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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q13-Q18):

NEW QUESTION # 13
An ML Engineer has developed a custom PyTorch model for image processing that requires GPU acceleration and specific PyPl packages ('torch' , 'torchvision'). They want to deploy it as a service on Snowpark Container Services (SPCS) using the Snowflake Model Registry. Which of the following statements are true regarding the deployment of this model to SPCS and its requirements? (Select all that apply.)

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

Answer: C,D,E

Explanation:
StatementA is incorrect. While Snowflake recommends using only 'conda_dependencies' or only 'pip_requirements' (not both) to avoid package conflicts, the scenario explicitly mentions PyPl packages. If using 'pip_requirements', all required packages should be listed there. The example incorrectly assumes 'torchvision' would necessarily be best sourced from Conda and dictates avoiding 'pip_requirements' entirely, which is an oversimplification of the recommendation. Statement B is correct. To utilize GPU acceleration in SPCS, a compute pool configured with a GPU instance family (e.g., *GPU must be created and then referenced by name in the 'service_compute_poor' argument when creating the service. Statement C is correct. Snowflake's warehouse nodes have restricted directory access, and '/tmpP is recommended as a safe and writeable location for models that need to write files during execution. This principle extends to SPCS containers. Statement D is correct. The 'create_service' method for deploying models to SPCS takes a gpu_requests argument, which specifies the number of GPUs to allocate to the service. Setting this (e.g., to "s) is crucial for ensuring the model runs on GPU hardware. Statement E is incorrect. The 'relax_version' option, which modifies version constraints, defaults to 'True' in 'log_moder' While often beneficial, it is not mandatory to explicitly set it to 'True' for every deployment scenario.


NEW QUESTION # 14
A project team is preparing to deploy a Document AI solution to process scanned customer feedback forms. They have created a dedicated role, 'customer feedback _ processor', and successfully granted it the SNOWFLAKE. DOCUMENT_INTELLIGENCE_CREATOR database role. The environment consists of 'feedback database, 'forms schema' schema, and 'ai workload warehouse. However, when the attempts to prepare a Document AI model build in Snowsight, they encounter a 'permission denied' error. Which of the following missing 'USAGE' grants could be the direct cause of this error?

  • A.
  • B.
  • C.
  • D.
  • E.

Answer: A,B,D

Explanation:


NEW QUESTION # 15
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. Direct comparison operators like
  • B. The
  • C. When calling
  • D. The
  • E. To store a vector with 512 floating-point dimensions in a table, the correct SQL syntax for adding a column would be:

Answer: C,D,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 # 16
An organisation is deploying a Snowflake Cortex Agent to assist business users with data insights. To enable users to interact with this agent via the agent : run API, which of the following database roles or privileges must be granted to their account role?

  • A. CREATE EXTERNAL AGENT
  • B. The
  • C. The
  • D. The
  • E. The

Answer: C

Explanation:
Option A is incorrect. The
CREATE EXTERNAL AGENT
privilege is listed as a requirement for AI Observability to create and execute runs, not for direct interaction with a Cortex Agent via its API.
Option B is correct. To make a request to Cortex Agent via the
agent : run
API, a role must have either the

or

database role granted. The

role specifically provides access to the Agents feature. Option C is incorrect. EXECUTE TASK global privilege is a prerequisite for AI Observability setup, but not explicitly for simply invoking the Cortex Agent's agent : run API. Option D is incorrect. The

database role is required for Document AI, which an agent *might* leverage as a tool, but it's not a direct requirement for the role interacting with the agent API. Option E is incorrect. The sources specify that roles with

are sufficient for Cortex Agent API requests, indicating that the ACCOUNTADMIN role is not strictly necessary for this operation.


NEW QUESTION # 17
A data analytics team is building a self-service analytics application using Snowflake Cortex Analyst to allow business users to query sales data with natural language. They are defining a semantic model in YAML to ensure accurate text-to-SQL generation. Which of the following is the most crucial aspect of the semantic model's configuration for Cortex Analyst to effectively translate natural language into SQL for structured data?

  • A. Specifying a dedicated 'CORTEX SEARCH SERVICE for every dimension to pre-compute all possible literal values, optimizing response time.
  • B. Defining a comprehensive 'verified_queries' section with a high volume of example natural language questions and their exact SQL translations to handle all potential user queries.
  • C. Utilizing advanced data types like 'VARIANT' and 'OBJECT for all dimensions to accommodate semi-structured data without complex transformations.
  • D. Configuring the 'base_table' parameter to directly reference a dynamic table, ensuring real-time data ingestion and processing before SQL generation.
  • E. Providing detailed 'name', 'description' , and 'synonyms' for logical tables, dimensions, and facts to bridge the gap between business terminology and the underlying database schema.

Answer: E

Explanation:
Option C is correct because the primary purpose of a semantic model in Cortex Analyst is to provide semantic information about your data, bridging the gap between business users' natural language and the technical database schema. This includes using descriptive names, synonyms, and descriptions for logical tables, dimensions, and facts, which is essential for Cortex Analyst to reliably generate accurate SQL from natural language questions. Option A is incorrect; while Cortex Search Services can improve literal matching for dimensions, it's an enhancement and not the most crucial foundational aspect of the semantic model for general text-to-SQL translation, nor is it required for 'every' dimension. Option B is incorrect because while 'verified_queries' improve accuracy for similar questions, a high volume of examples for 'all' potential queries is not feasible or the most crucial initial configuration; the core mapping (Option C) is more fundamental. Option D is incorrect as the 'base_table' must refer to a physical table or view, not directly to a dynamic table. Furthermore, Cortex functions do not support dynamic tables directly. Option E is incorrect because 'VARIANT, 'OBJECT, 'GEOGRAPHY , and 'ARRAY data types are explicitly not supported for dimension, fact, or metric columns in a semantic model.


NEW QUESTION # 18
......

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