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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q66-Q71):

NEW QUESTION # 66
A data engineering team is setting up a Retrieval Augmented Generation (RAG) application using Snowflake Cortex Search to provide contextual answers from customer support transcripts. The transcripts are stored in a Snowflake table named SUPPORT_TRANSCRIPTS. Which of the following statements are crucial considerations or accurate facts regarding the initial setup and configuration of the Cortex Search Service for this use case?

  • A. Cortex Search is designed to get users up and running quickly with a hybrid (vector and keyword) search engine on text data, handling embedding, infrastructure maintenance, and search quality parameter tuning automatically.
  • B. Columns specified in the ATTRIBUTES field during service creation are only used for filtering search results and do not need to be present in the source query.
  • C. Cortex Search Services currently support replication and cloning, allowing for easy disaster recovery and geographical distribution of the search index.
  • D. Snowflake recommends using a dedicated virtual warehouse of any size, including X-Large or 2X-Large, for each Cortex Search Service to ensure the fastest possible materialization of search indexes during creation and refresh.
  • E. The CREATE CORTEX SEARCH SERVICE command requires that CHANGE_TRACKING = TRUE be enabled on the source table, especially if the role creating the service is not the table owner. This ensures that the service can track updates to the base data.

Answer: A,E

Explanation:
Option A is correct because change tracking is required for the Cortex Search Service to monitor updates to the base table, particularly if the service creator is not the table owner. Option B is incorrect; Snowflake recommends using a dedicated warehouse no larger than MEDIUM for each service, as larger warehouses do not necessarily increase performance for index materialization. Option C is incorrect because columns in the ATTRIBUTES field must be included in the source query. Option D is correct as Cortex Search provides a low-latency, high-quality hybrid (vector and keyword) search engine that automatically manages embedding, infrastructure, and search quality tuning. Option E is incorrect because Cortex Search Services currently do not support replication or cloning.


NEW QUESTION # 67
A machine learning engineer is building a product recommendation system in Snowflake that uses item embeddings and customer query embeddings for similarity matching. They plan to use the VECTOR_L1_DISTANCE function to find the closest products to a user's query. Which statement accurately describes the cost and data type considerations for this approach?

  • A. Executing the
  • B. The
  • C. The maximum dimension for a
  • D. Storing the item and query embeddings in
  • E. Both

Answer: E

Explanation:
Option A is incorrect because vector similarity functions, including

, do not incur token-based costs. Option B is incorrect. The VECTOR data type supports elements of either 32-bit integers ( INT ) or 32-bit floating-point numbers ( FLOAT ), and its dimension can be a positive integer up to 4096, regardless of the specific dimension value. Option C is incorrect.

does not incur token-based costs. Conversely, generating embeddings using functions like EMBED_TEXT_768 does incur compute costs based on input tokens. Option D is correct. Vector similarity functions, such as VECTOR_INNER_PRODUCT and VECTOR_L1_DISTANCE , are explicitly stated to not incur token-based costs, unlike embedding functions. Option E is incorrect. The maximum dimension for the VECTOR data type is 4096.


NEW QUESTION # 68
A security auditor needs to access and analyze logs generated by Snowflake AI Observability for compliance auditing and to track the activity of generative AI applications. They need to understand how to reliably query this data and its temporal characteristics within Snowflake. Which of the following statements accurately describes the access and characteristics of this logged data?

  • A. Detailed request and response bodies, along with the generated SQL, are stored and can be directly queried using standard SQL.
  • B. Access to these detailed event tables is implicitly granted to roles holding the SNOWFLAKE. CORTEX_USER database role and the AI_OBSERVABILITY_EVENTS_LOOKUP application role.
  • C. The logs are automatically purged after 7 days of being recorded, requiring a separate process for long-term data retention.
  • D. Logged data from AI Observability's event tables becomes visible within a small latency, typically 1-2 minutes, after a request is made.
  • E. Logs are exclusively available for analysis through pre-built dashboards in Snowsight and cannot be accessed via direct SQL queries.

Answer: A,B,D

Explanation:


NEW QUESTION # 69
A data engineering team is setting up a Retrieval Augmented Generation (RAG) application using Snowflake Cortex Search to provide contextual answers from customer support transcripts. The transcripts are stored in a Snowflake table named SUPPORT _ TRANSCRIPTS. Which of the following statements are crucial considerations or accurate facts regarding the initial setup and configuration of the Cortex Search Service for this use case?

  • A. The Cortex Search Service can effectively be used as a RAG engine for LLM chatbots by leveraging semantic search capabilities to provide customized and contextualized responses from the text data.
  • B. Cortex Search is designed to get users up and running quickly with a hybrid (vector and keyword) search engine on text data, handling embedding, infrastructure maintenance, and search quality parameter tuning automatically.
  • C. Columns specified in the ATTRIBUTES field during service creation are only used for filtering search results and do not need to be present in the source query.
  • D. Snowflake recommends using a dedicated virtual warehouse of any size, including X-Large or 2X-Large, for each Cortex Search Service to ensure the fastest possible materialization of search indexes during creation and refresh.
  • E. The CREATE CORTEX SEARCH SERVICE command requires that CHANGE_TRACKING = TRUE be enabled on the source table, especially if the role creating the service is not the table owner. This ensures that the service can track updates to the base data.

Answer: A,B,E

Explanation:
Option A is correct because change tracking is required for the Cortex Search Service to monitor updates to the base table. Option B is incorrect; Snowflake recommends using a dedicated warehouse no larger than MEDIUM for each service, as larger warehouses do not necessarily increase performance for index materialization. Option C is incorrect because columns in the ATTRIBUTES field must be included in the source query. Options D and E are correct as Cortex Search provides low-latency, high-quality hybrid (vector and keyword) search, handling underlying complexities, and is primarily used as a RAG engine for LLM chatbots leveraging semantic search.


NEW QUESTION # 70
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. Direct comparison operators like
  • C. The
  • D. The
  • E. When calling

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 # 71
......

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