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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q158-Q163):
NEW QUESTION # 158
An ML engineer is preparing a Docker image for a custom LLM application that will be deployed to Snowpark Container Services (SPCS). The application uses a mix of packages, some commonly found in the Snowflake Anaconda channel and others from general open-source repositories like PyPI. They have the following Docker-file snippet and need to ensure the dependencies are correctly installed for the SPCS environment to support a GPU workload. Which of the following approaches for installing Python packages in the Dockerfile would ensure a robust and compatible setup for a custom LLM running in Snowpark Container Services, based on best practices for managing dependencies in this environment?
Answer: D
Explanation:
Option B is correct. The provided Dockerfile example for deploying Llama 2 in Snowpark Container Services explicitly uses 'conda install -n rapids -c https://repo.anaconda.com/pkgs/snowflake' to install Snowflake-specific packages like 'snowflake-ml-python' and 'snowflake- snowpark-python' from the Snowflake Anaconda channel. It then uses 'pip install' for other open-source libraries that are not available or preferred from the Anaconda channels. Option A is incorrect because while pip can install many packages, the provided example demonstrates using 'conda' from the Snowflake Anaconda channel for certain foundational packages. Option C is incorrect because while 'conda-forge' is a common channel for open-source packages, the specific Snowflake-related packages in the example are pulled directly from the 'https://repo.anaconda.com/pkgs/snowflake' channel. Although Source notes that 'conda-forge' is assumed for 'conda_dependencies' in when building container images, a Dockerfile explicitly defining 'RUN conda install' can specify the channel, which the example in demonstrates. Option D is incorrect because the 'defaultS channel often requires user acceptance of Anaconda terms, which is not feasible in an automated build environment. Option E is a generic approach for pip dependencies but doesn't specifically address the recommended use of 'conda' from the Snowflake Anaconda channel for certain core Snowflake packages as shown in the practical example.
NEW QUESTION # 159
A Gen AI Specialist is setting up AI Observability for a new generative AI application in Snowflake to monitor its performance and debug issues. The application is built in Python. Which of the following prerequisites must be met to enable tracing for this application?
Answer: D,E
Explanation:
NEW QUESTION # 160
A global enterprise has Snowflake accounts in various regions, including a US East (Ohio) account where a critical application is deployed. They need to use AI_COMPLETE with the claude-3-5-sonnet model for real-time customer support, but this model is not natively available in US East (Ohio) for direct AI_COMPLETE usage. The Snowflake administrator considers enabling cross-region inference. Which statements accurately reflect the considerations and characteristics of cross-region inference in Snowflake Cortex?
Answer: B,C
Explanation:
Option B is correct because setting the parameter to 'ANY_REGION' enables inference requests to be CORTEX_ENABLED_CROS_ REGION processed in a different region from the default, thereby allowing access to models not natively supported in the local region. For example, claude- 3 - 5- sonnet is available in AWS US East 1 (N. Virginia), which could be accessed from US East (Ohio) via cross-region inference. Option C is 3-5 -sonnet correct as cross-region inference is explicitly not supported in U.S. SnowGov regions for either inbound or outbound inference requests. Option A is incorrect because user inputs, service generated prompts, and outputs are not stored or cached during cross-region inference. Option D is incorrect; latency depends on the cloud provider infrastructure and network status, and testing is recommended. Option E is incorrect because CORTEX_ENABLED_CROSS_REGION is an account-level parameter, not a session parameter.
NEW QUESTION # 161
A development team plans to utilize Snowpark Container Services (SPCS) for deploying a variety of AI/ML workloads, including custom LLMs and GPU-accelerated model training jobs. They are in the process of creating a compute pool and need to select the appropriate instance families and configurations. Which of the following statements about 'CREATE COMPUTE POOL' in SPCS are accurate?
Answer: B,C
Explanation:
Option A is correct. GPU-accelerated workloads, such as LLM inference and model training, require instance families specifically designed with GPUs. The documentation lists instance family names starting with 'GPU' for this purpose, such as or 'GPU_GCP NV L4 Option B is incorrect. While 'MIN NODES and 'MAX NODES define the range, the size of compute clusters in Snowpark Container Services does ''not'' auto-scale dynamically based on workload demand. Users must manually alter the number of instances at runtime using commands like 'ALTER SERVICE MIN INSTANCES = s. Snowflake does handle load balancing across instances within the configured node counts. Option C is correct. The 'AUTO_RESUME = TRUE parameter, when specified during compute pool creation, enables the pool to automatically resume operation when a service or job is submitted, removing the need for explicit SALTER COMPUTE POOL RESUME commands. option D is incorrect. Setting = prevents the compute pool from automatically suspending, meaning it will continue to consume credits even when idle. This would generally lead to higher costs, not cost optimization, unless the pool is constantly active. The default is 3600 seconds (1 hour). SPCS Compute Nodes have a minimum charge of five minutes when started or resumed, making intelligent use of auto-suspend important for cost management. Option E is incorrect. Snowpark-optimized warehouses are a type of 'virtual warehouse' and are recommended for Snowpark workloads with large memory requirements or specific CPU architecture, typically for single-node ML training workloads 'within a warehouse'. SPCS compute pools, however, provide their own dedicated instance families (CPU, HighMemory, GPU) for containerized workloads, abstracting the underlying infrastructure and supporting distributed GPU clusters directly within SPCS, not Snowpark-optimized warehouses as a 'compute pool type' for SPCS.
NEW QUESTION # 162
A data engineering team is deploying Snowflake Cortex Analyst to enable natural language queries over their structured SALES_DATA table, which includes columns like PRODUCT_CATEGORY, SALES_AMOUNT, and ORDER_DATE. To maximize the accuracy and trustworthiness of responses for business users, which of the following practices should the team implement when configuring their semantic model?
Answer: A,C
Explanation:
Option A is correct because semantic models are defined in YAML and uploaded to a stage. When using VQR, logical table names in the SQL field must be prefixed with two underscores (e.g., sales_data), and logical column names are used directly. Option B is incorrect because 'Explore options' is a component of Cortex Agents for planning and disambiguation, not a feature within Cortex Analyst's semantic model configuration. Cortex Analyst uses semantic models to bridge language gaps but does not have an explicit 'Explore options' feature in this context. Option C is incorrect because VQR SQL queries must use the *logical* table and column names defined in the semantic model, not the physical names of the underlying dataset directly. Option D is incorrect. While exists, its purpose is to present a full set of predefined questions for onboarding, not to force prioritization for *all* complex questions regardless of semantic similarity to the user's input. This could lead to incorrect answers if the question isn't truly an onboarding question. Option E is correct because Cortex Analyst can integrate with Cortex Search Service within dimension definitions of the semantic model to improve literal string searches, which helps in cases where literal values cannot be directly extracted from the question.
NEW QUESTION # 163
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