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[General] GES-C01 PDF Download | Real GES-C01 Exam Dumps

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【General】 GES-C01 PDF Download | Real GES-C01 Exam Dumps

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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q20-Q25):NEW QUESTION # 20
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 E
  • B. Option C
  • C. Option A
  • D. Option D
  • E. Option B
Answer: B,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 # 21
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. Storing the item and query embeddings in
  • B. The
  • C. The maximum dimension for a
  • D. Both
  • E. Executing the

Answer: D
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 # 22
A team of data application developers is leveraging Snowflake Copilot to streamline the creation of analytical SQL queries within their Streamlit in Snowflake application. They observe that Copilot sometimes struggles with complex joins or provides suboptimal queries when dealing with a newly integrated, deeply nested dataset. Based on Snowflake's best practices and known limitations, which actions or considerations would help improve Copilot's performance in this scenario?
  • A. Grant Copilot direct access to the raw data using ACCOUNTADMIN privileges, allowing it to infer schema relationships more effectively from data content.
  • B. Ensure that a database and schema are explicitly selected for the current session, and that column names are meaningful, to provide Copilot with better context for query generation.
  • C. Break down complex requests into simpler, multi-turn questions, as Copilot is designed to build complex queries through conversational refinement and follow-up questions.
  • D. Implement curated views with descriptive and easy-to-understand names for views and columns, appropriate data types, and pre-define common/complex joins to simplify the underlying schema for Copilot.
  • E. Enable the CORTEX_MODELS_ALLOWLIST parameter to restrict Copilot to only use the largest available LLMs, thereby guaranteeing higher accuracy for complex queries.
Answer: B,C,D
Explanation:
To improve Snowflake Copilot's performance, creating curated views with descriptive names, appropriate data types, and capturing common/complex joins is a key best practice. Copilot can build complex queries through a conversation by asking follow-up questions. It also uses the names of databases, schemas, tables, and columns, and their data types to determine available data, so ensuring these are meaningful and correctly set for the session is crucial for relevant responses. Option B is incorrect because CORTEX_MODELS_ALLOWLIST controls access to specific LLMs but doesn't guarantee higher accuracy for Copilot's SQL generation. Option D is incorrect as Snowflake Copilot does not have access to the data inside tables; it operates on metadata. Granting privileges would not change this fundamental operational principle and is ACCOUNTADMIN against best practices for least privilege.

NEW QUESTION # 23
A global enterprise has Snowflake accounts in various regions, including a US East (Ohio) account where a critical application is deployed. They need to use AI_COMPLETE with the claude-3-5-sonnet model for real-time customer support, but this model is not natively available in US East (Ohio) for direct AI_COMPLETE usage. The Snowflake administrator considers enabling cross-region inference. Which statements accurately reflect the considerations and characteristics of cross-region inference in Snowflake Cortex?

  • A. Option E
  • B. Option C
  • C. Option A
  • D. Option D
  • E. Option B
Answer: B,E
Explanation:
Option B is correct because setting the parameter to 'ANY_REGION' enables inference requests to be CORTEX_ENABLED_CROS_ REGION processed in a different region from the default, thereby allowing access to models not natively supported in the local region. For example, claude- 3 - 5- sonnet is available in AWS US East 1 (N. Virginia), which could be accessed from US East (Ohio) via cross-region inference. Option C is 3-5 -sonnet correct as cross-region inference is explicitly not supported in U.S. SnowGov regions for either inbound or outbound inference requests. Option A is incorrect because user inputs, service generated prompts, and outputs are not stored or cached during cross-region inference. Option D is incorrect; latency depends on the cloud provider infrastructure and network status, and testing is recommended. Option E is incorrect because CORTEX_ENABLED_CROSS_REGION is an account-level parameter, not a session parameter.

NEW QUESTION # 24
A Snowflake administrator needs to configure Snowflake Copilot for a team distributed across different geographical regions, some of which are not natively supported for Copilot. Additionally, the team requires Copilot to adopt a specific tone in its responses. Which of the following correctly outlines the configuration steps for these requirements?

  • A. Option E
  • B. Option C
  • C. Option A
  • D. Option D
  • E. Option B
Answer: E
Explanation:


NEW QUESTION # 25
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