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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q112-Q117):

NEW QUESTION # 112
A security-conscious data scientist in an Azure East US 2 (Virginia) account wants to fine-tune a mistral-7b model for a specific text summarization task and then deploy it for real-time inference using the Cortex REST API. The base model is natively mistral -7b available for fine-tuning in Azure East US 2 (Virginia). For subsequent inference using the fine-tuned model, they need to understand the regional and cross-region inference considerations. Which of the following statements are correct?

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

Answer: B,C,D,E

Explanation:

NEW QUESTION # 113
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?

A. The 'ocrScore' provided in the '_documentMetadata' object for each document indicates the model's confidence in the content of specific extracted values, rather than the overall quality of the optical character recognition process.
B. If the 'vendor_name' field cannot be confidently identified in a document, the model will include '"vendor_name": [ { "score": O.X, "value": "NOT FOUND" } l' in the JSON output.
C. When extracting lists of values, such as 'invoice_items', the Document AI model returns them as an array in the JSON output, preserving the original order of items as they appear in the document.
D. To maximize accuracy when defining data values, questions should be broadly generic (e.g., 'What is the amount?) to allow the Document AI model to infer the most relevant context, especially for fields like 'tax_amount' where multiple numbers might be present.
E. For table extraction, such as the extracted values for each column (e.g., 'tablel litem', 'tablel Igross) are ordered consistently with the rows of the original table, facilitating direct joining of columns.

Answer: C,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 # 114
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 fine-tuning process occurs entirely within Snowflake's security and governance boundaries, ensuring the data never leaves the Snowflake environment.
C. The fine-tuned model is fully managed by the Snowflake Model Registry API, allowing programmatic management of its lifecycle.
D. The resulting fine-tuned model is exclusively available to the data science team and cannot be accessed by other Snowflake customers.
E. The proprietary customer interaction logs used for fine-tuning are leveraged by Snowflake to improve the base mistral -7b model for all customers.

Answer: A,B,D

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 # 115
A developer is building a conversational application using Snowflake Cortex Analyst and is interacting with it via the REST API. The application needs to support multi-turn conversations where users can ask follow-up questions. Which of the following statements accurately describe how to maintain conversational state and interact with the Cortex Analyst REST API for a multi-turn experience?

A. A special 'RESET_CONVERSATION' API endpoint should be called periodically to clear the LLM's internal context and manage token consumption for long conversations.
B. The Cortex Analyst REST API automatically retains the full conversation history for each user session, eliminating the need to explicitly pass previous messages.
C. The argument, when using the 'COMPLETE function (or its API equivalent), must be a string for single-turn conversations but an array of objects for multi-turn conversations, with each object containing 'role' and 'content'.
D. For performance optimization, only the most recent user prompt and the immediate preceding analyst response should be passed in the 'messages' array for follow-up questions.
E. To maintain state, the application must construct a 'messages' array for each new prompt, including all prior 'user' prompts and 'analyst' responses in chronological order, and send it with every API call.

Answer: C,E

Explanation:
Option B is correct. Cortex Analyst (and the underlying COMPLETE function) does not retain state from one call to the next. To provide a stateful conversational experience, the application must explicitly pass all previous user prompts and model (analyst) responses in the conversation as part of the 'messages' (or array for each new request. Option C is also correct. The argument for the 'COMPLETE function (and its REST API equivalent) accepts a string for single-turn prompts, but an array of objects for multi- turn conversations, where each object has a 'role' (e.g., 'user', 'assistant', 'system') and 'content'. Option A is incorrect because Cortex Analyst functions do not retain state; the conversation history must be explicitly managed by the client application. Option D is incorrect; while sources suggest resetting a long conversation if interpretation struggles, there is no command mentioned for cost optimization or clearing internal LLM context. Cost is managed by token usage, which increases with conversation length. Option E is incorrect. To maintain full conversational context, previous user prompts and model responses should be passed, not just the most recent ones. Passing only a partial history would lead to loss of context.

NEW QUESTION # 116

Answer: D

Explanation:

NEW QUESTION # 117
......

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New GES-C01 Braindumps Sheet - GES-C01 Reliable Test Preparation

Wiki Article

Snowflake Certification GES-C01 certkingdom exam torrent & GES-C01 practice dumps

Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q112-Q117):

Trusted New GES-C01 Braindumps Sheet & Realistic GES-C01 Reliable Test Preparation & Valid Snowflake SnowPro® Specialty: Gen AI Certification Exam

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