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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q10-Q15):

NEW QUESTION # 10
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 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.
B. 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.
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. 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.
E. 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.

Answer: A,C,E

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 # 11

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

Answer: D,E

Explanation:
To execute Snowflake cortex AI functions such as 'SNOWFLAKE.CORTEX.COMPLETE , 'SNOWFLAKE.CORTEX.CLASSIFY_TEXT, and 'EMBED_TEXT_768' (or their SAE prefixed counterparts), the role used by the application in this case) must be granted the 'SNOWFLAKE.CORTEX_USER database role. Additionally, for the Streamlit application to access any database or schema objects (like tables for data input/output, or for the Streamlit app itself if it is stored as a database object), the USAGE privilege must be granted on those specific database and schema objects. Option B, 'CREATE SNOWFLAKE.ML.DOCUMENT_INTELLIGENCE, is a privilege specific to creating Document AI model builds and is not required for general Cortex LLM functions. Option D, 'ACCOUNTADMIN', grants excessive privileges and is not a best practice for application roles. Option E, 'CREATE COMPUTE POOL' , is a privilege related to Snowpark Container Services for creating compute pools, which is not directly required for running a Streamlit in Snowflake application that consumes Cortex LLM functions.

NEW QUESTION # 12
A data engineering team is tasked with improving the accuracy of a Cortex Analyst solution for a large e-commerce product catalog. Users frequently ask natural language questions involving specific product names, brands, and categories. The team observes that Cortex Analyst sometimes struggles to identify and correctly filter by these literal values in the generated SQL. Which of the following configurations or approaches, within the semantic model, can effectively enhance Cortex Analyst's ability to precisely identify and use literal values for filtering, based on Snowflake's best practices?

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

Answer: C,D

Explanation:
Options A and B are correct. For dimensions with low cardinality (around 1-10 distinct values), setting 'is_enum: true' and providing an exhaustive 'sample_values' list ensures Cortex Analyst chooses only from that predefined list, improving literal usage. For higher cardinality dimensions, integrating a Cortex Search Service via the entry, specifying both the 'service' name and the , allows semantic search over the underlying data to find appropriate literal values. Option C is incorrect because Cortex Analyst leverages semantic similarity search or Cortex Search for literal values, not direct 'LIKE clauses in the 'expr' field. Option D is incorrect because while 'verified_queries' improve accuracy for specific, known questions, they are not a scalable solution for all possible literal search scenarios and are not the primary mechanism for improving general literal value identification. Option E is incorrect because the 'max_tokens' parameter controls the length of the LLM's output response, not its ability to identify or filter by literal values.

NEW QUESTION # 13
A new Gen AI team member attempts to use Document AI to process a batch of 1 ,500 scanned image files (JPG) that are 70 MB each, stored in an internal stage that was created without specifying an encryption type. Their '!PREDICT' queries consistently fail with various errors. Which of the following are valid reasons for the '!PREDICT' queries to fail in this scenario?

A. The team member's role lacks the database role, which is essential for using Document AI functions.
B. The individual JPG files exceed the maximum supported file size for Document AI.
C. The internal stage was not created with 'ENCRYPTION = (TYPE = 'SNOWFLAKE SSE'V, which is a requirement for Document AI.
D. JPG is an unsupported file format for Document AI.
E. Processing 1 ,500 documents in one query exceeds the maximum limit for Document AI.

Answer: A,B,C,E

Explanation:
Option A is correct because internal stages used with Document AI must specify 'ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE')' when created. Option B is correct as the database role is required for the account role to use Document AI functions to extract information. Option C is correct because Document AI supports processing a maximum of 1 ,000 documents in one query, so 1 ,500 documents would exceed this limit. Option D is correct because documents processed by Document AI must be 50 MB or less in size, and the 70 MB files exceed this limit. Option E is incorrect because JPG is listed as a supported file format for Document AI.

NEW QUESTION # 14
An enterprise is deploying a new RAG application using Snowflake Cortex Search on a large dataset of customer support tickets. The operations team is concerned about managing compute costs and ensuring efficient index refreshes for the Cortex Search Service, which needs to be updated hourly. Which of the following considerations and configurations are relevant for optimizing cost and performance of the Cortex Search Service in this scenario?

A. For optimal performance and cost efficiency, Snowflake recommends using a dedicated warehouse of size no larger than MEDIUM for each Cortex Search Service.
B. CHANGE_TRACKING
C. The
D. For embedding text, selecting a model like
E. The primary cost driver for Cortex Search is the number of search queries executed against the service, with the volume of indexed data (GBImonth) having a minimal impact on overall billing.

Answer: A,B,C,D

Explanation:
Option A is correct because a Cortex Search Service requires a virtual warehouse to refresh the service, which runs queries against base objects when they are initialized and refreshed, incurring compute costs. Option B is correct because the cost of embedding models varies. For example, 'snowflake-arctic-embed-m-vl .5 costs 0.03 credits per million tokens, while 'voyage-multilingual-2 costs 0.07 credits per million tokens. Choosing a more cost-effective model like 'snowflake-arctic-embed-m-vl for English-only data can reduce token costs. Option C is correct because Snowflake recommends using a dedicated warehouse of size no larger than MEDIUM for each Cortex Search Service to achieve optimal performance. Option D is correct because change tracking is required for the Cortex Search Service to be able to detect and process updates to the base table, enabling incremental refreshes that are more efficient than full re-indexing. Option E is incorrect because Cortex Search Services incur costs based on virtual warehouse compute for refreshes, 'EMBED TEXT TOKENS' cost per input token, and a charge of 6.3 Credits per GB/mo of indexed data. The volume of indexed data has a significant impact, not minimal.

NEW QUESTION # 15
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