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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q97-Q102):NEW QUESTION # 97
A data science team is planning to implement a new RAG (Retrieval Augmented Generation) application using Snowflake Cortex, specifically leveraging Cortex Search. They are evaluating the key features, best practices, and cost considerations associated with Cortex Search. Which of the following statements accurately describe aspects of Cortex Search?
- A. Cortex Search Services require a virtual warehouse for initial setup and subsequent refreshes to run queries against base objects and build the search index.
- B. For best search results, Snowflake recommends splitting text in the search column into chunks of no more than 512 tokens, even when longer-context embedding models are available.
- C. Cortex Search automatically handles embedding, infrastructure maintenance, and ongoing index refreshes, and can be used as a backend for enterprise search or a RAG engine for LLM chatbots.
- D. Cortex Search supports only English-only embedding models; multilingual RAG applications require external embedding solutions.
- E. The credit cost for Cortex Search Services is primarily based on the number of queries executed against the service, not the amount of indexed data.
Answer: A,B,C
Explanation:
Option A is correct. Cortex Search provides low-latency, high-quality 'fuzzy' search and handles embedding, infrastructure maintenance, search quality parameter tuning, and ongoing index refreshes. Its primary use cases are as a RAG engine for LLM chatbots and as a backend for enterprise search. Option B is incorrect. Cortex Search Services incur costs based on the amount of indexed data (6.3 Credits per GB/mo of indexed data), not solely on the number of queries executed. Option C is incorrect. Cortex Search offers multilingual embedding models like 'snowflake-arctic-embed-l-v2.ff and 'voyage-multilingual-2 , supporting multilingual AI workflows. Option D is correct. Snowflake recommends splitting text into chunks of no more than 512 tokens for optimal search results, as smaller chunks can lead to more precise retrieval and higher-quality LLM responses in RAG scenarios, even with models that support longer context windows. Option E is correct. A virtual warehouse is required for Cortex Search Service to refresh the service, which includes running queries against base objects, orchestrating text embedding jobs, and building the search index.
NEW QUESTION # 98
A business intelligence team wants to enable non-technical users to query their Snowflake data using natural language for sales analytics reports via Cortex Analyst. They are designing the YAML semantic model. Which of the following statements accurately describe key aspects of designing and utilizing a semantic model for Cortex Analyst?
- A. To optimize performance, Snowflake recommends including all available tables and columns from the underlying database in a semantic model, especially for complex analytical tasks.
- B. facts in a semantic model are primarily used to define categorical data, such as product types or customer segments, to support filtering operations.
- C. Dimensions in the semantic model YAML, such as 'state' or 'product_category', can include synonyms to map common business terms to underlying technical column names, thereby improving natural language understanding for users.
- D. The VARIANT, OBJECT, GEOGRAPHY, and ARRAY data types are fully supported for dimension and fact columns within a semantic model, offering flexibility for diverse data structures.
- E. The base_table field in a logical table definition must directly reference a physical table and cannot point to a view, as Cortex Analyst only works with raw tables for performance reasons.
Answer: C
Explanation:
Option A is incorrect because a logical table in a semantic model can represent either a physical database table or a view. Option B is correct; dimensions can include synonyms to help map natural language questions to technical terms, enhancing query accuracy. Option C is incorrect as the 'VARIANT, "OBJECT, 'GEOGRAPHY , and 'ARRAY' data types are currently not supported for dimension or fact columns in a semantic model. Option D is incorrect; 'facts' describe numerical values (e.g., revenue, salary), while 'dimensions' describe categorical values (e.g., state, user_type). Option E is incorrect because for performance reasons, Snowflake recommends starting with a small number of tables and columns (not more than 10 tables or 50 columns) and expanding gradually.
NEW QUESTION # 99
A large e-commerce company plans to implement real-time sentiment analysis on millions of incoming customer reviews using SNOWFLAKE. CORTEX. SENTIMENT. They are concerned about managing costs and ensuring efficient processing. Which of the following statements about cost considerations and performance optimizations for SNOWFLAKE. CORTEX. SENTIMENT are true?
(Select all that apply)
- A. The actual number of tokens processed and billed for a SENTIMENT call is typically higher than the raw input text length, due to an internal prompt added by the function.
- B. The newer AI_SENTIMENT function is a free, serverless alternative to SNOWFLAKE. CORTEX. SENTIMENT, offering cost savings for high-volume scenarios.
- C. The fixed billing rate for the SENTIMENT function is 0.08 Credits per one million input tokens processed.
- D. Snowflake recommends using a smaller warehouse (no larger than MEDIUM), as larger warehouses do not increase performance for SENTIMENT function calls.
- E. Billing for SNOWFLAKE. CORTEX. SENTIMENT is primarily based on the number of output tokens generated in the response.
Answer: A,C,D
Explanation:
Option B is correct. Snowflake recommends executing queries that call Cortex AI functions, including 'SENTIMENT , with a smaller warehouse (no larger than MEDIUM) because larger warehouses do not increase performance for these operations. Option C is correct. For functions like 'SENTIMENT, Snowflake adds an internal prompt to the input text in order to generate the response, which results in a higher input token count for billing than the raw text provided. Option E is correct. The 'Sentiment' function incurs compute cost at a rate of 0.08 Credits per one million Tokens. Option A is incorrect because for SENTIMENT , only input tokens are counted towards the billable total, not output tokens. Output tokens are typically billed for functions that generate new text, such as SAI COMPLETE or 'SUMMARIZE. Option D is incorrect because 'AI_SENTIMENT is an updated version of the function, but it still incurs compute costs based on tokens processed and is not free.
NEW QUESTION # 100
A data engineering team needs to implement a highly accurate, low-latency solution for classifying specialized technical documents into 50 distinct categories. They are considering fine-tuning a Large Language Model (LLM) within Snowflake Cortex for this task. Which of the following considerations are critical for optimizing the fine-tuned model's performance and minimizing inference latency for production use? (Select all that apply)
- A. Option D
- B. Option E
- C. Option B
- D. Option C
- E. Option A
Answer: C,E
Explanation:
To optimize a fine-tuned model's performance and minimize inference latency: * Smaller models (like *llama3-8b' with an 8k context window, supporting 6k for prompt and 2k for completion) generally have lower latency for both training and inference. While exceeding the context window results in truncation which can negatively impact quality, for specific tasks, a smaller, fine-tuned model can achieve the required accuracy with better performance. * **B:** Deploying a fine-tuned model to a Snowpark Container Services (SPCS) compute pool with GPU instances (e.g., or is crucial for leveraging GPU acceleration. This is explicitly optimized for intensive GPU usage scenarios like LLMsA/LMs, which significantly reduces inference latency and increases throughput. * It is important to ensure that prompt and completion pairs do not *exceed* the context window to prevent truncation and negative impact on model quality. However, *precisely filling* the context window is not a requirement or an optimization strategy; the focus should be on providing relevant and high-quality data within the model's limits. * '*D:" Setting 'max_epochs' to 1 reduces the *training time*. However, training time does not directly improve *inference* latency for the deployed model. Inference latency depends on the model's architecture, deployment hardware, and runtime optimizations. Furthermore, too few epochs can lead to a poorly performing model, failing the accuracy requirement. * E: This describes using the 'AI CLASSIFY managed function for zero-shot classification, which is an alternative to fine-tuning. While it might avoid the latency associated with fine-tuning *training*, the question is specifically about optimizing the performance of a *fine-tuned model* for a specialized task, implying that fine-tuning is chosen for its potential to achieve higher accuracy for that niche use case compared to zero-shot approaches.
NEW QUESTION # 101
An AI developer is testing a new RAG application in Snowflake. The application uses
in this scenario?
- A. Option D
- B. Option E
- C. Option B
- D. Option C
- E. Option A
Answer: C,E
Explanation:
Option A is correct. If access to a specific model is restricted by the
Option C is incorrect; setting 'temperature' to 0 makes the output more deterministic and focused, which generally improves adherence to instructions and is recommended for consistent results, not causes failures. Option D is incorrect. The
, with smaller warehouses (no larger than MEDIUM), meaning an X-Small warehouse is generally sufficient for executing the function itself, though it might impact performance for very large datasets if used for other operations.
NEW QUESTION # 102
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