[UPDATED 2026] Free Snowflake GES-C01 Exam Questions Self-Assess Preparation [Q101-Q125]

[UPDATED 2026] Free Snowflake GES-C01 Exam Questions Self-Assess Preparation

GES-C01 Free Sample Questions to Practice One Year Update

NEW QUESTION # 101
A Snowflake administrator needs to implement a granular access control strategy for LLMs. The general policy is to restrict access to a select few models via an account-level allowlist. However, a specific data science team (using role 'DATA SCIENCE TEAM ROLE) requires access to the 'claude-3-5-sonnet' model, which should not be available to other users or globally via the allowlist. Given this scenario, which set of commands would correctly establish this access control while adhering to the specified requirements?

• A.
• B.
• C.
• D.
• E.

Answer: D

Explanation:
Option A is correct. This sequence of commands sets an account-level allowlist for 'mistral-large? and 'snowflake-arctic' (restricting general access), and then explicitly grants the access to the 'claude-3-5-sonnet' model object using its dedicated application role 'SNOWFLAKE."CORTEX-MODEL-ROLE-CLAUDE-3-5-SONNET"'. The call is often necessary after 'ALTER ACCOUNT SET to ensure the changes are applied. Option B is incorrect because 'ALTER ACCOUNT requires the 'ACCOUNTADMIN' role, and setting to 'claude-3-5-sonnet' would make it globally available, contradicting the requirement. 'REVOKE APPLICATION ROLE' from 'PUBLIC' is not the correct mechanism to grant specific access. Option C is incorrect because model-level RBAC for base models in 'SNOWFLAKE.MODELS is primarily applied using application roles like 'CORTEX-MODEL-ROLE-', not directly with 'GRANT USAGE ON MODEL'. Option D is incorrect. While clearing the allowiist is possible, 'GRANT USAGE ON ALL MODELS IN SCHEMA SNOWFLAKE.MODELS' would grant access to 'all' models in that schema, which contradicts the requirement for 'claude-3-5-sonnet' to be exclusive to the data science team and not generally available. Option E is incorrect because 'ALTER ACCOUNT requires the ACCOUNTADMIN' role, and setting the allowlist to 'claude-3-5-sonnet' would make it generally available, violating the isolation requirement.

NEW QUESTION # 102

• A.
• B.
• C.
• D.
• E.

Answer: A,B,C,D,E

Explanation:

NEW QUESTION # 103
An operations team is investigating an issue with a generative AI application powered by Snowflake Cortex Analyst, where users reported unexpected behavior in generated SQL. To diagnose the problem, they examine the detailed event logs captured by Snowflake AI Observability. Which categories of information can they expect to find in these event tables to assist their investigation?

• A. The full text of the natural language questions submitted by the users.
• B. Any error messages or warnings that occurred during the processing of the request.
• C. The exact SQL queries that Cortex Analyst generated in response to user questions.
• D. Real-time CPU and memory usage statistics for the Snowflake virtual warehouse executing the LLM inference.
• E. The complete request and response bodies associated with the application's execution steps.

Answer: A,B,C,E

Explanation:
Cortex Analyst logs requests to an event table to aid in refining semantic models or views. These logs are comprehensive and include specific details crucial for debugging and monitoring. The captured information includes ' ''The user who asked the question'", ' ''The question asked Generated SQL Errors and/or warnings Request and response and 'Other metadata'. Therefore, options A, B, C, and D are all accurate descriptions of the data available in these event logs. Option E, real-time CPU and memory usage, refers to infrastructure monitoring metrics rather than the content specifically logged within the application's execution event table by Cortex Analyst itself.

NEW QUESTION # 104
A retail company wants to implement an automated data pipeline in Snowflake to analyze daily customer reviews. The goal is to enrich a 'product_reviews_sentiment' table with sentiment categories (e.g., 'positive', 'neutral', 'negative') for each new review. They require the sentiment to be returned as a JSON object for downstream processing and need the pipeline to handle potential LLM errors gracefully without stopping. Assuming a stream 'new reviews_stream' monitors a 'customer _ reviews' table, which approach effectively uses a Snowflake Cortex function for this scenario?

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

Answer: C

Explanation:
Option C is the most effective approach for this scenario. It correctly uses "SNOWFLAKE.CORTEX.TRY COMPLETE", which performs the same operation as 'COMPLETE but returns NULL instead of raising an error when the operation cannot be performed, making the pipeline more robust to LLM issues. The 'response_format option ensures the output adheres to a specified JSON schema for structured sentiment categories, meeting the requirement for structured output. This is integrated within a 'MERGE statement in a task for incremental processing of new data from Option A suggests a Python UDF with 'COMPLETE. While feasible, 'TRY_COMPLETE is explicitly designed for graceful error handling in pipelines, which 'COMPLETE lacks by default. Option B uses 'SNOWFLAKE.CORTEX.SENTIMENT , which returns a numeric score (e.g., 0.5424458), not a categorical JSON object, requiring additional post-processing logic for categorization. Option D uses for summarization and 'AI CLASSIFY for classification. While 'AI_CLASSIFY can categorize, the request is for sentiment of 'each' review, and 'AI_AGG' would aggregate before classifying, not fulfilling the individual review sentiment requirement. Option E suggests a dynamic table, but dynamic tables currently do not support incremental refresh with 'COMPLETE (or 'AI_COMPLETE) functions, making them unsuitable for continuous LLM-based processing in this manner. Furthermore, 'COMPLETE does not offer the graceful error handling of 'TRY COMPLETE'.

NEW QUESTION # 105
A data application developer is building a Streamlit chat application within Snowflake. This application uses a RAG pattern to answer user questions about a knowledge base, leveraging a Cortex Search Service for retrieval and an LLM for generating responses. The developer wants to ensure responses are relevant, concise, and structured. Which of the following practices are crucial when integrating Cortex Search with Snowflake Cortex LLM functions like AI_COMPLETE for this RAG chatbot?

• A. The
  
• B. Using the
• C. The retrieved context from Cortex Search should be directly concatenated with the user's prompt as input to the
• D. To maintain conversational context in a multi-turn chat, the developer should pass all previous user prompts and model responses in the
• E. For performance and cost optimization, it is always recommended to query Cortex Search and the LLM function within a single

Answer: B,D

Explanation:

NEW QUESTION # 106
A data engineer has successfully experimented with a prompt and various model settings in the Snowflake Cortex Playground for a text classification task using the mistral-large2 model and Cortex Guard. They now want to operationalize this solution within their Snowflake environment. Which of the following statements correctly describe capabilities or considerations when moving from the Cortex Playground to a production pipeline?

• A. For continuous processing of new data, the exported SQL query can be automated using
• B. If the mistral-large2 model is not natively available in the target production region, cross-region inference must be enabled by setting the CORTEX_ENABLED_CROSS REGION parameter.
• C. The exported SQL query, when used with dynamic tables, supports incremental refresh for efficient processing of new data without recomputing the entire table.
• D. The Playground allows exporting the exact SQL query with all defined model settings, including temperature and Cortex Guard enablement, for direct use in a Snowflake worksheet or task.
• E. To filter unsafe LLM responses in production, the Cortex Guard option, which is built with Meta's Llama Guard 3, must be explicitly enabled in the COMPLETE function's options argument.

Answer: A,B,D,E

Explanation:
The Cortex Playground offers the capability to export the exact SQL query, including specified settings like 'temperature' and whether Cortex Guard is enabled. This exported SQL can be directly used in Snowflake worksheets, notebooks, or automated via streams and tasks for continuous execution. Cortex Guard is a feature of the (or ' COMPLETE) function, built with Meta's Llama Guard 3, and needs to be explicitly enabled using the 'guardrails' option to filter unsafe responses. Automation with streams and tasks is a valid approach for document processing pipelines using 'PREDICT' and 'COMPLETE' functions. If a model like 'mistral-large? is not natively available in a specific Snowflake region, cross-region inference must be enabled for Cortex LLM functions. However, dynamic tables do not currently support incremental refresh when using the 'COMPLETE' function.

NEW QUESTION # 107
An operations team at a company is implementing a robust governance framework to monitor and optimize the costs associated with their Snowflake Cortex LLM function usage. They need to identify which functions are driving the highest token consumption and overall credit usage to pinpoint areas for cost reduction. Which of the following monitoring tools or methods are appropriate for gaining these insights into Cortex LLM function costs and token consumption?

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

Answer: B,C,D,E

Explanation:

NEW QUESTION # 108
A data team has implemented a Snowflake data pipeline using SQL tasks that process customer call transcripts daily. This pipeline relies heavily on SNOWFLAKE. CORTEX. COMPLETE() (or its updated alias) for various text analysis tasks, such as sentiment analysis and summary generation. Over time, they observe that the pipeline occasionally fails due to LLM-related errors, and the compute costs are higher than anticipated. What actions should the team take to improve the robustness and cost-efficiency of this data pipeline? (Select all that apply.)

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

Answer: C,D,E

Explanation:
A). ''Correct." performs the same operation as 'COMPLETE(Y but returns 'NULC on failure instead of raising an error when the LLM operation cannot be performed. This is critical for building robust data pipelines, as it prevents pipeline halts due to transient or specific LLM failures. B. ''Incorrect." Snowflake recommends executing queries that call Cortex AISQL functions with a smaller warehouse (no larger than MEDIUM). Larger warehouses do not necessarily increase performance but can lead to unnecessary costs, as the LLM inference itself runs on Snowflake-managed compute, not solely on the user's virtual warehouse compute size. C. "Correct." The 'SNOWFLAKE.ACCOUNT USAGE.CORTEX_FUNCTIONS USAGE HISTORY view provides detailed information on token consumption and credit usage for Snowflake Cortex LLM functions. Monitoring this view is essential for understanding cost drivers and optimizing expenditure within AI pipelines. D. ' 'Correct." Snowflake Cortex AI functions incur compute costs based on the number of tokens processed (both input and output). Optimizing prompt engineering to be concise and effective directly contributes to reducing the number of tokens consumed and, therefore, the associated costs. E. "Incorrect." Setting the 'temperature' parameter to 1.0 makes the LLM's output more diverse and random. While useful for creativity, it does not guarantee a reduction in token usage or a lower error rate. For the most consistent results, setting 'temperature' to 0 is generally recommended.

NEW QUESTION # 109
A data scientist has successfully deployed a Hugging Face sentence transformer model to Snowpark Container Services (SPCS) for GPU-powered inference, making it accessible via an HTTP endpoint. To ensure secure and proper programmatic access to this service from an external application, which of the following statements correctly describe the authentication and access control considerations for calling this public endpoint?

• A. The 'Authorization' header with a 'Snowflake Token=""' value is a valid method for authenticating requests to the public endpoint programmatically.
• B. The public endpoint of the SPCS service can be accessed directly without any authentication, as it's a public endpoint.
• C. The default role for the calling user must have the 'SNOWFLAKCORTEX USER database role granted to access the SPCS service via its public endpoint.
• D. The Python API for calling the service requires the Snowflake session object directly, bypassing HTTP endpoint authentication.
• E. Applications must use key pair authentication to generate a JSON Web Token (JWT), exchange it with Snowflake for an OAuth token, and then use this OAuth token to authenticate requests to the public endpoint.

Answer: A,E

Explanation:
When using a model deployed to SPCS via its public HTTP endpoint, applications authenticate requests using key pair authentication to generate a JSON Web Token (JWT), then exchange the JWT for an OAuth token, which is subsequently used for authentication. An example shows generating headers including authorization using a session token, formatted as 'Authorization: Snowflake Token=""'. Therefore, options B and D are correct. Option A is incorrect because public endpoints still require authentication. Option C describes internal access via the Snowpark Python API, not external HTTP endpoint authentication. Option E, while relevant for Snowflake Cortex LLM functions accessed via the REST API generally, is not the primary mechanism for authenticating to a SPCS public HTTP endpoint, which uses tokens derived from key pair authentication or session tokens.

NEW QUESTION # 110
A financial services company is developing an automated data pipeline in Snowflake to process Federal Reserve Meeting Minutes, which are initially loaded as PDF documents. The pipeline needs to extract specific entities like the FED's stance on interest rates ('hawkish', 'dovish', or 'neutral') and the reasoning behind it, storing these as structured JSON objects within a Snowflake table. The goal is to ensure the output is always a valid JSON object with predefined keys. Which AI_COMPLETE configuration, used within an in-line SQL statement in a task, is most effective for achieving this structured extraction directly in the pipeline?

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

Answer: C

Explanation:
To ensure that LLM responses adhere to a predefined JSON structure, the 'AI_COMPLETE function's 'response_format' argument, which accepts a JSON schema, is the most effective and direct method. This mechanism enforces the structure, data types, and required fields, significantly reducing the need for post-processing and ensuring deterministic, high-quality output. The AI-Infused Data Pipelines with Snowflake Cortex blog highlights asking the LLM to create a JSON object for maximizing utility. While setting 'temperature' to 0 can improve consistency, it does not enforce a specific schema. Prompt engineering (Option A) can help but does not guarantee strict adherence. Using multiple extraction calls (Option D) is less efficient and robust for extracting multiple related fields than a single 'AI_COMPLETE call with a structured output schema. Snowflake Cortex does not automatically infer and enforce a JSON schema without explicit configuration (Option E).

NEW QUESTION # 111
A multinational corporation is implementing Document AI to automate the processing of purchase orders from various global suppliers. These purchase orders vary significantly in layout and are often submitted in English, German, and Spanish. The data engineering team aims to optimize the preparation phase for effective model training and deployment. Considering Document AI's 'Question optimization best practices' and general document preparation guidelines, which of the following is a 'critical consideration' for successful implementation?

• A. Document AI model builds can only support a single document layout type; therefore, separate model builds must be created for each distinct purchase order layout from different suppliers.
• B. To simplify prompt engineering, define generic questions such as 'What is the total amount?' irrespective of document layout, as Document AI's foundation model is expected to handle most layout variations through its zero-shot capabilities.
• C. For maximum efficiency in defining data values for complex extractions (e.g., lists of line items), prioritize spending extensive time on crafting highly precise and detailed natural language prompts to guide the model.
• D. The training dataset should be highly diverse, representing various layouts, data variations (including potential NULLs), and containing documents in all target languages (English, German, Spanish) to ensure robust model performance.
• E. For multilingual support, it is mandatory to externally translate all non-English purchase orders to English before uploading them to an internal stage, as

Answer: D

Explanation:
Optimizing Document AI involves careful preparation of both the questions and the training data. - ' 'Option ' is incorrect. While Document AI formally supports English, it also supports processing documents in Spanish, French, German, Portuguese, Italian, and Polish, especially if the model is trained appropriately. External translation is not mandatory; questions should be written in English, using native terms when appropriate, and the model trained for the specific language documents. - ''Option B" is correct. To improve model training, it is crucial that the uploaded documents represent a real use case, and the dataset consists of diverse documents in terms of both layout and data. This includes variation in information, not just all documents containing the same data or presented in the same form. When dealing with multilingual documents, training the model appropriately with diverse language examples is key for successful extraction. - "Option is incorrect. Document AI best practices emphasize specificity in questions. While zero-shot capabilities exist, relying on generic questions for diverse layouts without additional training or specificity can lead to inaccurate results. - "Option is incorrect. For complex extractions like lists of line items, the best practice is 'Show, don't tell'. This means showing the expected result through annotations and fine-tuning across the training set, rather than relying solely on elaborate prompt engineering. - ' 'Option is incorrect. Document AI model builds are designed to handle the entire range and diversity of document types for a specific use case, not just a single layout. The goal is often to have a single model for a document type (e.g., invoices) even with layout variations. Training with diverse documents, including different layouts, is a best practice.

NEW QUESTION # 112
An administrator has configured the 'CORTEX MODELS ALLOWLIST parameter to only permit the 'mistral-large? model at the account level. A user with the 'PUBLIC' role, which has been granted 'SNOWFLAKE.CORTEX USER and 'SNOWFLAKE."CORTEX- MODEL-ROLE-LLAMA3.1-70B"' , attempts to execute several 'COMPLETE (SNOWFLAKE.CORTEX)' queries. Which of the following queries will successfully execute?

• A.
• B.
• C.
• D.
• E.

Answer: D,E

Explanation:
Option A is correct because the query directly references 'MISTRAL-LARGE2', which is explicitly in the account-level , so it will succeed. Option B is correct because when a model name is provided as a fully-qualified string argument (e.g., Cortex treats it as an identifier for a schema-level model object. If found, role-based access control (RBAC) is applied. The user's role has granted, which provides access to the 'LLAMA3.1-70B' model object in 'SNOWFLAKE.MODELS, regardless of the setting for plain model names. Option C is incorrect. When 'llama3.1-70b' is provided as a plain model name string, Cortex first attempts to match it as a schema-level model object (e.g., 'SNOWFLAKE.MODELS.llama3.1-70b). If this direct match fails (which is typical for a plain, unquoted name not exactly matching a model object identifier), it then checks the plain name 'llama3.1-70b' against the 'CORTEX MODELS ALLOWLIST. Since 'llama3.1- 70b' is not in the allowlist, this query will fail, even though the user has access to the fully-qualified model object. Option D is incorrect as 'snowflake-arctic' is neither in the 'CORTEX MODELS ALLOWLIST' nor does the user have a specific application role granting access to a 'snowflake-arctic' model object. Option E is incorrect because 'ALTER ACCOUNT operations can only be performed by the 'ACCOUNTADMIN' role, not by a user with the FUBLIC' role.

NEW QUESTION # 113
A developer has successfully created a Cortex Search Service named transcript _ search service within cortex_search_db. services based on customer support transcripts. They now need to query this service to find support tickets related to 'internet issues' specifically from the 'North America' region, and they only want the single most relevant result. Which of the following SQL commands correctly performs this query?

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

Answer: D

Explanation:
Option A is the correct syntax for querying a Cortex Search Service using the SEARCH PREVIEW function, as demonstrated in the sources. It correctly specifies the service name, query, columns to retrieve, filter condition using the operator, and the limit, all within a JSON @eq string. Options B, C, D, and E use incorrect function names, parameter formats, or JSON structures for the SEARCH PREVIEW function as defined in the provided documentation.

NEW QUESTION # 114
A compliance officer is reviewing the usage of Snowflake Cortex LLM functions and the Cortex REST API within their organization, specifically focusing on the implementation and impact of Cortex Guard. They observe several instances where 'guardrails' were enabled. Which of the following statements accurately describe the behavior and cost considerations of Cortex Guard when integrated with Snowflake Cortex LLM functions or the Cortex REST API?

• A. Cortex Guard operates by evaluating responses after the LLM has fully generated its content, and it incurs additional compute cost for both input and output tokens during its processing.
• B. Cortex Guard can be configured with a custom message using the argument in the options object for both 'COMPLETE SQL function and the Cortex REST API.
• C. Cortex Guard is inherently part of all Cortex LLM functions and does not require explicit enablement via 'guardrails: TRUE for SQL functions or the REST API.
• D. When Cortex Guard is enabled and a response is deemed unsafe, the LLM-generated output is replaced with a predefined message, and only the input tokens for Cortex Guard processing ('guard_tokens') are billed, not the potentially unsafe completion tokens.
• E. The underlying model for Cortex Guard is Meta's Llama Guard 3, and its processing costs are separate from the primary LLM inference costs.

Answer: B,D,E

Explanation:
Option B is correct: when Cortex Guard is enabled and a response is blocked, the model's output is replaced by a message (defaulting to 'Response filtered by Cortex Guard'), and only 'guard_tokens' are counted as input tokens for Cortex Guard's processing, in addition to the primary LLM's prompt and completion tokens. Option C is correct as the argument allows customization of the filtered response message for both 'COMPLETE' and the REST API. Option E is correct because Cortex Guard is built with Meta's Llama Guard 3 and its usage is billed separately as 'guard_tokens' in addition to the 'COMPLETE function cost. Option A is incorrect because while guard_tokens' are billed, it's specifically for the guardrail processing, and the 'unsafe' completion tokens are not returned or billed as such, rather replaced by a filtered message. Option D is incorrect because Cortex Guard requires explicit enablement by setting 'guardrails' to 'TRUE'.

NEW QUESTION # 115
A Snowflake user attempts to access the Cortex Playground to experiment with LLM functions but receives an error indicating insufficient privileges. They have an active Snowflake session. Which of the following steps must be taken to ensure they can successfully use the Cortex Playground?

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

Answer: D,E

Explanation:

NEW QUESTION # 116
A data application developer, adhering to Snowflake's Gen AI best practices for deploying LLMs, needs to perform inference with a newly fine-tuned llama3.1-70b model via AI_COMPLETE and expects a structured JSON output. Which of the following statements accurately describe how to configure this inference and potential limitations within Snowflake Cortex?

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

Answer: C,E

Explanation:
To use a fine-tuned model for inference, you can call the 'COMPLETE' (or 'AI_COMPLETE') LLM function with the name of your fine-tuned model. For structured output, you can specify a JSON schema using the 'response_format argument with 'AI_COMPLETE. For OpenAI (GPT) models, the 'additionalProperties' field must be set to and the 'required' field must contain the names of every property in the schema. For the most consistent results, setting the 'temperature' option to 0 when calling 'COMPLETE (or 'AI_COMPLETE) is recommended. JSON schema guidelines also state that object definitions should be placed at the top level of the schema, specifically under the 'definitions' or '$defs key, and Snowflake's validation strictly enforces this structure. Cortex LLM functions do not support dynamic tables. While fine-tuned models appear in the Snowsight UI of the Model Registry, they are not managed by the Model Registry API. Usage of Cortex functions can be tracked using views like "CORTEX_FUNCTIONS_QUERY_USAGE_HISTORY', but this is for tracking costs/usage, not for real-time model monitoring in the context of Model Registry.

NEW QUESTION # 117
A data scientist fine-tuned a mistral-lb model in Snowflake for a specific customer support response generation task, naming it my_custom_responder_model. They now want to make this model available for AI_COMPLETE calls in production, ensuring proper access control and regional availability. Which of the following statements is true regarding the deployment and management of this fine-tuned model in Snowflake?

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

Answer: C

Explanation:

NEW QUESTION # 118
A data engineer is designing a new feature for a Retrieval Augmented Generation (RAG)-based application in Snowflake. They plan to store document embeddings and perform semantic similarity searches to retrieve relevant context for an LLM. Which of the following statements about using the VECTOR data type and related functions in Snowflake are true? (Select all that apply.)

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

Answer: B,C,D

Explanation:

NEW QUESTION # 119
An operations team is investigating an issue with a generative AI application powered by Snowflake Cortex Analyst, where users reported unexpected behavior in generated SQL. To diagnose the problem, they examine the detailed event logs captured by Snowflake AI Observability. Which categories of information can they expect to find in these event tables to assist their investigation?

• A. The full text of the natural language questions submitted by the users.
• B. Any error messages or warnings that occurred during the processing of the request.
• C. The exact SQL queries that Cortex Analyst generated in response to user questions.
• D. Real-time CPU and memory usage statistics for the Snowflake virtual warehouse executing the LLM inference.
• E. The complete request and response bodies associated with the application's execution steps.

Answer: A,B,C,E

Explanation:
Cortex Analyst logs requests to an event table to aid in refining semantic models or views. These logs are comprehensive and include specific details crucial for debugging and monitoring. The captured information includes 'The user who asked the question', 'The question asked', 'Generated SQL', 'Errors and/or warnings', 'Request and response bodies', and 'Other metadata'. Therefore, options A, B, C, and D are all accurate descriptions of the data available in these event logs. Option E, real-time CPU and memory usage, refers to infrastructure monitoring metrics rather than the content specifically logged within the application's execution event table by Cortex Analyst itself.

NEW QUESTION # 120
A legal department uses Snowflake to manage and review large volumes of contracts. They need to automate the process of finding specific pieces of information, such as the effective_date or involved_parties, from these unstructured contract texts. They are considering using SNOWFLAKE. CORTEX. EXTRACT_ANSWER. Which characteristic correctly describes the primary intent or behavior of SNOWFLAKE. CORTEX. EXTRACT_ANSWER, distinguishing it from other LLM functions?

• A. It evaluates a text and returns a numeric score indicating the overall positive or negative sentiment.
• B. It is primarily designed to generate entirely new text based on a given prompt, much like a conversational AI.
• C. It transforms an input document into a summarized version, reducing its length while preserving key information.
• D. It focuses on identifying and returning a direct answer to a specific question contained within the provided source document.
• E. It classifies an input text into one of several predefined categories provided by the user.

Answer: D

Explanation:
Option D is correct. The 'SNOWFLAKE.CORTEX.EXTRACT_ANSWER function is specifically designed to extract an answer to a given question from a text document. Option A describes the 'COMPLETE function. Option B describes the 'SENTIMENT function. Option C describes the 'CLASSIFY_TEXT function. Option E describes the 'SUMMARIZE function.

NEW QUESTION # 121
A developer is building a prototype Gen AI application using the Snowflake Cortex LLM REST API. During testing, they frequently encounter HTTP 429 'Too Many Requests' errors, particularly when rapidly invoking the 'COMPLETE endpoint. Which of the following statements accurately describes the usage quotas and troubleshooting steps for such rate limit events in the Cortex REST API?

• A. If the usage is below the RPM limit but still triggers a 429, the developer should review the token usage rate as TPM limits might be exceeded.
• B. Rate limits are model-specific, and exceeding either the Tokens Processed per Minute (TPM) or Requests per Minute (RPM) limits will result in an HTTP 429 response code.
• C. To resolve rate limiting, the developer should increase the virtual warehouse size for their API requests, as larger warehouses automatically increase API quotas.
• D. Cortex REST API rate limits are primarily based on Requests per Minute (RPM) and do not consider the total Tokens Processed per Minute (TPM).
• E. The HTTP 429 response indicates that the daily credit consumption for Snowflake Cortex AISQL usage has exceeded the 10-credit limit for on-demand accounts without a valid payment method, requiring conversion to a paid account.

Answer: A,B

Explanation:
Option C is correct because the Cortex REST API implements rate limits for both Tokens Processed per Minute (TPM) and Requests per Minute (RPM), and offending either of these limits will result in a 429 response code. Option D is correct as the sources explicitly state that if REST API usage is below the RPM limit but still receives a 429, the user should double-check the token usage rate, implying that TPM limits could be the cause. Option A is incorrect because while on-demand Snowflake accounts without a valid payment method are limited to 10 credits per day, an HTTP 429 specifically refers to rate limiting due to high usage, not a daily credit budget exceeding for the account type. Option B is incorrect as Cortex REST API rate limits consider both TPM and RPM. Option E is incorrect because Cortex REST API requests do not require a warehouse, and increasing warehouse size does not directly increase API quotas or resolve rate limiting for the REST API.

NEW QUESTION # 122
A global marketing team uses Snowflake to manage customer feedback in various languages. They need to translate customer reviews from German ("de") into English ("en") for analysis. The reviews are stored in a table named 'CUSTOMER REVIEWS' in a column called 'REVIEW TEXT'. Which of the following SQL statements correctly applies the 'SNOWFLAKE.CORTEX.TRANSLATE function and what is the expected return type for the translated text?

• A. The query
  
• B. The query
  
• C. The query
  
• D. The query
  
• E. The query
  

Answer: C

Explanation:
Option B is correct. The 'SNOWFLAKE.CORTEX.TRANSLATE function takes three arguments: the text to be translated, the source language, and the target language. It returns a STRING value containing the translated text. Option A includes an unsupported 'high_accuracy' option and claims an incorrect return type. Option C uses an incorrect syntax and claims an incorrect return type. Option D uses an incorrect number of arguments. Option E claims an incorrect return type.

NEW QUESTION # 123
A financial institution uses Snowflake to process an average of 5 million PDF documents (average 10 pages each) monthly for regulatory compliance using AI_PARSE_DOCUMENT in LAYOUT mode. They are monitoring costs and considering performance optimizations. Given this scenario, which of the following statements about cost and performance for AI_PARSE_DOCUMENT are true?

• A. For optimal results with multilingual documents, the LAYOUT mode supports a broader range of languages than OCR mode.
• B. If a document is a HTML file, the billing is based on processing each chunk of 3,000 characters as a page.
• C. Selecting a 'LARGE' or 'X-LARGE' standard warehouse will significantly improve the processing speed and reduce the per-page cost for AI_PARSE_DOCUMENT.
• D. The primary billing metric for in LAYOUT mode is the number of tokens processed, which includes both input and output tokens.
• E. To reduce costs, the team should aim to process documents in batches, ensuring each query processes a maximum of 1 ,000 documents to avoid errors.

Answer: A,B,E

Explanation:
Option A is incorrect. For 'AI_PARSE_DOCUMENT , billing is based on the number of document pages processed, not tokens. Specifically, LAYOUT mode costs 3.33 Credits per 1 ,000 pages. Option B is correct. Document AI supports processing a maximum of 1 ,000 documents in one query. Processing documents in batches up to this limit is a recommended practice to avoid errors and optimize processing. Option C is incorrect. Snowflake recommends executing queries that call with a smaller warehouse (no larger than MEDIUM) because larger warehouses do not increase performance. Option D is correct. LAYOUT mode for supports 12 languages (English, French, German, Hindi, Italian, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian), while OCR mode supports 10 languages (English, French, German, Italian, Norwegian, Polish, Portuguese, Spanish, Swedish, Chinese), thus mode supports a broader LAYOUT range of languages. Option E is correct. For HTML and TXT files, each chunk of 3,000 characters is billed as a page, including the last chunk, which may be less than 3,000 characters.

NEW QUESTION # 124

• A.
• B.
• C.
• D.
• E.

Answer: E

Explanation:

NEW QUESTION # 125
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