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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q97-Q102):

NEW QUESTION # 97
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?

Answer: A,B,C,D

Explanation:


NEW QUESTION # 98
A Gen AI specialist is tasked with creating a Snowflake Cortex Search Service to power a Retrieval Augmented Generation (RAG) application for customer support transcripts. The goal is to allow semantic search over the 'transcript_text' column, filter results by 'region' and , and leverage a multilingual embedding model for high-quality results. The service should be created in the 'cortex_search_db.serviceS schema and use as the warehouse. Which of the following SQL commands correctly creates such a Cortex Search Service, assuming 'support_transcripts' is the source table and change tracking is enabled?

Answer: D

Explanation:


NEW QUESTION # 99
An organization is building a new knowledge base system within Snowflake, which relies on 'SNOWFLAKE.CORTEX.EMBED_TEXT_1024' to generate and store embeddings for documents in a 'VECTOR(FLOAT, 1024)' column. They plan to use these embeddings for semantic search and integrate them into various data processing workflows. Which of the following statements accurately describe limitations or specific compatibility aspects of 'EMBED TEXT 1024' or the 'VECTOR' data type within Snowflake?

Answer: B,E

Explanation:
Option C is correct. The 'VECTOR data type, which stores the output of ' , is explicitly not supported in 'VARIANT columns. This means embeddings cannot be stored directly within a 'VARIANT column. Option E is correct. When invoking the EMBED' function via the Cortex REST API, the 'text' argument accepts a list of text strings, and each string in that list can be up to 4096 characters long. Option A is incorrect because while 'VECTOR data types are allowed in hybrid tables, they are not supported as primary keys or secondary index keys. Option B is incorrect because Snowflake Cortex functions, including , do not support dynamic tables. Option D is incorrect because the Snowpark Python library does 'not' support the 'VECTOR_COSINE SIMILARITY function. It does, however, support , and SQL supports multiple vector similarity functions including


NEW QUESTION # 100
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?

Answer: A,D

Explanation:
The error messages 'Document has too many pages. Actual: 130. Maximum: 125.' and File exceeds maximum size. Actual: 54096026 bytes. Maximum: 50000000 bytes.' directly indicate that the documents do not meet Document AI's input requirements, which specify a maximum of 125 pages and 50 MB file size. Therefore, implementing a pre-processing step to split or resize these documents is an essential solution (Option B). The error 'cannot identify image file <_io.Bytesl0 object at Ox...>' is a known issue that occurs when an internal stage used for Document AI is not configured with 'SNOWFLAKE_SSE encryption. Correctly configuring the stage with this encryption type is crucial for resolving this processing error (Option D). Option A, while addressing cost optimization, is not a root cause of the 'processing errors' themselves, although it is a best practice for cost governance as larger warehouses do not increase Document AI query processing speed. Option C is incorrect; best practices for question optimization suggest being specific, not generic. Option E is incorrect as 'max_tokenS relates to the length of the model's output, not the input document's size or page limits.


NEW QUESTION # 101
A machine learning engineering team is evaluating two different configurations of a Retrieval Augmented Generation (RAG) application. uses for generation, while uses 'mistral-7b' with a refined prompt for the same task. They aim to compare the and 'groundedness' of the generated responses, as well as the efficiency of context retrieval. Which of the following steps are crucial for setting up AI Observability in Snowflake to facilitate a meaningful side-by-side comparison and assess these specific metrics?

Answer: A,B,D

Explanation:
Option A is correct because instrumenting the 'generate_answer' function with the 'GENERATION' span type is essential for correctly capturing and evaluating the LLM's output for metrics like 'answer_relevance' and 'groundednesS. Registering them as distinct ' TruApp' versions or runs allows for side-by-side comparisons. Option C is correct because instrumenting the retrieval component with 'RETRIEVAL' span type enables the calculation of 'context_relevance' , which directly assesses the quality of the search results and is crucial for RAG evaluation. Option D is correct as creating separate runs with specific configurations (like ' and explicitly computing desired metrics such as 'answer_relevance' and 'groundedness' is the standard way to set up systematic evaluations and comparisons in AI Observability. Option B is incorrect; while cross-region inference might be necessary for model availability, it doesn't directly enable the comparison 'feature' within AI Observability. Option E is incorrect because 'prompt_tokens' and 'completion_tokens' track cost, not directly the quality aspects like 'answer_relevance' and 'groundednesS , which are key for RAG performance evaluation.


NEW QUESTION # 102
......

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