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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q163-Q168):

NEW QUESTION # 163
A financial services company uses Snowflake Cortex's AI_COMPLETE for sentiment analysis on customer call transcripts, which contain personally identifiable information (PII). They also fine-tune a llama3.1-70b model with proprietary financial data. Which of the following statements accurately describe Snowflake's Gen AI principles regarding data privacy, model usage, and governance in this scenario?

A. Customer Data (inputs and outputs) for AI_COMPLETE, including PII, are guaranteed not to be available to other customers or used to train models made available to others.
B. When using Cortex Analyst with Snowflake-hosted LLMs, metadata and prompts are transmitted outside Snowflake's governance boundary for processing, incurring additional cross-cloud data transfer costs.
C. Enabling Cortex Guard for AI_COMPLETE automatically anonymizes all PII within the prompt before it reaches the LLM, regardless of the model chosen, to ensure privacy.
D. Snowflake's metadata fields, such as table and column names, should not contain personal, sensitive, or export-controlled data when using Snowflake AI services, to maintain data governance.
E. The fine-tuned llama3.1-70b model, including the proprietary training data used, is exclusively owned by the financial institution and is not shared with other Snowflake customers.

Answer: A,D,E

Explanation:
Option A is correct because Snowflake's trust and safety principles explicitly state that Usage and Customer Data (inputs and outputs of Snowflake AI Features) are NOT available to other customers and are NOT used to train, re-train, or fine-tune Models made available to others. Option B is correct because fine-tuned models built using your data can only be used by you, ensuring exclusivity. Option C is correct as customers should ensure no personal, sensitive, or regulated data is entered as metadata when using the Snowflake service. Option D is incorrect because Cortex Guard filters unsafe and harmful responses from the LLM; it does not automatically anonymize PII in the prompt before it reaches the LLM. Option E is incorrect because when Cortex Analyst is powered by Snowflake-hosted LLMs, data (including metadata or prompts) stays within Snowflake's governance boundary.

NEW QUESTION # 164
A global analytics firm is developing a Retrieval Augmented Generation (RAG) system in Snowflake to answer customer queries across a large repository of technical documentation, which includes documents in English, German, and Spanish. They are looking to use a Snowflake Cortex embedding model to convert document chunks into vector embeddings for their Cortex Search Service. Which of the following considerations are critical when selecting an appropriate embedding model to optimize for both query relevance and cost-efficiency for their multilingual RAG application? (Select all that apply)

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

Answer: A,E

Explanation:
Option B is correct because both

model provides an increased context window of 8000 tokens while maintaining the same cost per million tokens (0.05 credits) as the 512-token version of

Option C is correct because Snowflake recommends splitting text into chunks of no more than 512 tokens for best search results with Cortex Search, as research shows this typically leads to higher retrieval precision and improved downstream LLM response quality, even when using longer-context embedding models. Option A is incorrect because

is an English-only embedding model, which does not meet the requirement for multilingual documentation. Option D is incorrect; the cost per million tokens for EMBED_TEXT_1024 models (e.g., 0.05-0.07 credits) is not inherently more cost-efficient than EMBED_TEXT_768 models (e.g., 0.03 credits), and cost-efficiency depends on the specific model and use case, not just output dimensions. Option E is incorrect; the context window of an embedding model refers to the maximum length of a text input (chunk) it can process. The maximum pages a document can have (e.g., 300 pages for Document AI) is a separate document requirement, not directly determined by the embedding model's context window.

NEW QUESTION # 165
A security-conscious data scientist in an Azure East US 2 (Virginia) account wants to fine-tune a mistral-7b model for a specific text summarization task and then deploy it for real-time inference using the Cortex REST API. The base model is natively mistral -7b available for fine-tuning in Azure East US 2 (Virginia). For subsequent inference using the fine-tuned model, they need to understand the regional and cross-region inference considerations. Which of the following statements are correct?

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

Answer: B,C,D,E

Explanation:

NEW QUESTION # 166
An enterprise is deploying a new RAG application using Snowflake Cortex Search on a large dataset of customer support tickets. The operations team is concerned about managing compute costs and ensuring efficient index refreshes for the Cortex Search Service, which needs to be updated hourly. Which of the following considerations and configurations are relevant for optimizing cost and performance of the Cortex Search Service in this scenario?

A. The
B. The primary cost driver for Cortex Search is the number of search queries executed against the service, with the volume of indexed data (GB/month) having a minimal impact on overall billing.
C. For embedding text, selecting a model like
D. For optimal performance and cost efficiency, Snowflake recommends using a dedicated warehouse of size no larger than MEDIUM for each Cortex Search Service.
E. CHANGE_TRACKING

Answer: A,C,D,E

Explanation:
Option A is correct because a Cortex Search Service requires a virtual warehouse to refresh the service, which runs queries against base objects when they are initialized and refreshed, incurring compute costs. Option B is correct because the cost of embedding models varies. For example, 'snowflake-arctic-embed-m-v1.5 costs 0.03 credits per million tokens, while 'voyage-multilingual-2 costs 0.07 credits per million tokens. Choosing a more cost-effective model like 'snowflake-arctic-embed-m-v1.5' for English-only data can reduce token costs. Option C is correct because Snowflake recommends using a dedicated warehouse of size no larger than MEDIUM for each Cortex Search Service to achieve optimal performance. Option D is correct because change tracking is required for the Cortex Search Service to be able to detect and process updates to the base table, enabling incremental refreshes that are more efficient than full re-indexing. Option E is incorrect because Cortex Search Services incur costs based on virtual warehouse compute for refreshes, 'EMBED_TEXT_TOKENS' cost per input token, and a charge of 6.3 Credits per GB/mo of indexed data. The volume of indexed data has a significant impact, not minimal.

NEW QUESTION # 167
A Gen AI Specialist is tasked with enhancing a Cortex Analyst semantic model to improve the accuracy of literal string searches for product names within user queries. The product names are stored in a high-cardinality PRODUCT_NAME column in the underlying PRODUCT table. The current semantic model already defines a dimension for PRODUCT_NAME. Which of the following configurations and considerations are essential for integrating Cortex Search with Cortex Analyst to achieve this goal?

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

Answer: E

Explanation:
For dimensions with high-cardinality, Cortex Analyst recommends integrating with Cortex Search to improve literal string searches. A Cortex Search Service can be created on the relevant column (e.g., 'PRODUCT NAME') to perform a semantic search for literal values. The semantic model's dimension should then include the configuration, referencing the created service. Option A is incorrect because 'sample_values' are recommended for dimensions with low-cardinality (approximately 1-10 distinct values) to avoid exceeding the LLM's context window. For high-cardinality data, Cortex Search is the appropriate solution. Option C is incorrect because 'VARIANT and other complex data types are currently not supported for dimensions in Cortex Analyst semantic models. Option D is incorrect. While 'max_tokens' can be controlled for 'COMPLETE functions, Cortex Analyst's primary mechanism for literal search improvement is through semantic search over sample values or Cortex Search Services, not solely by increasing token limits. Option E is incorrect. The 'use_as_onboarding_question' flag is used for Verified Queries to explicitly suggest questions to users as a starting point, not to improve the accuracy of literal string matching within queries.

NEW QUESTION # 168
......

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