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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q96-Q101):

NEW QUESTION # 96
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?

Answer: C

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 # 97
A Gen AI Specialist is setting up AI Observability for a new generative AI application in Snowflake to monitor its performance and debug issues. The application is built in Python. Which of the following prerequisites must be met to enable tracing for this application?

Answer: D,E

Explanation:


NEW QUESTION # 98
A development team plans to utilize Snowpark Container Services (SPCS) for deploying a variety of AI/ML workloads, including custom LLMs and GPU-accelerated model training jobs. They are in the process of creating a compute pool and need to select the appropriate instance families and configurations. Which of the following statements about 'CREATE COMPUTE POOL' in SPCS are accurate?

Answer: B,E

Explanation:
Option A is correct. GPU-accelerated workloads, such as LLM inference and model training, require instance families specifically designed with GPUs. The documentation lists instance family names starting with 'GPU' for this purpose, such as or 'GPU_GCP NV L4 Option B is incorrect. While 'MIN NODES and 'MAX NODES define the range, the size of compute clusters in Snowpark Container Services does ''not'' auto-scale dynamically based on workload demand. Users must manually alter the number of instances at runtime using commands like 'ALTER SERVICE MIN INSTANCES = s. Snowflake does handle load balancing across instances within the configured node counts. Option C is correct. The 'AUTO_RESUME = TRUE parameter, when specified during compute pool creation, enables the pool to automatically resume operation when a service or job is submitted, removing the need for explicit SALTER COMPUTE POOL RESUME commands. option D is incorrect. Setting = prevents the compute pool from automatically suspending, meaning it will continue to consume credits even when idle. This would generally lead to higher costs, not cost optimization, unless the pool is constantly active. The default is 3600 seconds (1 hour). SPCS Compute Nodes have a minimum charge of five minutes when started or resumed, making intelligent use of auto-suspend important for cost management. Option E is incorrect. Snowpark-optimized warehouses are a type of 'virtual warehouse' and are recommended for Snowpark workloads with large memory requirements or specific CPU architecture, typically for single-node ML training workloads 'within a warehouse'. SPCS compute pools, however, provide their own dedicated instance families (CPU, HighMemory, GPU) for containerized workloads, abstracting the underlying infrastructure and supporting distributed GPU clusters directly within SPCS, not Snowpark-optimized warehouses as a 'compute pool type' for SPCS.


NEW QUESTION # 99
A financial analyst is concerned about the rising costs of their Document AI pipeline, which uses to extract data from daily financial reports. They observe that their assigned 'LARGE virtual warehouse is running continuously, even during periods of low document ingestion, contributing significantly to their bill. They want to investigate how to reduce costs effectively for their existing Document AI setup.

Answer: C

Explanation:
Snowflake explicitly recommends using an X-Small, Small, or Medium warehouse for Document AI. Scaling up the warehouse does not increase the speed of query processing for Document AI but can lead to unnecessary costs. This directly addresses the scenario of a 'LARGE warehouse running continuously and contributing to high bills. Option A is incorrect because while 'METERING DAILY HISTORY is used for cost tracking, Document AI's service-side usage appears under 'AI_SERVICES , not 'WAREHOUSE_METERING' for the AI service component itself. 'WAREHOUSE METERING' would show general warehouse costs, not specifically tied to Document AI's compute portion. Option C is incorrect because Document AI (using ' !PREDICT) incurs 'AI Services compute' costs based on 'time spent actually using these resources' (8 Credits per hour of compute), not per token. Option D is not necessarily accurate guidance; SAI PARSE DOCUMENT is a separate Cortex AI SQL function for document processing, billed per page, while Document AI's '!PREDICT is part of a Document AI model build. Replacing it without a full re- evaluation of the workflow might not be optimal or directly cost-efficient for an established pipeline. Option E is incorrect because the view tracks Document AI processing activity, including '!PREDICT calls.


NEW QUESTION # 100
A data engineering team aims to automatically classify incoming customer support requests into predefined categories ('Technical Issue', 'Billing Inquiry', 'General Question') as part of their Snowflake data ingestion pipeline. The goal is to achieve high classification accuracy while managing LLM inference costs efficiently. Which of the following strategies, when applied within a Snowflake data pipeline using Streams and Tasks, would best contribute to meeting these objectives?

Answer: B

Explanation:


NEW QUESTION # 101
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