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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q189-Q194):

NEW QUESTION # 189
A data platform administrator needs to retrieve a consolidated overview of credit consumption for all Snowflake Cortex AI functions (e.g., LLM functions, Document AI, Cortex Search) across their entire account for the past week. They are interested in the aggregated daily credit usage rather than specific token counts per query. Which Snowflake account usage views should the administrator primarily leverage to gather this information?

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

Answer: E

Explanation:

NEW QUESTION # 190
A data engineer is designing an automated pipeline to process customer feedback comments from a 'new_customer_reviews' table, which includes a 'review_text' column. The pipeline needs to classify each comment into one of three predefined categories: 'positive', 'negative', or 'neutral', and store the classification label in a new 'sentiment_label' column.
Which of the following statements correctly describe aspects of implementing this data transformation using 'SNOWFLAKE.CORTEX.CLASSIFY_TEXT' in a Snowflake pipeline?

A. Both the input string to classify and the are case-sensitive, potentially yielding different results for variations in capitalization.
B. The cost for 'CLASSIFY _ TEXT is incurred based on the number of pages processed in the input document.
C. The argument must contain exactly three unique categories for sentiment classification.
D. Including an optional 'task_description' such as
E. The classification can be achieved by integrating a 'SELECT statement with

Answer: A,D,E

Explanation:
Option A is correct. 'SNOWFLAKE.CORTEX.CLASSIFY_TEXT classifies free-form text into categories and returns an 'OBJECT' value (VARIANT) where the 'label' field specifies the category. This can be extracted using '['labeI']' and seamlessly integrated into 'INSERT or 'UPDATE' statements within a pipeline task for data transformation.
Option B is correct. Adding a clear 'task_description' to the 'options' argument for 'CLASSIFY_TEXT' can significantly improve classification accuracy. This is particularly useful when the relationship between the input text and the provided categories is ambiguous or nuanced.
Option C is incorrect. incurs compute cost based on the number of tokens processed (both input and output tokens), not on the number of pages in a document. Functions like 'AI_PARSE_DOCUMENT bill based on pages.
Option D is incorrect. The argument for 'CLASSIFY_TEXT' must contain at least two and at most 100 unique categories. It is not strictly limited to three for any classification task, including sentiment.
Option E is correct. Both the 'input' string to classify and the are case-sensitive, meaning that differences in capitalization for either the input text or the category labels can lead to different classification results.

NEW QUESTION # 191
An ML engineer is working on a Snowflake project that requires storing and comparing high-dimensional feature vectors extracted from customer interaction logs. They need to ensure the vector data type is correctly defined and then perform an inner product calculation.
Which of the following statements about defining and using the
VECTOR
data type and
VECTOR_INNER_PRODUCT
function in Snowflake SQL are correct? (Select all that apply)

A. To store a vector with 512 floating-point dimensions in a table, the correct SQL syntax for adding a column would be:
B. The
C. When calling
D. Direct comparison operators like
E. The

Answer: A,C,E

Explanation:
Option A is correct. The
VECTOR
data type supports elements of 32-bit integers (
INT
) or 32-bit floating-point numbers (
FLOAT
), and its dimension can be a positive integer value with a maximum of 4096. Option B is correct. The syntax for specifying a VECTOR type is VECTOR(,
. The example

demonstrates this structure. Option C is incorrect. Direct vector comparisons (e.g.,

) are byte-wise lexicographic and do not produce the expected results for number comparisons; therefore, dedicated vector similarity functions should be used. Option D is incorrect. Vectors are explicitly not supported in VARIANT columns. Option E is correct. SQL examples in the documentation demonstrate explicit casting of array literals to the VECTOR data type, such as

, when used with vector similarity functions.

NEW QUESTION # 192
A development team is building a RAG application in Snowflake Cortex that needs to extract high-fidelity text and layout from a collection of technical documentation PDFs stored in an internal stage to power semantic search and LLM responses. They want to ensure proper context retrieval for complex user queries. Given this scenario, which of the following actions or statements are crucial for effectively leveraging AI_PARSE_DOCUMENT to optimize the RAG pipeline?

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

Answer: B,E

Explanation:

NEW QUESTION # 193
A Snowflake developer, 'AI _ ENGINEER , is creating a Streamlit in Snowflake (SiS) application that will utilize a range of Snowflake Cortex LLM functions, including SNOWFLAKE. CORTEX. COMPLETE, SNOWFLAKE .CORTEX.CLASSIFY TEXT, and SNOWFLAKE. CORTEX. EMBED TEXT 768. The application also needs to access data from tables within a specific database and schem a. 'AI _ ENGINEER has created a custom role, for the application to operate under. Which of the following privileges or roles are absolutely necessary to grant to for the successful execution of these Cortex LLM functions and interaction with the specified database objects? (Select all that apply.)

A.
B. The USAGE privilege on the specific database and schema where the Streamlit application and its underlying data tables are located.
C. The ACCOUNTADMIN role to ensure unrestricted access to all Snowflake Cortex features.
D. The CREATE COMPUTE POOL privilege to provision resources for the Streamlit application.
E.

Answer: A,B

Explanation:
To execute Snowflake Cortex AI functions such as 'SNOWFLAKE.CORTEX.COMPLETE , 'SNOWFLAKE.CORTEX.CLASSIFY_TEXT , and (or their 'AL' prefixed counterparts), the role used by the application in this case) must be granted the 'SNOWFLAKE.CORTEX USER database role. Additionally, for the application to access any database or schema objects (like tables for data input/output or storing the Streamlit app itself), 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 # 194
......

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Dumps GES-C01 Torrent, GES-C01 Test Vce Free

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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q189-Q194):

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