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GES-C01 - SnowPro® Specialty: Gen AI Certification Exam–Trustable Valid Study Notes

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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q140-Q145):

NEW QUESTION # 140
A data engineer is developing an AI-infused data pipeline in Snowflake Notebooks to analyze Federal Reserve Meeting Minutes and official Statements, which are initially in PDF format. The goal is to determine the FED's stance on interest rates (hawkish, dovish, or neutral) and the reasoning for each ingested PDF using an LLM. The pipeline needs to automate data ingestion, text extraction, LLM inference, and store the results in a Snowflake table. Which sequence of operations and Snowflake features is most appropriate for building this pipeline within Snowflake?

A. Scrape data from an external website directly into a Snowflake table using an 'EXTERNAL FUNCTION'. Then, apply 'SNOWFLAKE.CORTEX.EXTRACT ANSWER with a question like 'What is the FED's stance?' and 'SNOWFLAKE.CORTEX.SUMMARIZE' for reasoning to enrich the table. Automate this using 'STREAMS' and 'TASKS.
B. Scrape PDF data from an external website, load unstructured PDF files to an internal stage, then use a 'UDE to parse raw text from PDFs and a separate UDF' ('GENERATE_PROMPT) to encapsulate a custom prompt. Finally, use a 'TASK' to automate the process, calling Snowflake's function with the custom prompt at the point of ingestion to generate the sentiment signal and reasoning.
C. Load unstructured PDF files into an internal stage. Use a stored procedure to download new PDFs from the FOMC website. Leverage Snowpark Container Services to deploy a fine-tuned open-source LLM (e.g., Llama 2) for text extraction and sentiment analysis, and orchestrate the pipeline with ' Dynamic TableS for continuous updates.
D. Directly ingest PDF documents into a 'VARIANT column in a Snowflake table. Then, use the SQL function in 'OCR mode to extract text and layout. The extracted text is then passed to 'SNOWFLAKE.CORTEX.CLASSIFY TEXT to determine the sentiment, and the results are stored in a new table.
E. Ingest PDF documents into a directory table. Use 'Document AI' C!PREDICT') to extract specific entities and tables from the PDFs into structured JSON. Then, create a 'STREAM' on the stage and a 'TASK' to continuously process new documents, extracting information and potentially performing additional sentiment analysis with another LLM.

Answer: B,E

Explanation:
Option A is correct. This option directly aligns with the 'AI-Infused Pipelines with Snowflake Cortex' blog post. It describes scraping data, loading to an internal stage, using a stored procedure to download new PDFs, and 'UDFS for parsing text and generating prompts. It explicitly mentions using 'Snowflake's TRY_COMPLETE function for LLM inference with a custom prompt at ingestion, all automated with 'Streams' and 'Tasks'. Option B is incorrect. PARSE DOCUMENT in 'OCR mode extracts text but does not preserve layout and primarily focuses on text and layout extraction, not directly sentiment analysis or complex reasoning. While 'CLASSIFY _ TEXT (or can classify text, 'AI PARSE DOCUMENT is for extracting text and layout from documents, not direct ingestion into a 'VARIANT' column for text extraction via 'OCR mode. is a Cortex AI SQL function, typically used on files stored in stages. Option C is incorrect. While Snowpark Container Services can host LLMs and Dynamic Tables can automate updates, using an external LLM for text extraction when 'Document AI' or are available for native PDF processing is not the most direct approach. Additionally, 'Dynamic Tables' do not support incremental refresh with 'COMPLETE , and the prompt generation and sentiment analysis would still need to be explicitly defined. Option D is incorrect. Directly scraping into a Snowflake table with an 'EXTERNAL FUNCTION' isn't covered as the primary ingestion method for PDFs in the context of LLMs for sentiment. 'EXTRACT ANSWER and 'SUMMARIZE are task-specific Cortex functions, but the core task is a multi-step pipeline for PDF content analysis, which would be better served by a robust document processing solution. The focus of the pipeline example is on PDFs, not general website scraping directly to a table for immediate LLM application. Also, 'EXTRACT_ANSWER is for extracting a specific answer, while the sentiment is a classification. Option E is correct. This option uses 'Document AI' , which is specifically designed to extract structured information (entities, tables) from unstructured documents like PDFs using 'Arctic-TILT'. It explicitly mentions creating a 'STREAM' on a stage and a "TASK' for continuous processing, which is a standard pattern for Document AI pipelines. This approach directly handles the PDF extraction and structuring into JSON, which can then be further processed for sentiment analysis or reasoning if needed. The output JSON from '!PREDICT includes various extracted fields and can be parsed.

NEW QUESTION # 141
A global marketing team uses Snowflake to manage customer feedback in various languages. They need to translate customer reviews from German ("de") into English ("en") for analysis. The reviews are stored in a table named 'CUSTOMER REVIEWS' in a column called 'REVIEW TEXT'. Which of the following SQL statements correctly applies the 'SNOWFLAKE.CORTEX.TRANSLATE function and what is the expected return type for the translated text?

A. The query
B. The query
C. The query
D. The query
E. The query

Answer: E

Explanation:
Option B is correct. The 'SNOWFLAKE.CORTEX.TRANSLATE function takes three arguments: the text to be translated, the source language, and the target language. It returns a STRING value containing the translated text. Option A includes an unsupported 'high_accuracy' option and claims an incorrect return type. Option C uses an incorrect syntax and claims an incorrect return type. Option D uses an incorrect number of arguments. Option E claims an incorrect return type.

NEW QUESTION # 142
An AI development team is deploying a new Cortex Agent and needs to ensure optimal performance and adherence to Snowflake's Gen AI principles regarding data governance. Which of the following statements accurately reflect the models supported by Cortex Agents and the data governance considerations?

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

Answer: B,C,E

Explanation:

NEW QUESTION # 143
An organization is building a new knowledge base system within Snowflake, which relies on 'SNOWFLAKE.CORTEX.EMBED_TEXT_1024' to generate and store embeddings for documents in a 'VECTOR(FLOAT, 1024)' column. They plan to use these embeddings for semantic search and integrate them into various data processing workflows. Which of the following statements accurately describe limitations or specific compatibility aspects of 'EMBED TEXT 1024' or the 'VECTOR' data type within Snowflake?

A. The 'EMBED function in the Cortex REST API can be used to process a list of text strings, where each individual string can be up to 4096 characters long.
B. The 'VECTOR data type, used to store the output of is fully supported as primary or secondary index keys in Snowflake's hybrid tables.
C. To compare the generated 1024-dimension vectors for similarity, only the 'VECTOR COSINE SIMILARITY function is officially supported by Snowpark Python.
D. When 'EMBED_TEXT 1024' is invoked within a Snowflake dynamic table's SELECT statement, it allows for continuous, automated updates of embeddings as new data arrives.
E. The 'VECTOR data type is not supported in 'VARIANT columns, which means direct storage of embeddings alongside other semi-structured metadata in a single "VARIANT column is not possible.

Answer: A,E

Explanation:
Option C is correct. The 'VECTOR data type, which stores the output of ' , is explicitly not supported in 'VARIANT columns. This means embeddings cannot be stored directly within a 'VARIANT column. Option E is correct. When invoking the EMBED' function via the Cortex REST API, the 'text' argument accepts a list of text strings, and each string in that list can be up to 4096 characters long. Option A is incorrect because while 'VECTOR data types are allowed in hybrid tables, they are not supported as primary keys or secondary index keys. Option B is incorrect because Snowflake Cortex functions, including , do not support dynamic tables. Option D is incorrect because the Snowpark Python library does 'not' support the 'VECTOR_COSINE SIMILARITY function. It does, however, support , and SQL supports multiple vector similarity functions including

NEW QUESTION # 144
A Gen AI developer is using 'SNOWFLAKE.CORTEX.COMPLETE" to generate concise summaries of legal documents. Initially, the LLM sometimes provides overly creative or slightly off-topic responses, indicating potential 'hallucinations' or a lack of focus. To improve the factual accuracy and conciseness of the summaries, which combination of prompt engineering techniques and 'COMPLETE' function options should be prioritized?

A. Provide a 'task description' focusing on broad themes and set 'top_p' to 0.5 to balance creativity and relevance.
B. Instruct the model to 'think out loud' with an inner monologue in the prompt, and set 'max_tokens' to a large value to allow full reasoning.
C. Implement 'first principles thinking' in the prompt, clearly outlining logical steps for summarization, and set 'temperature' to 0 for deterministic output.
D. Use a system prompt defining a persona like 'creative writer' and set 'temperature' to 0.9 to encourage diverse summaries.
E. Rely solely on a comprehensive list of 'stop sequences' to end generation when the summary is complete.

Answer: C

Explanation:
To reduce 'hallucinations' and improve factual accuracy and conciseness, applying 'first principles thinking' by breaking prompts into logical steps helps the model respond from foundational concepts rather than assumptions. Setting 'temperature' to 0 yields the most consistent and deterministic results, which is crucial for factual accuracy and conciseness in summaries. Defining a 'creative writer' persona or using a high 'temperature' would increase diversity and potentially lead to more hallucinations (Option A). Instructing the model to 'think out loud' (Option C) enhances transparency of thought but does not directly enforce factual accuracy or conciseness in the way 'first principles' and low 'temperature' do. 'max_tokens' (Option C) affects output length, not necessarily accuracy or conciseness. A broad 'task description' (Option D) might not be specific enough, and 'top_p' also influences diversity. 'Stop sequences' (Option E) help with truncation but do not prevent factual errors or improve conciseness directly from the model's generation process.

NEW QUESTION # 145
......

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