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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q168-Q173):

NEW QUESTION # 168
A Gen AI specialist is preparing to upload a large volume of diverse documents to an internal stage for Document AI processing. The objective is to extract detailed information, including lists of items and potentially classifying document types, and then automate this process. Which of the following statements represent 'best practices or important considerations/limitations' when preparing documents and setting up the Document AI workflow in Snowflake? (Select ALL that apply.)

A. Documents with a page count exceeding 125 pages or a file size greater than 50 MB will be processed, but with a potential reduction in extraction accuracy.
B. When defining data values for extraction, especially for nonstandard formats or combinations of values, fine-tuning the model with annotations is generally more effective than relying solely on complex prompt engineering.
C. To improve model training, documents uploaded should represent a real use case, and the dataset should consist of diverse documents in terms of both layout and data.
D. If the Document AI model does not find an answer for a specific field, the '!PREDICT method will omit the 'value' key but will still return a 'score' key to indicate confidence that the answer is not present.
E. For continuous processing of new documents, it is best practice to create a stream on the internal stage and a task to automate the '!PREDICT method execution.

Answer: B,C,D,E

Explanation:

NEW QUESTION # 169
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. Using the Snowpark Python library, developers can directly invoke
B. Document embeddings, which are typically float arrays, can be stored in a
C. VECTOR
D. To prevent issues with direct vector comparisons, explicitly using
E. O When defining a table column for 1024-dimensional float embeddings, the SQL type specification

Answer: A,D,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 # 170
A data scientist is working on a new feature that involves querying a Cortex Search Service and integrating the results into various downstream processes. They are concerned about potential data type compatibility issues and limitations within the Snowflake environment. Which of the following statements accurately describe how to query a Cortex Search Service or the limitations of the 'VECTOR data type and Cortex Search itself? (Select all that apply)

A. To retrieve the support ticket most relevant to a query about 'internet issues', filtered to return results only in the 'North America' region, one can use the 'filter' parameter in the 'SEARCH PREVIEW function's JSON argument like so:
B. Cortex Search Services can only be queried using the 'SNOWFLAKE.CORTEX.SEARCH_PREVIEW' SQL function and do not offer a programmatic interface for applications.
C. Cortex Search Services can be configured as a source for Snowflake dynamic tables, enabling continuous and automated synchronization of the search index with base data.
D. The 'VECTOR data type, which stores the output of embedding models like those used by Cortex Search, is explicitly not supported in 'VARIANT columns.
E. The "VECTOR data type, used to store embeddings generated for Cortex Search, is fully supported as a primary key in Snowflake's hybrid tables to accelerate similarity searches.

Answer: A,D

Explanation:
Option A is incorrect. Cortex Search Services can be queried using both the 'SNOWFLAKE.CORTEX.SEARCH PREVIEW' SQL function and programmatically via the Python API. Option B is incorrect. While the 'VECTOR data type is allowed in hybrid tables, it is not supported as a primary key or secondary index key. Option C is correct. The 'SNOWFLAKE.CORTEX.SEARCH_PREVIEW' function accepts a JSON object as an argument, which can include a 'filter' parameter to refine search results based on specified conditions, such as filtering by region. Option D is correct. The 'VECTOR data type is explicitly not supported in 'VARIANT columns, meaning embeddings cannot be stored directly within semi-structured data in a single 'VARIANT column. Option E is incorrect. Snowflake Cortex functions, including Cortex Search, do not support dynamic tables.

NEW QUESTION # 171

A. If a user attempts to call an LLM not explicitly listed in the via the Cortex LLM Playground, Snowflake will automatically fall back to the 'snowflake-arctic' model for that request.
B. Users with 'ds_team_role' will still be able to successfully call
C. The 'ACCOUNTADMIN' must execute the SQL command:
D. Enabling Cortex Guard for 'AI_COMPLETE through the 'guardrails' option automatically bypasses the 'CORTEX_MODELS_ALLOWLIST to ensure all LLMs can be used for content safety filtering.
E. The parameter provides granular control at the database or schema level, allowing administrators to define different sets of approved models for different data contexts.

Answer: B,C

Explanation:

NEW QUESTION # 172
A Data Engineer is responsible for deploying machine learning models using Snowpark Container Services. They need to ensure that a specific role, model_deployer_role, has the appropriate permissions to create a Snowpark Container Service that uses an image from an existing image repository named my_inferenc_ images. Which of the following SQL commands grant the necessary privileges 'on the image repository' for this purpose?

Answer: A,D

Explanation:
Option D is correct because the privilege allows a role to read images from the repository, which is READ ON IMAGE REPOSITORY fundamental for creating a service that uses those images. Option E is correct because the privilege is SERVICE READ ON IMAGE REPOSITORY specifically identified as being for services that need to use images from the repository. Option A is incorrect as is a general USAGE ON DATABASE privilege for database access, not a specific privilege on the image repository for using its images. Option B is incorrect because CREATE SERVICE ON is required to create the service object in a schema, but it is a privilege on the 'schema' itself, not directly on the 'image repository' for SCHEMA accessing its content. Option C is incorrect because is used for pushing or uploading images to the repository, not for WRITE ON IPtAGE REPOSITORY consuming them when creating a service.

NEW QUESTION # 173
......

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