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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data analytics team is building a self-service analytics application using Snowflake Cortex Analyst to allow business users to query sales data with natural language. They are defining a semantic model in YAML to ensure accurate text-to-SQL generation. Which of the following is the most crucial aspect of the semantic model's configuration for Cortex Analyst to effectively translate natural language into SQL for structured data?

A) Providing detailed 'name', 'description' , and 'synonyms' for logical tables, dimensions, and facts to bridge the gap between business terminology and the underlying database schema.
B) Configuring the 'base_table' parameter to directly reference a dynamic table, ensuring real-time data ingestion and processing before SQL generation.
C) Utilizing advanced data types like 'VARIANT' and 'OBJECT for all dimensions to accommodate semi-structured data without complex transformations.
D) Specifying a dedicated 'CORTEX SEARCH SERVICE for every dimension to pre-compute all possible literal values, optimizing response time.
E) Defining a comprehensive 'verified_queries' section with a high volume of example natural language questions and their exact SQL translations to handle all potential user queries.

2. A data engineering team wants to deploy a proprietary PyCaret classification model, saved as pycaret_best_model.pkl, for inference within Snowpark Container Services (SPCS). They need to register this custom model in the Snowflake Model Registry. Which of the following is a correct and essential step in this process?

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

3. A data engineering team needs to implement a highly accurate, low-latency solution for classifying specialized technical documents into 50 distinct categories. They are considering fine-tuning a Large Language Model (LLM) within Snowflake Cortex for this task. Which of the following considerations are critical for optimizing the fine-tuned model's performance and minimizing inference latency for production use? (Select all that apply)

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

4. A data engineer is configuring a Document AI pipeline to process scanned PDF invoices stored in an internal stage named 'invoice_docs_stage'. After uploading the PDF files, they execute an extracting query using '!PREDICT. The query consistently returns the error:

Which of the following is the most likely cause of this error?

A) The documents contain non-English text, which is not fully supported by Document AI for optimal results.
B) The PDF documents exceed the maximum allowed file size of 50 MB.
C) The 'GET_PRESIGNED_URL' function used in the '!PREDICT query has an expired URL.
D) The internal stage was not created with 'SNOWFLAKE_SSE' encryption enabled.
E) The Document AI model build is attempting to process more than 1000 documents in a single query.

5.

A)

B)

C)

D)

E)

Solutions: