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[General] GES-C01 Valid Braindumps Questions | GES-C01 Exam Course

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【General】 GES-C01 Valid Braindumps Questions | GES-C01 Exam Course

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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q201-Q206):NEW QUESTION # 201
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. CHANGE_TRACKING
  • B. For embedding text, selecting a model like
  • C. The
  • D. The primary cost driver for Cortex Search is the number of search queries executed against the service, with the volume of indexed data (GBImonth) having a minimal impact on overall billing.
  • E. For optimal performance and cost efficiency, Snowflake recommends using a dedicated warehouse of size no larger than MEDIUM for each Cortex Search Service.
Answer: A,B,C,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-vl .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-vl 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 # 202
An organization enforces strict LLM access control. The has configured 'CORTEX MODELS_ALLOWLIST ='mistral-large2" and executed 'CALL SNOWFLAKE.MODELS.CORTEX BASE MODELS REFRESH()'. A developer, whose role DEV ROLE has been granted 'SNOWFLAKE.CORTEX USER' and 'SNOWFLAKE."CORTEX-MODEL-ROLE-CLAUDE-3-5- SONNET"' , attempts to make a REST API call to 'api/v2/cortex/inference:complete' using 'claude-3-5-sonnet' as the model name in the request body. Which of the following statements are true regarding this scenario?

  • A. Option C
  • B. Option A
  • C. Option D
  • D. Option B
  • E. Option E
Answer: A,D
Explanation:
Option A is incorrect. When the model is specified as a plain string '"claude-3-5-sonnet"' in the REST API request, Cortex first attempts to match it as a schema-level model object by that plain name. If this fails (as is typical for built-in models which often use quoted identifiers like "'CLAUDE-3-5-SONNET"'), it then checks the plain name against the 'CORTEX_MODELS_ALLOWLIST. Since 'claude-3-5- sonnet' is not in the allowlist, the call will be denied despite the user's role having the application role for the model object 'SNOWFLAKE.MODELS."CLAUDE-3-5-SONNET"'. option B is correct because the parameter explicitly limits which models can be used by the 'Cortex LLM REST API'. Option C is correct. To use 'claude-3-5-sonnet' via the REST API, it either needs to be added to the account-level as a plain name, or the request must explicitly use the fully-qualified object identifier 'SNOWFLAKE.MODELS."CLAUDE-3-5-SONNET"' which would then be subject to RBAC on that model object. Option D is incorrect. An HTTP '403 Not Authorized' error typically indicates that the account is not enabled for the REST API or the calling user's default role lacks the 'SNOWFLAKE.CORTEX USER database role. For 'AI_COMPLETE (which 'COMPLETE' maps to), an error related to an unallowed model would contain information about how to modify the allowlist, implying a different error message than a generic '403'. Option E is incorrect. The 'ENABLE CORTEX ANALYST MODEL AZURE_OPENAF parameter is specifically for Cortex Analyst and determines if it can use Azure OpenAl models, but it is incompatible with model-level RBAC for Cortex Analyst. It does not affect generic 'COMPLETE (SNOWFLAKE.CORTEX)' REST API calls.

NEW QUESTION # 203
A security administrator needs to manage access to Snowflake Cortex LLM functions and models, and ensure compliant usage across different regions for various teams. Which configuration options and parameters are relevant for these requirements?

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


NEW QUESTION # 204
A team is building a critical Document AI pipeline for continuous processing of new financial reports. They've identified that occasionally, the 'GET_PRESIGNED_URL' generated for specific documents expires before the '!PREDICT function can successfully process them, resulting in HTTP 403 errors. To mitigate this, they plan to modify their SQL query logic.
Which approach effectively addresses the presigned URL expiration issue without altering the stage definition or the model build itself, and adheres to recommended practices for handling batch processing as described in Snowflake's troubleshooting documentation?
  • A.
  • B.
  • C.
  • D.
Answer: C
Explanation:
The core problem is the expiration of presigned URLs when processing documents with S!PREDICT , which defaults to 60 minutes. Snowflake's troubleshooting documentation specifically recommends 'Use several queries to process the documents' as a solution for 'Presigned URL has expired'. This implies breaking down the workload into smaller, more manageable batches to ensure that the processing for each batch completes within the URL's active lifespan. Option A is incorrect as when used with a stage and in the context of does not directly support an 'expiration_time' parameter within the provided syntax examples. Option B describes a task that processes new documents in batches, which is a practical implementation of the recommended solution (using several queries), but option D describes the underlying recommended strategy more broadly and accurately as per the documentation's troubleshooting guidance. Option C attempts to filter documents by modification time, which doesn't directly prevent a URL from expiring if the subsequent processing is slow. Option E modifies 'COPY OPTIONS' which is irrelevant to '!PREDICT query errors for Document AI.

NEW QUESTION # 205
A data analyst is tasked with identifying customers who purchased items with similar feature vectors. They have a table products with an

to measure similarity. Which of the following statements correctly describe aspects of defining and using vector types or functions in this scenario? (Select all that apply)
  • A. Comparing two
  • B. To correctly define a column to store 768-dimensional float embeddings, the SQL statement
  • C. When inserting literal arrays as vectors into a table for comparison, explicit casting, e.g.,
  • D. The Snowpark Python library provides native support for calling
  • E. If
Answer: B,C,D
Explanation:
Option A is correct. The syntax for specifying a

) are byte-wise lexicographic and do not produce semantically expected results for number comparisons; dedicated vector similarity functions should be used instead. Option D is correct. The Snowpark Python library supports the VECTOR data type and vector similarity functions. While the sources specifically mention VECTOR_L2_DISTANCE in a Snowpark Python example, VECTOR_L1_DISTANCE is listed as one of the four core vector similarity functions provided by Snowflake Cortex, implying similar support in Snowpark Python. Option E is correct. SQL examples demonstrate the necessity of explicit casting when using array literals as vectors, such as .


NEW QUESTION # 206
......
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