GES-C01 Fragen Und Antworten & GES-C01 Prüfungsvorbereitung

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GES-C01 Fragen Und Antworten, GES-C01 Prüfungsvorbereitung, GES-C01 Antworten, GES-C01 Ausbildungsressourcen, GES-C01 Testking

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Snowflake SnowPro® Specialty: Gen AI Certification Exam GES-C01 Prüfungsfragen mit Lösungen (Q136-Q141):

136. Frage
A development team is building an AI-powered data pipeline in Snowflake. The pipeline involves extracting text from documents, generating embeddings using

,and then performing similarity searches using

to find related documents. They plan to manage this pipeline using Snowflake tasks and want to integrate with a Python application for some custom processing. Considering this scenario, which of the following statements about implementing this pipeline are true?

  • A. To generate document embeddings, the
  • B. Snowflake
  • C.
  • D. If the team wants to use the Snowpark Python library to call
  • E. When using Snowflake tasks to automate the embedding generation and similarity search,

Antwort: C

Begründung:
Option A is incorrect. Snowflake recommends executing queries that call Cortex AI SQL functions like EMBED_TEXT_768 with a smaller warehouse (no larger than MEDIUM), as larger warehouses do not increase performance. Snowpark-optimized warehouses are recommended for workloads with large memory requirements or specific CPU architectures, typically for ML training, not for general Cortex AI function calls. Option B is incorrect. The Snowpark Python library explicitly states that it does not support the VECTOR_COSINE_SIMILARITY function, meaning it does not 'fully support all vector similarity functions'. Option C is incorrect. The VECTOR data type is not supported as clustering keys. Option D is correct. After generating embeddings (e.g., storing them in a VECTOR column like issue vec ), vector similarity functions can be effectively used in SQL queries with ORDER BY and LIMIT clauses to retrieve the most similar results, as demonstrated with VECTOR_COSINE_SIMILARITY in a RAG example. This pattem applies to VECTOR_INNER_PRODUCT as well. Option E is incorrect. The VECTOR data type is not supported for use with dynamic tables. Additionally, Snowflake Cortex functions (including EMBED_TEXT_768 ) do not support dynamic tables.


137. Frage
A data processing team is using Snowflake Document AI to extract data from incoming supplier invoices. They observe that many documents are failing to process, and successful extractions are taking longer than expected, leading to increased costs. Upon investigation, they find error messages such as

. Additionally, their 'X-LARGE virtual warehouse is constantly active, contributing to higher-than-anticipated bills. Which two of the following actions are essential steps to troubleshoot and address the root causes of these processing errors and optimize their Document AI pipeline?

  • A. Implement a pre-processing step to split documents exceeding 125 pages or 50 MB into smaller, compliant files before loading to the stage.
  • B. Increase the 'max_tokens' parameter within the '!PREDICT' function options to accommodate longer document responses from the model.
  • C. Scale down the virtual warehouse to 'X-SMALL' or 'SMALL' size, as larger warehouses do not increase Document AI query processing speed and incur unnecessary costs.
  • D. Configure the internal stage used for storing invoices with 'ENCRYPTION = (TYPE = 'SNOWFLAKE SSE')'.
  • E. Redefine extraction questions to be more generic and encompassing, reducing the number of distinct questions needed per document.

Antwort: A,D

Begründung:
The error messages 'Document has too many pages. Actual: 130. Maximum: 125.' and File exceeds maximum size. Actual: 54096026 bytes. Maximum: 50000000 bytes: directly indicate that the documents do not meet Document AI's input requirements, which specify a maximum of 125 pages and 50 MB file size. Therefore, implementing a pre-processing step to split or resize these documents is an essential solution (Option B). The error 'cannot identify image file < io.ByteslO object at Ox...>' is a known issue that occurs when an internal stage used for Document AI is not configured with encryption. Correctly configuring the stage with this encryption type is crucial for resolving this processing error (Option D). Option A, while addressing cost optimization, is not a root cause of the 'processing errors' themselves, although it is a best practice for cost governance as larger warehouses do not increase Document AI query processing speed. Option C is incorrect; best practices for question optimization suggest being specific, not generic. Option E is incorrect as 'max_tokenS relates to the length of the model's output, not the input document's size or page limits.


138. Frage
A data science team is planning to implement a new RAG (Retrieval Augmented Generation) application using Snowflake Cortex, specifically leveraging Cortex Search. They are evaluating the key features, best practices, and cost considerations associated with Cortex Search. Which of the following statements accurately describe aspects of Cortex Search?

  • A. Cortex Search Services require a virtual warehouse for initial setup and subsequent refreshes to run queries against base objects and build the search index.
  • B. Cortex Search automatically handles embedding, infrastructure maintenance, and ongoing index refreshes, and can be used as a backend for enterprise search or a RAG engine for LLM chatbots.
  • C. The credit cost for Cortex Search Services is primarily based on the number of queries executed against the service, not the amount of indexed data.
  • D. Cortex Search supports only English-only embedding models; multilingual RAG applications require external embedding solutions.
  • E. For best search results, Snowflake recommends splitting text in the search column into chunks of no more than 512 tokens, even when longer-context embedding models are available.

Antwort: A,B,E

Begründung:
Option A is correct. Cortex Search provides low-latency, high-quality 'fuzzy' search and handles embedding, infrastructure maintenance, search quality parameter tuning, and ongoing index refreshes. Its primary use cases are as a RAG engine for LLM chatbots and as a backend for enterprise search. Option B is incorrect. Cortex Search Services incur costs based on the amount of indexed data (6.3 Credits per GB/mo of indexed data), not solely on the number of queries executed. Option C is incorrect. Cortex Search offers multilingual embedding models like 'snowflake-arctic-embed-l-v2.ff and 'voyage-multilingual-2 , supporting multilingual AI workflows. Option D is correct. Snowflake recommends splitting text into chunks of no more than 512 tokens for optimal search results, as smaller chunks can lead to more precise retrieval and higher-quality LLM responses in RAG scenarios, even with models that support longer context windows. Option E is correct. A virtual warehouse is required for Cortex Search Service to refresh the service, which includes running queries against base objects, orchestrating text embedding jobs, and building the search index.


139. Frage
A financial institution is building a Document AI model to process loan applications. They need to extract all applicant names (which can be multiple, forming a list), classify the application as 'Approved' or 'Rejected' based on various internal criteria (not explicitly stated in the document), and normalize diverse date formats (e.g., '1st Jan 2023', '01/01/2023') to 'YYYY-MM-DD'. Which of the following approaches represent 'best practices' for defining data values and training the Document AI model for these complex extractions? (Select ALL that apply.)

  • A. To normalize diverse date formats to 'YYYY-MM-DD', train the model with sufficient annotations, showing the desired output format for various input date formats, allowing Document AI to learn the normalization internally.
  • B. For extracting lists of items from tables, use individual data values for each column and then merge these lists in the pipeline, ensuring adequate training with sample data, including NULL values and correct order, for reconstruction.
  • C. For applicant names, define a single value 'applicant_name' and rely solely on advanced prompt engineering to instruct the model on how to identify and list all names, without providing specific examples through annotations.
  • D. To classify applications as 'Approved' or 'Rejected', define a data value like 'What is the application status?' and ensure the training dataset includes annotated examples for both 'Approved' and 'Rejected' classes.
  • E. When defining data values for combinations of values, arrays, or nonstandard formats, fine-tuning the model with annotations that 'show' the expected result is generally more effective than 'telling' it via complex prompt engineering.

Antwort: A,B,D,E

Begründung:
Document AI has specific best practices for handling complex extractions, often summarized as 'Show, don't tell', which prioritizes annotations and training over complex prompt engineering for nuanced tasks. - ' 'Option A" is incorrect. For combinations of values or arrays (like multiple applicant names in a list), relying solely on complex prompt engineering without annotations is less effective. The 'Show, don't tell' principle recommends showing the expected result through annotations across appropriate training documents. - ''Option ' is correct. Normalizing results, such as date formats, is best achieved by training a model with enough documents and annotations, showing the desired normalized format. - ''Option C' ' is correct. For classification tasks (like 'Approved' or 'Rejected'), it's best practice to train a single-defined value, such as 'What is the document type?', and provide every iteration of a possible classifier (e.g., 'Approved', 'Rejected') in the training set. - "Option is correct. This statement directly reflects the 'Show, don't tell' principle, which advises that for complex scenarios like combinations of values, arrays, nonstandard formats, normalization, and classification, showing the model what is expected through annotations and fine-tuning is generally more effective than relying on elaborate prompt engineering. - ''Option is correct. For extracting data from tables that span many documents and reconstructing them into a schema, extracting columns of data into list form (columnar extraction) and then merging them in the pipeline is a valid approach. It is vital to use enough data to train the model to include NULL values and maintain order.


140. Frage
An analytics team is preparing documents for a new Document AI model build to extract information from internal policy reviews. They have a variety of documents that they intend to upload to an internal stage for processing. The document list includes: (1 ) a 70 MB PDF with 100 pages, (2) a 45 MB DOCX with 150 pages, (3) a 30 MB PNG image, (4) a 60 MB TIFF image, and (5) a 20 MB HTML file. All documents are in English. Which of these documents would 'fail' to meet the direct input requirements for Document AI processing?

  • A. The 60 MB TIFF image.
  • B. The 30 MB PNG image.
  • C. The 20 MB HTML file.
  • D. The 70 MB PDF with 100 pages.
  • E. The 45 MB DOCX with 150 pages.

Antwort: A,D,E

Begründung:


141. Frage
......

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