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[General] 100% Pass Quiz Snowflake - GES-C01 - Efficient Cert SnowPro® Specialty: Gen

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【General】 100% Pass Quiz Snowflake - GES-C01 - Efficient Cert SnowPro® Specialty: Gen

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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q80-Q85):NEW QUESTION # 80
A data science team is fine-tuning a mistral-lb model within Snowflake Cortex using proprietary customer interaction logs. Which of the following principles and practices apply to this fine-tuning process concerning data privacy, model ownership, and subsequent inference?
  • A. The fine-tuned model, which is a CORTEX_FINETUNED type, can be shared with other Snowflake accounts using secure data sharing.
  • B. The resulting fine-tuned model is exclusively available to the data science team and cannot be accessed by other Snowflake customers.
  • C. The proprietary customer interaction logs used for fine-tuning are leveraged by Snowflake to improve the base mistral -7b model for all customers.
  • D. The fine-tuned model is fully managed by the Snowflake Model Registry API, allowing programmatic management of its lifecycle.
  • E. The fine-tuning process occurs entirely within Snowflake's security and governance boundaries, ensuring the data never leaves the Snowflake environment.
Answer: A,B,E
Explanation:
Cortex Fine-tuning is a fully managed service that lets you fine-tune popular LLMs using your data, all within Snowflake. Your Usage and Customer Data (including inputs and outputs) are NOT used to train, re-train, or fine-tune Models made available to others. Fine-tuned Models built using your data can only be used by you. Therefore, Option A is incorrect. Options B and C are correct, as the fine-tuned model is exclusive to the customer, and the process is managed within Snowflake's boundaries. Option D is also correct because models generated with Cortex Fine- tuning (CORTEX_FINETUNED type) can be shared using Data Sharing. Option E is incorrect because Cortex Fine-Tuned LLMs appear in the model registry's Snowsight UI, but are not managed by the model registry API.

NEW QUESTION # 81
A Snowflake user attempts to access the Cortex Playground to experiment with LLM functions but receives an error indicating insufficient privileges. They have an active Snowflake session. Which of the following steps must be taken to ensure they can successfully use the Cortex Playground?

  • A. Option C
  • B. Option A
  • C. Option B
  • D. Option D
  • E. Option E
Answer: B,D
Explanation:


NEW QUESTION # 82
A development team is preparing to deploy a new Retrieval-Augmented Generation (RAG) application written in Python. They intend to use Snowflake AI Observability to capture detailed logs and traces for debugging and performance analysis. Which of the following configurations are essential prerequisites for enabling this logging capability effectively?

  • A. Option D
  • B. Option C
  • C. Option A
  • D. Option E
  • E. Option B
Answer: B,C,D,E
Explanation:


NEW QUESTION # 83
A Gen AI Specialist is setting up their Snowflake environment to deploy a high-performance open-source LLM for real-time inference using Snowpark Container Services (SPCS). They need to create a compute pool that can leverage NVIDIAAIOG GPUs to optimize model performance. Which of the following SQL statements correctly creates a compute pool capable of supporting an intensive GPU usage scenario, such as serving LLMs, while adhering to common configuration best practices for a new, small-scale deployment in Snowpark Container Services?
  • A.
  • B.
  • C.
  • D.
  • E.
Answer: B
Explanation:
Option D is correct. The 'GPU NV_M' instance family is explicitly described as "Optimized for intensive GPU usage scenarios like Computer Vision or LLMsNLMs", providing 4 NVIDIAAIOG GPUs. Setting = 1' and 'MAX_NODES = 1' is appropriate for a small- scale deployment, and = 1800' (30 minutes) is a sound practice for cost management during inactivity. Option A is incorrect because is a generic CPU instance, not a GPU instance suitable for LLMs. Option B uses which is a GPU instance and the "smallest NVIDIA GPU size available for Snowpark Containers to get started". While functional, ' GPU is more directly aligned with "intensive GPU usage scenarios like LLMs" as stated in the question. 'AUTO RESUME = TRUE is the default behavior. Option C is incorrect because is a high-memory CPU instance, not a GPU instance. Setting = 0' means the compute pool will not suspend automatically, which is generally not a best practice for a new, small-scale deployment unless continuous availability is strictly required. Option E uses which is a CPU instance, making it unsuitable for GPU-accelerated workloads.

NEW QUESTION # 84
A data science team is fine-tuning a Snowflake Document AI model to improve the extraction accuracy of specific fields from a new type of complex legal document. They are consistently observing low confidence scores and inconsistent 'value' keys for extracted entities, even after initial training. Which two of the following best practices should the team follow to most effectively improve the model's extraction accuracy and confidence for this complex document type?
  • A. Prioritize extensive prompt engineering by creating highly detailed and complex questions with intricate logic to guide the LLM's understanding of the extraction task.
  • B. Set the 'temperature' parameter to a higher value (e.g., 0.7) during '!PREDICT calls to encourage more creative and diverse interpretations by the model.
  • C. Limit the fine-tuning training data exclusively to perfectly formatted and clean documents to ensure the model learns from ideal examples without noise.
  • D. Ensure the training dataset used for fine-tuning includes diverse documents representing various layouts, data variations, and explicit examples of values or empty cells where appropriate.
  • E. Actively involve subject matter experts (SMEs) or document owners throughout the iterative process to help define data values, provide annotations, and evaluate the model's effectiveness.
Answer: D,E
Explanation:
To improve Document AI model training, it is crucial to ensure that the documents uploaded for training represent a real use case and that the dataset consists of diverse documents in terms of both layout and data. If all documents contain the same data or are always presented in the same form, the model might provide incorrect results. For table extraction, it is vital that enough data is used to train the model to include ' NULC values and maintain order. Therefore, ensuring a diverse training dataset (Option B) is a key best practice. Additionally, Subject Matter Experts (SMEs) and document owners are crucial partners in understanding and evaluating the model's effectiveness in extracting the required information. Their involvement in defining data values, providing annotations, and evaluating results will significantly improve accuracy (Option C). Option A is not a best practice; it's recommended to keep questions as encompassing as possible and rely on training with annotations rather than complex prompt engineering, especially for document variability. Option D is incorrect; a higher 'temperature' value increases the randomness and diversity of the model's output, which is generally undesirable for accurate data extraction where deterministic results are preferred. For most consistent results, 'temperature' should be set to 0. Option E is incorrect because training on a restricted set of perfectly formatted documents can lead to a model that performs poorly on real-world, varied documents; diversity in training data is essential.

NEW QUESTION # 85
......
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