GES-C01퍼펙트최신버전덤프자료, GES-C01덤프자료

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그 외, PassTIP GES-C01 시험 문제집 일부가 지금은 무료입니다: https://drive.google.com/open?id=1RblKMtUSNoHKjU4n5rhEJ_KyPRovOury

PassTIP Snowflake인증GES-C01시험덤프 구매전 구매사이트에서 무료샘플을 다운받아 PDF버전 덤프내용을 우선 체험해보실수 있습니다. 무료샘플을 보시면PassTIP Snowflake인증GES-C01시험대비자료에 믿음이 갈것입니다.고객님의 이익을 보장해드리기 위하여PassTIP는 시험불합격시 덤프비용전액환불을 무조건 약속합니다. PassTIP의 도움으로 더욱 많은 분들이 멋진 IT전문가로 거듭나기를 바라는바입니다.

PassTIP에서 출시한 Snowflake인증 GES-C01덤프는 실제시험문제 커버율이 높아 시험패스율이 가장 높습니다. Snowflake인증 GES-C01시험을 통과하여 자격증을 취득하면 여러방면에서 도움이 됩니다. PassTIP에서 출시한 Snowflake인증 GES-C01덤프를 구매하여Snowflake인증 GES-C01시험을 완벽하게 준비하지 않으실래요? PassTIP의 실력을 증명해드릴게요.

>> GES-C01퍼펙트 최신버전 덤프자료 <<

GES-C01덤프자료 - GES-C01최고품질 인증시험 기출자료

비스를 제공해드려 아무런 걱정없이 GES-C01시험에 도전하도록 힘이 되어드립니다. PassTIP덤프를 사용하여 시험에서 통과하신 분이 전해주신 희소식이 PassTIP 덤프품질을 증명해드립니다.

최신 Snowflake Certification GES-C01 무료샘플문제 (Q138-Q143):

질문 # 138
A financial institution wants to leverage Snowflake Cortex Agents to build an AI application for complex financial analysis, requiring interaction with both their structured transaction databases and unstructured legal documents, while also ensuring intelligent decision- making throughout the process. Which of the following accurately describe the foundational capabilities of Snowflake Cortex Agents?

정답：C,D,E

설명：
Option B is correct because Cortex Agents orchestrate across structured and unstructured data sources, planning tasks and using tools like Cortex Analyst for structured data and Cortex Search for unstructured data. Option C is correct as 'Reflection' is a key component where the agent evaluates results after each tool use to determine next steps. Option E is correct because Cortex Agents allow the implementation of custom tools using stored procedures and user-defined functions (UDFs). Option A is incorrect; this describes Cortex Search, which is a tool that Cortex Agents can utilize, but not the primary, overarching capability of the agent itself. Option D is incorrect as this describes Cortex Fine-tuning, a separate capability for customizing LLMs, while agents use LLMs for orchestration.


질문 # 139

Which of the following SQL snippets, when executed against a single invoice file like "invoice001 .pdf", correctly extracts and transforms the desired data, assuming 'json_content' holds the raw Document AI output?

정답：D

설명：
Option B correctly uses a Common Table Expression (CTE) to retrieve the raw JSON output from (which is a Document AI method for extracting information from documents in a stage), leveraging to access the document. It then accesses the 'invoice_number' and 'vendor_name' using .value' syntax, appropriate for values returned as an array containing a single object with a 'value' field, as shown in Document AI output examples. The 'LATERAL FLATTEN' clause is correctly applied to expand the array of line items, and 'ARRAY_AGG' combined with 'ARRAY _ TO STRING' converts these items into a comma-separated string. Finally, it groups by the single-value extracted fields.
Option A attempts to flatten the result multiple times or in an incorrect way within the SELECT statement without a proper FROM' clause for the flattened data, leading to inefficient or incorrect aggregation. Option C directly references a staged file path (@invoice_docs_stage/invoice001.pdf) without the necessary GET PRESIGNED URL' function, which is required when calling '!PREDICT' with a file from a stage. It also incorrectly assumes direct .value' access for array-wrapped single values and does not correctly transform the 'invoice_itemS array into a string. Option D's subquery for 'ARRAY AGG' is syntactically problematic for direct column access from the outer query without explicit 'LATERAL FLATTEN' at the top level. Option E only extracts the 'ocrScore' from the document metadata and does not perform the requested data transformations.


질문 # 140
A data engineering team is onboarding a new client whose workflow involves extracting critical financial data from thousands of daily scanned PDF receipts. They decide to use Snowflake Document AI and store all incoming PDFs in an internal stage name. After deploying their pipeline, they observe intermittent failures and varying error messages in the output, specifically:

Which two of the following actions are most likely required to resolve these processing errors?

정답：C,D

설명：
The first error message, 'cannot identify image file', is a known error that occurs when an internal stage used for Document AI is not configured with 'SNOWFLAKE_SSE encryption. Therefore, option A is a direct solution. The second error message, 'Document has too many pages. Actual: 130. Maximum: 125.', indicates that some documents exceed Document AI's page limit of 125 pages per document. Option B directly addresses this limitation. Option C is incorrect because 'max_tokens' is relevant for LLM output length, not document input page/size limits. Option D is incorrect because scaling up the warehouse for Document AI does not increase query processing speed and is not recommended for cost efficiency; X-Small, Small, or Medium warehouses are typically sufficient for Document AI. Option E is incorrect because is the required database role for Document AI, not 'SNOWFLAKE.CORTEX_USER'.


질문 # 141
A data engineering team has developed a Python-based generative AI application and instrumented its key functions using the TruLens SDK. Their next step is to register this application with Snowflake AI Observability to initiate evaluation runs and capture application traces within Snowflake.

정답：B

설명：
To register a generative AI application in Snowflake for capturing traces and evaluations, a 'TruApp' object is created. The connector' parameter within 'TruApp' is a 'SnowflakeConnector' instance, specifically a wrapper class that manages the Snowpark session and Snowflake database connection to export traces to Snowflake. - Option A is incorrect because 'test_app' is an instance of the user-defined application, not responsible for managing the connection. - Option B is incorrect; is an arbitrary name for the application but the source does not state it dictates the name of an underlying table for traces. The event table contains logs, but its naming convention is not directly tied to app_name' in this manner. - Option D is incorrect because 'main_method' is optional if another method is instrumented with 'RECORD_ROOT. It doesn't state it's mandatory, and the responsibility for correct trace export lies with the 'connector'. - Option E is incorrect. 'app_version' is for experiment tracking and comparison, not for controlling the pricing model for evaluation runs. LLM judge costs are based on Cortex Complete function calls.


질문 # 142
A data science team is fine-tuning a Snowflake Document AI model to improve the extraction accuracy of specific fields from a new type of complex legal document. They are consistently observing low confidence scores and inconsistent 'value' keys for extracted entities, even after initial training. Which two of the following best practices should the team follow to most effectively improve the model's extraction accuracy and confidence for this complex document type?

정답：D,E

설명：
To improve Document AI model training, it is crucial to ensure that the documents uploaded for training represent a real use case and that the dataset consists of diverse documents in terms of both layout and data. If all documents contain the same data or are always presented in the same form, the model might provide incorrect results. For table extraction, it is vital that enough data is used to train the model to include ' NULC values and maintain order. Therefore, ensuring a diverse training dataset (Option B) is a key best practice. Additionally, Subject Matter Experts (SMEs) and document owners are crucial partners in understanding and evaluating the model's effectiveness in extracting the required information. Their involvement in defining data values, providing annotations, and evaluating results will significantly improve accuracy (Option C). Option A is not a best practice; it's recommended to keep questions as encompassing as possible and rely on training with annotations rather than complex prompt engineering, especially for document variability. Option D is incorrect; a higher 'temperature' value increases the randomness and diversity of the model's output, which is generally undesirable for accurate data extraction where deterministic results are preferred. For most consistent results, 'temperature' should be set to 0. Option E is incorrect because training on a restricted set of perfectly formatted documents can lead to a model that performs poorly on real-world, varied documents; diversity in training data is essential.


질문 # 143
......

PassTIP는 모든 IT관련 인증시험자료를 제공할 수 있는 사이트입니다. 우리PassTIP는 여러분들한테 최고 최신의 자료를 제공합니다. PassTIP을 선택함으로 여러분은 이미Snowflake GES-C01시험을 패스하였습니다. 우리 자료로 여러분은 충분히Snowflake GES-C01를 패스할 수 있습니다. 만약 시험에서 떨어지셨다면 우리는 백프로 환불은 약속합니다. 그리고 갱신이 된 최신자료를 보내드립니다. 하지만 이런사례는 거이 없었습니다.모두 한번에 패스하였기 때문이죠. PassTIP는 여러분이Snowflake GES-C01인증시험 패스와 추후사업에 모두 도움이 되겠습니다. Pass4Tes의 선택이야말로 여러분의 현명한 선택이라고 볼수 있습니다. Pass4Tes선택으로 여러분은 시간도 절약하고 돈도 절약하는 일석이조의 득을 얻을수 있습니다. 또한 구매후 일년무료 업데이트버전을 바을수 있는 기회를 얻을수 있습니다.

GES-C01덤프자료: https://www.passtip.net/GES-C01-pass-exam.html

PassTIP의Snowflake인증GES-C01자료는 제일 적중률 높고 전면적인 덤프임으로 여러분은 100%한번에 응시로 패스하실 수 있습니다, Snowflake GES-C01덤프자료 GES-C01덤프자료덤프는 PDF버전외에 온라인버전과 테스트엔진버전도 있는데 온라인버전은 휴대폰에서도 사용가능하고 테스트엔진버전은 PC에서 사용가능합니다, 다른 사람보다 빠르게 GES-C01 인증시험을 패스하여 자격증을 취득하고 싶은 분은 PassTIP 에서 출시한 GES-C01덤프로 시험준비를 하시면 됩니다, Snowflake GES-C01퍼펙트 최신버전 덤프자료 그래도 불행하게 시험에서 떨어지는 경우 주문번호와 불합격성적표를 메일로 보내오시면 바로 환불가능합니다.

다만, 그 이외에 모든 것들을 절제하고 있을 뿐이다, 무슨 일 없나 해서 잠시 둘러보러 왔습니다, PassTIP의Snowflake인증GES-C01자료는 제일 적중률 높고 전면적인 덤프임으로 여러분은 100%한번에 응시로 패스하실 수 있습니다.

100% 유효한 GES-C01퍼펙트 최신버전 덤프자료 공부자료

Snowflake Snowflake Certification덤프는 PDF버전외에GES-C01온라인버전과 테스트엔진버전도 있는데 온라인버전은 휴대폰에서도 사용가능하고 테스트엔진버전은 PC에서 사용가능합니다, 다른 사람보다 빠르게 GES-C01 인증시험을 패스하여 자격증을 취득하고 싶은 분은 PassTIP 에서 출시한 GES-C01덤프로 시험준비를 하시면 됩니다.

그래도 불행하게 시험에서 떨어지는 경우 주문번호와 불합격성적표를 메일로GES-C01덤프자료보내오시면 바로 환불가능합니다, PassTIP의 덤프들은 모두 전문적으로 IT관련인증시험에 대하여 연구하여 만들어진것이기 때문입니다.

그 외, PassTIP GES-C01 시험 문제집 일부가 지금은 무료입니다: https://drive.google.com/open?id=1RblKMtUSNoHKjU4n5rhEJ_KyPRovOury

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