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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data science team is using SNOWFLAKE. CORTEX. CLASSIFY_TEXT to categorize product reviews into detailed segments like 'Bug Report - Critical', 'Feature Request - UI/UX', 'General Praise', or 'Query - Billing Issue'. For highly nuanced reviews, they find the initial classifications lack precision, and they are also concerned about the associated compute costs for processing large volumes of dat a. Which strategies should they employ to optimize classification accuracy and manage costs effectively with this function?

A) For complex scenarios where the relationship between review text and categories is not straightforward, including a concise task_description (e.g., 'Classify the product review focusing on technical support relevance') in the options argument is recommended to guide the model.

B) To improve accuracy for ambiguous classifications, they should augment the list_of_categories with explicit description and examples for each category, understanding that these additions will increase input token costs for each record processed.

C) CLASSIFY_TEXT labels, descriptions, and examples are counted as input tokens only once per function call, regardless of the number of records processed in a batch, to optimize cost efficiency.
D) If classifying thousands of reviews, they can significantly reduce overall compute costs by setting the temperature option to 0.0 within CLASSIFY_TEXT to ensure deterministic and cheaper inference.
E) To reduce input token costs for classifications, the input text should be pre-processed to remove common stop words and punctuation, as these characters are counted as billable tokens without contributing to classification accuracy.

2. A data engineer is integrating SNOWFLAKE. CORTEX. CLASSIFY_TEXT into an automated data pipeline that uses dynamic tables to process and transform streaming text dat a. They have ensured that the service account used has been granted the necessary SNOWFLAKE. CORTEX_USER database role. After deploying the pipeline, they consistently receive an error whenever CLASSIFY_TEXT is invoked. Which of the following is the most likely cause of the error encountered by the data engineer?

A) The input text being processed by 'CLASSIFY _ TEXT includes extensive non-plain English content, such as code blocks, which causes the function to fail with an error.
B) The role used by the data engineer, despite having 'SNOWFLAKE.CORTEX_USER, lacks the fundamental 'USAGE privilege on the database where the text data is stored.
C) Snowflake Cortex functions, including 'CLASSIFY_TEXT , currently do not support integration with dynamic tables within data pipelines.
D) The array contains more than 100 unique categories, exceeding the maximum allowed limit for the function.
E) The 'task_description' provided in the optional arguments for 'CLASSIFY_TEXT exceeds the recommended length of approximately 50 words, leading to a validation error.

3. A Data Engineer has successfully deployed a Document AI model build named 'expense reports' to extract 'total amount' and approver signature' from digital expense reports. They observe that sometimes the 'approver signature' is not present in a document, or certain table cells are intentionally left blank in other document types processed by Document AI. They also want to automate the ingestion and processing of new expense reports. Regarding the '!PREDICT' method's JSON output when 'approver signature' is missing or a table cell is empty, and the recommended Snowflake features for continuously processing new documents, which statements are true?

A) To automate continuous processing, the Data Engineer should create a stream on the internal stage where documents are uploaded, and a task that calls the ' !PREDICT method when new data arrives.
B) Dynamic Tables are the primary recommended feature for continuous document processing with Document AI, replacing the need for streams and tasks.
C) When a table cell is empty, the Document AI model will return a 'score' key but no 'value' key for that cell.
D) If the 'approver_signature' is not found, the JSON output for 'approver_signature' will contain Tvalue" : null, "score" : 0.0}'.
E) If the model does not find an answer, it returns an empty string for 'value' and a score indicating its confidence that no answer was found.

4. A financial institution is deploying a sentiment analysis application that uses Snowflake Cortex 'SENTIMENT' and 'COMPLETE' functions, with different LLMs, for processing customer feedback. They are using AI Observability (Public Preview) to compare the cost- efficiency of using 'mistral-7b' versus 'claude-3-5-sonnet' as LLM judges for evaluation metrics, and also tracking the overall cost of their AI Observability usage. Which statements accurately reflect the cost implications and monitoring tools for this scenario?

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

5. 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:

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 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.