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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A machine learning team is leveraging the Snowflake Model Registry to manage diverse models, including a custom Python utility for data preprocessing that they wish to make available as a model method. Which of the following statements accurately describe capabilities or considerations when logging models and their associated artifacts and methods in the Model Registry?

A) Option A
B) Option B
C) Option C
D) Option E
E) Option D

2. A data processing team is using Snowflake Document AI to extract data from incoming supplier invoices. They observe that many documents are failing to process, and successful extractions are taking longer than expected, leading to increased costs. Upon investigation, they find error messages such as

. Additionally, their 'X-LARGE virtual warehouse is constantly active, contributing to higher-than-anticipated bills. Which two of the following actions are essential steps to troubleshoot and address the root causes of these processing errors and optimize their Document AI pipeline?

A) Scale down the virtual warehouse to 'X-SMALL' or 'SMALL' size, as larger warehouses do not increase Document AI query processing speed and incur unnecessary costs.
B) Increase the 'max_tokens' parameter within the '!PREDICT' function options to accommodate longer document responses from the model.
C) Redefine extraction questions to be more generic and encompassing, reducing the number of distinct questions needed per document.
D) Implement a pre-processing step to split documents exceeding 125 pages or 50 MB into smaller, compliant files before loading to the stage.
E) Configure the internal stage used for storing invoices with 'ENCRYPTION = (TYPE = 'SNOWFLAKE SSE')'.

3. A financial analytics team is using AI_COMPLETE to extract specific financial metrics (e.g., revenue, profit margin) from quarterly reports and requires the output in a strict JSON format for automated ingestion into a data warehouse. They've encountered issues where the LLM sometimes generates malformed JSON or includes extraneous text. Which of the following approaches will help ensure deterministic, schema-compliant JSON outputs and mitigate these 'hallucinations' related to format?

A)

B)

C) Set the 'temperature' option to 0 when calling 'AI_COMPLETE' to obtain the most consistent and deterministic JSON outputs.
D)

E) Include a prompt instruction such as 'Respond in JSON' to improve adherence, especially for complex tasks, even though 'response_format' is provided.

4. 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) Limit the fine-tuning training data exclusively to perfectly formatted and clean documents to ensure the model learns from ideal examples without noise.
B) Ensure the training dataset used for fine-tuning includes diverse documents representing various layouts, data variations, and explicit examples of 'NULL' values or empty cells where appropriate.
C) 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.
D) Prioritize extensive prompt engineering by creating highly detailed and complex questions with intricate logic to guide the LLM's understanding of the extraction task.
E) 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.

5. A data platform architect is integrating 'SNOWFLAKE.CORTEX.EMBED TEXT 768' into a complex data pipeline for a new search application. The pipeline involves extracting text from various sources, generating embeddings, storing them in Snowflake, and performing semantic searches. Which of the following statements accurately describes a compatibility aspect or limitation when working with 'EMBED TEXT 768' and the resulting 'VECTOR' data type within Snowflake?

A) If the function is not natively available in the account's primary Snowflake region, cross-region inference cannot be enabled, thus preventing its use.
B) To support diverse embedding dimensions from different models, the 'VECTOR data type can be stored efficiently within a 'VARIANT column, which automatically handles schema variations.
C) The 'VECTOR' data type, which stores the output of is fully compatible with all Snowflake features, including being used as a primary key in hybrid tables for fast lookups.
D) The function can be directly integrated into a dynamic table's 'SELECT statement to provide continuous, automated embedding updates for new data.
E) When is invoked within a Snowpark Python User-Defined Function (UDF) on Snowflake data, the data remains within Snowflake's network boundary during the embedding generation process.

Solutions: