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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q175-Q180):

NEW QUESTION # 175
An administrator has configured the 'CORTEX MODELS ALLOWLIST parameter to only permit the 'mistral-large? model at the account level. A user with the role, which has been granted 'SNOWFLAKE.CORTEX USER and 'SNOWFLAKE."CORTEX- MODEL-ROLE-LLAMA3.1-70B"', attempts to execute several queries. Which of the following queries will successfully execute?

Answer: A,C

Explanation:
Option A is correct because the 'MISTRAL-LARGE2' model is explicitly included in the account-level , allowing the 'AI_COMPLETE' function to be used with it. Option B is correct because the user's 'PUBLIC' role has been granted the application role , which provides access to the 'LLAMA3.1- 70B' model object in 'SNOWFLAKE.MODELS'. When a model name is provided as a string argument, Cortex first checks if it's an identifier for a schema-level model object and applies RBAC, overriding the allowlist for that specific object. Option C is incorrect because 'llama3.1-70b' as a plain model name is not in the 'CORTEX MODELS ALLOWLIST' , and while the user has RBAC access to the model 'object' , a plain string name will be matched against the allowlist if it fails to match a model object by that plain name. Option D is incorrect as 'snowflake-arctic' is neither in the 'CORTEX MODELS ALLOWLIST nor does the user have a specific application role for it, causing the query to fail. Option E is incorrect because 'ALTER ACCOUNT operations, such as modifying , require the 'ACCOUNTADMIN' role, not the SPUBLIC' role.

NEW QUESTION # 176
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 B
B. Option D
C. Option E
D. Option C
E. Option A

Answer: A,B,C,D

Explanation:

NEW QUESTION # 177
A financial institution needs to process thousands of incoming PDF loan application forms daily, extracting applicant names, loan amounts, and submission dates, and loading them into a Snowflake table. They aim for continuous processing with minimal manual intervention. Which of the following statements correctly describe how Document AI can be used in an automated SQL pipeline for this purpose?

A. The SNOWFLAKE .DOCUMENT_INTELLIGENCE_CREATOR database role alone is sufficient for defining the model build and configuring the processing pipeline, without needing additional CREATE MODEL privileges on the schema.
B. The pipeline can leverage the <model build name> ! PREDICT method within a CREATE TASK statement to automatically process new PDFs as they arrive in an internal or external stage, once the Document AI model build is published.
C. The extracted information, including confidence scores and values, is returned as a JSON object, which can then be parsed into separate columns in a Snowflake table using SQL functions like LATERAL FLATTEN.
D. To ensure continuous data ingestion and processing, a STREAM can be created on the stage to detect new PDF documents, triggering the TASK for extraction and subsequent loading into a Snowflake table.
E. Document AI's PREDICT method natively supports all PDF files up to 500 MB and 500 pages, allowing for large-scale, single-query processing without requiring users to split documents into smaller chunks.

Answer: B,C,D

Explanation:
Option A is correct because DocumentAI supports creating automated pipelines with tasks that call the method to extract information from documents in a stage. Option B is correct as streams are used to detect new data (e.g., PDFs) in a stage, and tasks can be set up to execute when new data is available in the stream, enabling continuous processing. Option E is correct because the 'PREDICT method returns its results as a JSON object, which typically contains 'score' and 'value' fields for extracted entities, and this JSON output can be parsed into separate columns using 'LATERAL FLATTEN'. Option C is incorrect as, in addition to the 'SNOWFLAKE.DOCUMENT_INTELLIGENCE_CREATOR database role, the role used must also have 'CREATE SNOWFLAKE.ML.DOCUMENT_INTELLIGENCE and 'CREATE MODEL' privileges on the schema where the model build is located. Option D is incorrect because DocumentAI has specific limitations on document size (max 50 MB) and page count (max 125 pages per document), and also limits processing to a maximum of 1000 documents in one query.

NEW QUESTION # 178
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: C,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 # 179
A data scientist wants to fine-tune a
mistral -7b
model to improve its ability to generate specific product descriptions based on brief input features. They have a table named PRODUCT_CATALOG with columns PRODUCT_FEATURES (text) and GENERATED_DESCRIPTION (text). Which of the following statements correctly describe the preparation and initiation of this fine-tuning job in Snowflake Cortex?
(Select all that apply)

A. Once a fine-tuned model is created, it is fully managed by the Snowflake Model Registry API, allowing for programmatic updates to its parameters and versions.
B. O To generate highly structured
C. The fine-tuning job must be created using a
D. The SQL query for the training data must select columns aliased as
E. The

Answer: B,D

Explanation:
Option A is incorrect. While prompt engineering with system roles is crucial for general COMPLETE function calls, the FINETUNE training data query only requires prompt and completion columns. It does not explicitly require system role messages within the training data format itself. Option B is correct. The FINETUNE function's required arguments for training data specify that the SQL query must return columns named prompt and completion
. Aliases can be used if the source columns have different names. Option C is incorrect.
FINETUNE
is a SQL function invoked with the string 'CREATE' as its first argument, e.g., SELECT SNOWFLAKE-CORTEX. FINETUNE( ' CREATE',...)
. It is not a DDL
CREATE SNOWFLAKE .ML. FINETUNE
statement, unlike
ANOMALY DETECTION
models which are created as objects using
CREATE SNOWFLAKE .ML.ANOMALY DETECTION
Option D is correct. Structured outputs from the
AI COMPLETE
function, by specifying a JSON schema with the
response_format
argument, can be used to generate high-quality, schema-conforming data. This is an effective way to prepare the prompt and completion columns needed for fine-tuning, especially when specific output formats (like JSON) are desired. Option E is incorrect. While Cortex Fine-tuned LLMs do appear in the Model Registry UI, they are explicitly stated as not being managed by the Model Registry API directly. Models that contain user code, such as those developed with Snowpark ML modeling classes, are managed by the Model Registry API.

NEW QUESTION # 180
......

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