Purchase & study exam preparing with GES-C01 pass-king quiz training materials

Snowflake GES-C01 Exam Overview:

Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. 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) Reconfigure the external stage to use
B) 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.
C) Grant the

D) Increase the virtual warehouse size to a Large or X-Large to speed up processing and prevent URL expiration.
E) Implement a mechanism to process documents in smaller batches or extend the expiration time for the presigned URLs to ensure timely access by Document

2. A data engineering team is building an automated pipeline in Snowflake to process incoming sensor dat a. Each sensor reading includes a 1024-dimensional feature vector, and the team needs to flag readings that are significantly different from a baseline reference vector using VECTOR_L1_DISTANCE
. The pipeline uses Snowflake tasks to orchestrate data loading and transformation. Which statement regarding the integration and operational aspects of this pipeline is true?

A) The
B) The
C) To ensure efficient processing of new sensor data, the
D) Snowflake Cortex AI SQL functions, including
E) For optimal performance when calculating

3. 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) VECTOR
B) Document embeddings, which are typically float arrays, can be stored in a
C) Using the Snowpark Python library, developers can directly invoke

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

4. A data scientist needs to fine-tune a 'mistral-7b' LLM using Snowflake Cortex for a specific text summarization task. They have prepared a training dataset in a Snowflake table, with text in a column named 'source_text' and the desired summaries in a column named 'expected_summary' . They also want to understand the cost implications. Which SQL statement will correctly initiate the fine-tuning job, and how will the cost be primarily calculated?

A) The fine-tuning job is initiated by providing the prompt and completion data directly as arrays within the 'FINETUNE' function call, avoiding the need for a separate training data table, and costs are only for the storage of the fine-tuned model.
B) The fine-tuning job is initiated by:

C) The fine-tuning job is initiated by:

D) The fine-tuning job is initiated by:

E) The fine-tuning job is initiated by:

5. A development team is creating a new search application using Snowflake Cortex Search. They are currently using a 'snowflake-arctic- embed-I-v2.0' embedding model. After an initial load of 10 million rows, each with approximately 500 tokens of text, they observe a significant 'EMBED_TEXT_TOKENS' cost. They want to minimize these costs for future updates and ongoing operations. Considering their goal to optimize 'EMBED_TEXT_TOKENS' costs, which two strategies should the team prioritize for their Cortex Search Service?

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

Solutions: