Valid Snowflake GES-C01 Exam Tips - Dump GES-C01 Collection

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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q248-Q253):

NEW QUESTION # 248
A data engineering team is implementing a solution using Snowflake Cortex's AI_COMPLETE function to process customer support tickets. They are concerned about sensitive information and ensuring the model's responses are safe, while adhering to Snowflake's data governance principles. Which of the following statements correctly describe the functionality of Cortex Guard and Snowflake's data privacy commitments in this context?

Answer: A,E

Explanation:
Option B is correct because Cortex Guard evaluates the LLM's responses, not just the prompts, to filter unsafe content. Additionally, only the input tokens for Cortex Guard are counted for billing, and this cost is in addition to the function's cost. Option E is correct because Snowflake's trust and safety principles explicitly state that your Usage and Customer Data (including inputs and outputs of Snowflake AI Features) are NOT available to other customers and are NOT used to train, re-train, or fine-tune Models made available to others. Option A is incorrect as customer data is not anonymised and used to improve general model performance for others. Option C is incorrect as the sources do not mention fine-tuning a customer-owned guardrail model. Option D is incorrect as the sources do not mention a 'CORTEX GUARD ADMIN' role or logging of filtered content to a customer-accessible audit table.


NEW QUESTION # 249
A data scientist is preparing to log a custom PyCaret classification model into the Snowflake Model Registry. The goal is to deploy this model on Snowpark Container Services (SPCS) for scalable inference. The PyCaret model relies on the 'pycaret' and 'scipy' Python libraries, and the data scientist has local 'sample data.csv' for inferring the model's signature. Which statements are crucial for successfully logging this custom model for eventual SPCS deployment?

Answer: D,E

Explanation:
Option B is correct because for models deployed to Snowpark Container Services, dependencies are typically obtained from 'conda- forge' or PyPl. Therefore, 'pip_requirements' is the appropriate way to specify PyPl packages, and explicitly setting ['SNOWPARK_CONTAINER_SERVICEST guides the deployment target. Option C is correct because either (a Pandas or Snowpark DataFrame) or a model 'signature' must be provided to the method for input validation and to infer the model's input signature. Option A is incorrect because 'conda_dependencieS in 'log_model' assumes the Snowflake channel for warehouse deployment, whereas for SPCS, it's 'conda-forge' or PyPl. Mixing channels or assuming Snowflake channel for SPCS is incorrect. Option D is incorrect. The 'use_gpu" option is used when 'loading' a model version Cmv.load(options={'use_gpu': to enable GPU-specific loading logic, not when logging the model. GPU requests for inference are specified when creating the service. Option E is incorrect. Snowflake recommends using only 'conda_dependencieS or 'pip_requirements' , not both, to avoid potential compatibility issues during the container image build.


NEW QUESTION # 250
A developer is refining a Document AI extraction process using the '!PREDICT' method and is meticulously examining the JSON output for invoices, which include 'invoice number', 'invoice items', 'tax amount', and 'vendor name'. They also have a detailed internal table of 'product details' to be extracted. To ensure optimal data quality and accurate interpretation of the extracted information, which of the following best practices or characteristics of Document AI's output should the developer consider?

Answer: D,E

Explanation:
Option A is incorrect. The 'ocrScore' in the '_documentMetadata' field specifies the confidence score for the optical character recognition (OCR) 'process' for that document, not the confidence of specific extracted values. The 'score' field associated with individual extracted values indicates confidence for that specific value. Option B is correct. Document AI models can return lists, and the 'invoice_itemS field is given as an example. The JSON format for 'invoice_items' shows an array of objects for multiple items. The order is inherently maintained in such list extractions. Option C is correct. The sources explicitly state that in table extraction, the values in the JSON output are provided in the same order as the rows in the table, which allows columns to be easily joined. This ensures the structural integrity of the extracted table data. Option D is incorrect. For question optimization, it is crucial to be specific and precise. The guidelines advise against asking generic questions like 'What is the date?' without including more details, especially when multiple similar values might be present, as Document AI is not expected to guess intentions or have extended domain knowledge. Option E is incorrect. If the Document AI model does not find an answer (such as it does not return a 'value' key at all within that field, although it does return the 'score' key to indicate its confidence that the answer is not present.


NEW QUESTION # 251
A security architect is configuring access controls for a new custom role, 'document_processor_role' , which will manage Document AI operations within a designated database 'doc_processing_db' and schema 'doc_workflow_schema'. The goal is to grant only the minimum essential database-level role required to begin working with Document AI features.

Answer: E

Explanation:
To work with Document AI, the database role must be granted to the account role. This role specifically enables creating Document AI model builds and working on document processing pipelines. Option A grants a more general Cortex user role, which is not the specific foundational role for Document AI. Option B grants access to all Cortex models, but not the foundational Document AI database role itself. Options D and E grant schema-level or warehouse-level privileges, which are also necessary but are not the database-level 'role' specifically for DocumentAI capabilities.


NEW QUESTION # 252
A data engineering team is planning to build a real-time data pipeline using Snowflake's dynamic tables to process incoming log dat a. They want to use SNOWFLAKE. CORTEX. EXTRACT_ANSWER to pull out specific error codes and timestamps from log entries. They are also mindful of the operational costs. Which of the following statements accurately describes limitations or cost considerations for using SNOWFLAKE . CORTEX. EXTRACT_ANSWER in this scenario?

Answer: C,D

Explanation:
Option A is correct because Snowflake Cortex functions, including , do not support dynamic tables. Option B is correct because for EXTRACT_ANSWER, the number of billable tokens is the sum of the number of tokens in the (source document) and 'question' fields. Option C is incorrect; Snowflake recommends executing queries that call Cortex AISQL functions with a smaller warehouse (no larger than MEDIUM) as larger warehouses do not increase performance. Option D is incorrect; inputs that exceed the model's token limit result in an error, rather than automatic truncation. Option E is incorrect; while the newer 'AI_EXTRACT supports multiple languages, the documentation for "EXTRACT_ANSWER does not explicitly state multi-language support and generally refers to plain English text. Costs are incurred per token regardless of language.


NEW QUESTION # 253
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