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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q72-Q77):

NEW QUESTION # 72
A data engineering team is designing a Snowflake data pipeline to automatically enrich a 'customer issues' table with product names extracted from raw text-based 'issue_description' columns. They want to use a Snowflake Cortex function for this extraction and integrate it into a stream and task-based pipeline. Given the 'customer_issues' table with an 'issue_id' and (VARCHAR), which of the following SQL snippets correctly demonstrates the use of a Snowflake Cortex function for this data enrichment within a task, assuming is a stream on the 'customer issues' table?

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

Answer: E

Explanation:
Option B correctly uses to pull specific information (product name) from unstructured text, which is a common data enrichment task. It also integrates with a stream ('issue_stream') by filtering for 'METADATA$ACTION = 'INSERT" and uses a 'MERGE statement, which is suitable for incremental updates in a data pipeline by inserting new extracted data based on new records in the stream. Option A uses for generating a response, not for specific entity extraction, and its prompt is less precise for this task than 'EXTRACT_ANSWER. Option C uses 'SNOWFLAKE.CORTEX.CLASSIFY_TEXT for classification, not direct entity extraction of a product name, and attempts to update the source table directly, which is not ideal for adding new columns based on stream data. Option D proposes a stored procedure and task, which is a valid pipeline structure. However, the EXTRACT ANSWER call within the procedure only returns a result set and does not demonstrate the final insertion or merging step required to persist the extracted data into an 'enriched_issues' table. Option E uses to generate vector embeddings, which is a form of data enrichment, but the scenario specifically asks for 'product names' (a string value), not embeddings for similarity search.

NEW QUESTION # 73
A financial institution wants to automate the extraction of key entities (e.g., invoice number, total amount, list of invoice items) from incoming PDF financial statements into a structured JSON format within their Snowflake data pipeline. The extracted data must conform to a specified JSON schema for seamless downstream integration. Which Snowflake Cortex capabilities, when combined, can best achieve this data augmentation and ensure schema adherence in a continuous processing pipeline?

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

Answer: C,E

Explanation:

NEW QUESTION # 74
A development team is implementing a document retrieval system in Snowflake. They plan to store document embeddings and use VECTOR_L2_DISTANCE to find the most relevant documents for a given query embedding. Considering Snowflake's capabilities, which of the following statements are true regarding the use of vector types and VECTOR_L2_DISTANCE
? (Select all that apply)

A. O When defining a table column for 1024-dimensional float embeddings, the SQL type specification
B. VECTOR
C. Using the Snowpark Python library, developers can directly invoke
D. Document embeddings, which are typically float arrays, can be stored in a
E. To prevent issues with direct vector comparisons, explicitly using

Answer: A,C,E

Explanation:
Option A is incorrect. Vectors are explicitly not supported in
VARIANT
columns. Option B is correct. The
VECTOR
data type supports elements of type
FLOAT
and a dimension up to 4096. Therefore,
VECTOR(FLOAT, 1024)
is a valid type specification. Option C is correct. The Snowpark Python library supports the VECTOR data type and provides functions like vector_12_distance for distance calculations on DataFrame columns. Option D is incorrect. The VECTOR data type is not supported as a clustering key. Option E is correct. Direct vector comparisons using operators like

are byte-wise lexicographic and do not produce semantically expected results for numerical comparisons; dedicated vector similarity functions like VECTOR_L2_DISTANCE should be used instead.

NEW QUESTION # 75
An organization is planning to implement a new Retrieval Augmented Generation (RAG) application and has chosen Snowflake Cortex Search as its core retrieval engine. To effectively manage their budget, the finance and data teams need a clear understanding of the various cost components associated with deploying and operating a Cortex Search Service. Which of the following represent distinct cost categories directly attributable to the deployment and ongoing operation of a Snowflake Cortex Search Service?

A. Compute costs for LLM inference (e.g., SNOWFLAKE.CORTEX.COMPLETE) when the RAG application uses the retrieved context to generate responses.
B. Storage for the materialized source data and the optimized search index data structures within the Snowflake account.
C. ' Services compute specifically for generating vector embeddings of text data during the indexing and update processes.
D. Cloud Services compute for monitoring underlying base objects for changes to trigger search service refreshes.
E. Virtual warehouse compute used for refreshing the search service's index and processing base object changes.

Answer: B,C,D,E

Explanation:
Virtual warehouse compute is required for refreshing the search service, which includes running queries against base objects, orchestrating text embedding jobs, and building the search index. Storage costs are incurred for the materialized source query into a table and for storing the optimized data structures for low-latency serving (the search index), billed at a flat rate per TB. ' Services compute cost, specifically for EMBED_TEXT_TOKENS, is incurred for generating vector embeddings during the indexing and update process of the search service. Cloud services compute is used by Cortex Search Services to identify changes in underlying base objects, triggering refreshes of the search service. Option B is incorrect because the cost of LLM inference (e.g., using 'SNOWFLAKE.CORTEX.COMPLETE) to generate responses based on retrieved context is a separate cost from the Cortex Search Service itself; Cortex Search provides the context, but the LLM call then incurs its own cost.

NEW QUESTION # 76
A development team is building an AI-powered data pipeline in Snowflake. The pipeline involves extracting text from documents, generating embeddings using

,and then performing similarity searches using

to find related documents. They plan to manage this pipeline using Snowflake tasks and want to integrate with a Python application for some custom processing. Considering this scenario, which of the following statements about implementing this pipeline are true?

A. If the team wants to use the Snowpark Python library to call
B.
C. Snowflake
D. To generate document embeddings, the
E. When using Snowflake tasks to automate the embedding generation and similarity search,

Answer: B

Explanation:
Option A is incorrect. Snowflake recommends executing queries that call Cortex AI SQL functions like EMBED_TEXT_768 with a smaller warehouse (no larger than MEDIUM), as larger warehouses do not increase performance. Snowpark-optimized warehouses are recommended for workloads with large memory requirements or specific CPU architectures, typically for ML training, not for general Cortex AI function calls. Option B is incorrect. The Snowpark Python library explicitly states that it does not support the VECTOR_COSINE_SIMILARITY function, meaning it does not 'fully support all vector similarity functions'. Option C is incorrect. The VECTOR data type is not supported as clustering keys. Option D is correct. After generating embeddings (e.g., storing them in a VECTOR column like issue vec ), vector similarity functions can be effectively used in SQL queries with ORDER BY and LIMIT clauses to retrieve the most similar results, as demonstrated with VECTOR_COSINE_SIMILARITY in a RAG example. This pattem applies to VECTOR_INNER_PRODUCT as well. Option E is incorrect. The VECTOR data type is not supported for use with dynamic tables. Additionally, Snowflake Cortex functions (including EMBED_TEXT_768 ) do not support dynamic tables.

NEW QUESTION # 77
......

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