Snowflake GES-C01 - SnowPro® Specialty: Gen AI Certification Exam

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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1.
After resolving this, they try to process a batch of 1500 documents in a single query using the method, which also fails. Which two issues are most likely contributing to these failures?

A) The has not been granted the 'CREATE STREAM' privilege on the
B) The virtual warehouse being used for Document AI is not a Snowpark-optimized warehouse.
C) The batch of documents exceeds Document AI's limit of 1000 documents per query.
D) The 'SNOWFLAKE.DOCUMENT INTELLIGENCE CREATOR database role or the 'CREATE SNOWFLAKE.ML.DOCUMENT INTELLIGENCE' privilege on the schema has not been granted to the .
E) The name is not unique within the .

2. A Gen AI developer is implementing a Document AI solution to extract key fields from thousands of diverse PDF reports, which vary significantly in length and complexity. They use the '!PREDICT method with 'GET_PRESIGNED_URL' to process documents from an external stage. After initial testing, they observe two distinct types of errors in the query results:

for other, lengthy PDF files. Which two of the following actions should the developer take to resolve these issues?

A) Implement a mechanism to process documents in smaller batches or extend the expiration time for the presigned URLs to ensure timely access by Document
B) Grant the

C) Redesign the input documents to ensure they do not exceed 125 pages per file, or preprocess by splitting overly long documents into multiple smaller files.
D) Reconfigure the external stage to use
E) Increase the virtual warehouse size to a Large or X-Large to speed up processing and prevent URL expiration.

3. A company wants to ingest and process scanned invoices and digitally-born contracts in Snowflake. They need to extract all text, preserving layout for contracts and just the text content for scanned invoices. Which AI_PARSE_DOCUMENT modes would be most appropriate for this scenario, and what is the primary purpose of the function itself?

A) Primary purpose is to summarize text. For contracts, use OCR mode; for invoices, use LAYOUT mode.
B) Primary purpose is to extract data and layout. For contracts, use LAYOUT mode; for invoices, use OCR mode.
C) Primary purpose is to translate text. Both document types should use LAYOUT mode.
D) Primary purpose is to classify text. For contracts, use LAYOUT mode; for invoices, use OCR mode.
E) Primary purpose is to generate new text. For contracts, use OCR mode; for invoices, use LAYOUT mode.

4. A data scientist has developed a Hugging Face sentence transformer model for semantic search and needs to deploy it for GPU- powered inference using Snowpark Container Services (SPCS) in Snowflake. They've already trained the SentenceTransformer model locally. Which of the following statements correctly describe essential considerations for logging and deploying this model, ensuring it leverages GPU resources and appropriate dependencies?

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

5. An ML engineering team is preparing to log a custom Python model to the Snowflake Model Registry. This model has several Python package dependencies. The team wants to ensure the model can be deployed optimally, either in a Snowflake warehouse or to Snowpark Container Services (SPCS), depending on future needs. They are particularly concerned with how dependency specification impacts deployment eligibility. Which statements accurately describe how Snowflake handles model dependencies and determines deployment eligibility for custom Python models logged in the Model Registry, particularly when considering both Snowflake warehouse and Snowpark Container Services (SPCS) environments? (Select all that apply.)

A) For models intended for SPCS, 'pip_requirements' are always preferred over 'conda_dependencies' because SPCS strictly prohibits the use of any conda packages from 'conda-forge'
B) Specifying both 'conda_dependencies' and 'pip_requirements' for a model is recommended to cover all possible deployment scenarios, and Snowflake's build process ensures compatibility between them.
C) When 'conda_dependencies' are specified for a model to be deployed to SPCS, these dependencies are by default obtained from 'conda-forge' rather than the Snowflake conda channel.
D) The function will fail if 'WAREHOUSE is specified in 'target_platforms' but the model's size or GPU requirements make it ineligible for warehouse deployment.
E) If all of a model's 'conda_dependencieS are available in the Snowflake conda channel, the model is automatically deemed eligible to run in a warehouse.

Solutions: