs within the Snowflake network boundary, making them efficient and secure for data-intensive tasks like batch inference. Option D is incorrect: While is a cost- effective CPU instance, GPU instances like 'GPU_NV_M' are explicitly optimized for 'intensive GPU usage scenarios like Computer Vision or LLMs/VLMs'. Therefore, using CPU-only instances for all LLM tasks, especially performance-critical ones, is not the general best practice. Option E is incorrect: Container images for Snowpark Container Services are stored in Snowflake's OCIv2 compliant Image Registry, not typically pulled directly from public Docker Hub repositories for deployment within Snowflake. The image registry has a unique hostname which allows OCI clients to access it via REST API calls, and images are pushed to image repositories within this registry.
NEW QUESTION # 198
A data team is designing a new Cortex Analyst application and wants to ensure optimal performance, accuracy, and user experience for text-to-SQL conversions. They are particularly interested in how custom instructions interact with other semantic model features and LLM functionalities. Which of the following statements about using in Cortex Analyst are accurate?
Answer: C,E
Explanation:
Option A is correct because custom instructions provide unique business context to the LLM, enabling greater control over the generated SQL queries to align with specific business needs or formatting. Option C is also correct because by providing business context to the LLM via custom instructions, the model can better handle domain-specific terminology or complex business logic, improving accuracy. Option B is incorrect; a 'verified_querv' provides a *pre-written and verified SQL query* for a specific question. If a user's question is similar to a verified query, Cortex Analyst typically uses that query, potentially overriding or prioritizing it over general 'custom_instructions' for that specific scenario, as verified queries are explicit answers. The sources imply that verified queries are a direct solution for known questions, while custom instructions provide general guidance. Option D is incorrect for Cortex Analyst; the credit rate usage is based on the number of messages processed, not the number of tokens, so the length of custom instructions doesn't directly affect cost via token count. Option E is incorrect as 'custom_instructions' are for guiding SQL generation, not for defining or extending the semantic model's structure (logical tables, dimensions).
NEW QUESTION # 199
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