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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q178-Q183):

NEW QUESTION # 178
Considering Snowflake's Gen AI principles for cost governance within Snowflake Cortex, an ML engineer is assessing the expenditure for an LLM fine-tuning job. Which option correctly identifies how compute costs for Cortex Fine-tuning are primarily incurred and how fine-tuned models are treated regarding usage by other customers?

A. Costs are incurred per hour of compute pool usage, similar to virtual warehouses. Fine-tuned models are anonymized and used to train future foundation models for all customers.
B. Only inference using fine-tuned models incurs costs, not the training itself. Fine-tuned models can be openly shared on the Snowflake Marketplace.
C. Fine-tuning costs are a flat monthly fee, irrespective of token usage or model size. Fine-tuned models become part of Snowflake's proprietary models after training.
D. Compute costs for fine-tuning are based on the number of tokens used in training, calculated as 'number of input tokens number of epochs trained'. Fine-tuned models built using a customer's data are available exclusively for that customer's use.
E. Costs are based on the number of fine-tuning jobs created, not tokens. Fine-tuned models are shared across all Snowflake customers to improve the general service.

Answer: D

Explanation:
Snowflake Cortex Fine-tuning incurs compute cost based on the number of tokens used in training. Specifically, fine-tuning trained tokens are calculated as 'number of input tokens number of epochs trained'. Furthermore, fine-tuned models built using your data are available exclusively for your use and are not used to train, re-train, or fine-tune Models made available to others.

NEW QUESTION # 179
A company is building a chatbot for internal support, powered by Snowflake Cortex LLMs. The primary goals are to provide answers that are accurate, grounded in proprietary documentation, and to minimize factual 'hallucinations'. They are considering various strategies to achieve this. Which of the following statements correctly describe effective methods or tools within Snowflake for addressing these concerns?

A. AI Observability can be leveraged to systematically evaluate applications, measuring metrics like 'factual correctness and 'groundedness' to detect and mitigate hallucinations, especially in summarization.
B. For tasks requiring LLMs to generate SQL queries from natural language, using the Cortex Analyst verified Query Repository (VQR) can improve accuracy by leveraging pre-verified SQL queries for similar questions.
C. Deploying a custom fine-tuned model using SNOWFLAKE.CORTEX.FINETUNE on proprietary documentation is the most effective approach to ensure factual accuracy for any LLM task.
D. Using Cortex Search as a Retrieval Augmented Generation (RAG) engine can enhance LLM responses by providing relevant context from proprietary documentation, thereby reducing hallucinations.
E. Enabling Cortex Guard with guardrails: true directly addresses model hallucinations by ensuring responses are always factually correct and aligned with the provided context.

Answer: A,B,D

Explanation:
Option B is correct: Cortex Search is explicitly designed as a RAG engine to enhance LLM responses with contextualized information from Snowflake data, which directly addresses factual accuracy and reduces hallucinations. Option C is correct: AI Observability's evaluation features, including 'factual correctness and 'groundednes' scores, are specifically mentioned for detecting the truthfulness and relevance of responses based on retrieved context, and for avoiding LLMs with high hallucination frequencies, especially in summarization tasks. Option D is correct: The Cortex Analyst Verified Query Repository (VQR) provides a collection of pre-verified SQL queries for specific natural language questions, significantly improving the accuracy and trustworthiness of SQL generation and reducing errors that could be seen as 'hallucinations' in the text-to-SQL context. Option A is incorrect: While fine-tuning (using 'SNOWFLAKE.CORTEX.FINETUNE) can adapt a model to specific tasks and data, it is not a direct guarantee against 'all' factual inaccuracies or 'hallucinations' for 'any' LLM task, especially if the fine- tuning data itself is limited or the model generalizes poorly. RAG is generally preferred for grounding responses in up-to-date external knowledge. Option E is incorrect: Cortex Guard is designed to filter 'harmful or unsafe' LLM responses, not to directly ensure factual correctness or prevent hallucinations related to content accuracy or grounding.

NEW QUESTION # 180
An ML engineering team is preparing to log a custom Python model to the Snowflake Model Registry. This model has several Python package dependencies. The team wants to ensure the model can be deployed optimally, either in a Snowflake warehouse or to Snowpark Container Services (SPCS), depending on future needs. They are particularly concerned with how dependency specification impacts deployment eligibility. Which statements accurately describe how Snowflake handles model dependencies and determines deployment eligibility for custom Python models logged in the Model Registry, particularly when considering both Snowflake warehouse and Snowpark Container Services (SPCS) environments? (Select all that apply.)

A. The function will fail if 'WAREHOUSE is specified in 'target_platforms' but the model's size or GPU requirements make it ineligible for warehouse deployment.
B. If all of a model's 'conda_dependencieS are available in the Snowflake conda channel, the model is automatically deemed eligible to run in a warehouse.
C. Specifying both 'conda_dependencies' and 'pip_requirements' for a model is recommended to cover all possible deployment scenarios, and Snowflake's build process ensures compatibility between them.
D. For models intended for SPCS, 'pip_requirements' are always preferred over 'conda_dependencies' because SPCS strictly prohibits the use of any conda packages from 'conda-forge'
E. When 'conda_dependencies' are specified for a model to be deployed to SPCS, these dependencies are by default obtained from 'conda-forge' rather than the Snowflake conda channel.

Answer: A,B,E

Explanation:
Option A is correct. When a model version is logged using 'reg.log_moder , its 'conda_dependencies' are validated against the Snowflake conda channel. If all dependencies are found there, the model is considered eligible to run in a warehouse. Option B is incorrect. Snowpark Container Services models, by default, obtain their 'conda_dependencieS from 'conda-forge' . Therefore, SPCS does not prohibit conda packages from 'conda-forge' . Option C is correct. The Snowflake documentation explicitly states that for models running on Snowpark Container Services (SPCS), 'conda-forge' is the assumed channel for 'conda_dependencies' , while the Snowflake conda channel is for warehouse deployments only. Option D is correct. If the 'WAREHOUSE platform is specified in the 'target_platforms' argument of , and the model is ineligible for warehouse deployment (e.g., due to its size, dependencies, or GPU requirements), the call will fail. Option E is incorrect. Snowflake recommends using 'either' 'conda_dependencieS 'or' , but not both simultaneously. This is because combining both can lead to package conflicts, causing the container image to build successfully but potentially resulting in an unexpected or non-functional container image.

NEW QUESTION # 181
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.)

A. GPU-accelerated LLM training, which is a finite and often resource-intensive task, is best implemented as a ''job'' in SPCS, invoked via "EXECUTE JOB SERVICE', as it is designed to run to completion and then spin down.
B. Real-time LLM inference for a conversational interface is ideally deployed as a ' 'Service' ' in SPCS, which is long-running and accessible via an HTTP endpoint, ensuring continuous availability and responsiveness.
C. When deploying LLMs to SPCS, it's generally most cost-efficient to use generic CPU instance types like 'CPU X64 XS' for all tasks, as GPU instances (e.g., are exclusively for highly specialized computer vision tasks and not optimized for LLMs.
D. For batch inference on Snowflake data where data locality and efficiency are key, using ''Service Functions'' is highly efficient because data is passed as input parameters directly from SQL queries, and this design ensures the data never leaves the Snowflake network boundary.
E. Container images for SPCS deployments are typically pushed to a public Docker Hub repository, and Snowflake pulls them as needed during service creation and scaling, simplifying image management.

Answer: A,B,D

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 # 182
A financial institution is deploying a sentiment analysis application that uses Snowflake Cortex 'SENTIMENT' and 'COMPLETE' functions, with different LLMs, for processing customer feedback. They are using AI Observability (Public Preview) to compare the cost- efficiency of using 'mistral-7b' versus 'claude-3-5-sonnet' as LLM judges for evaluation metrics, and also tracking the overall cost of their AI Observability usage. Which statements accurately reflect the cost implications and monitoring tools for this scenario?

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

Answer: A,C,D

Explanation:
Option A is correct because AI Observability utilizes LLM judges (such as 'mistral-7b' or 'claude-3-5-sonnet') through 'COMPLETE (SNOWFLAKE.CORTEX)' function calls to compute evaluation metrics, and these calls incur charges based on the 'tokens processed'. Option D is correct as, beyond LLM judge costs, AI Observability also incurs warehouse charges for managing evaluation runs and for queries that compute evaluation metrics. Option E is correct because the view, with a filter for 'SERVICE _ TYPE ILIKE , provides a comprehensive daily credit usage report for all AI services, which would include AI Observability's components. Option B is incorrect; the view is specifically for Document AI processing functions like '!PREDICT and 'AI_EXTRACT , not for general LLM judge usage in AI Observability. The view is more appropriate for tracking individual Cortex function calls. Option C is incorrect because while prompt sizes might be similar, the pricing for different LLMs (e.g., 'mistral-7b' at 0.12 credits per million tokens vs. 'claude-3-5-sonnet' at 2.55 credits per million tokens for AI Complete) will still result in different billed amounts due to varying per-token costs, even if the number of tokens is the same.

NEW QUESTION # 183
......

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