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  1. Home
  2. Snowflake Certification
  3. GES-C01 Exam
  4. Snowflake.GES-C01.v2026-03-27.q130 Dumps
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Question 46

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?

Correct Answer: B
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.
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Question 47

A Gen AI Specialist is setting up Snowpark Container Services (SPCS) to host a custom open-source LLM. They need to understand the fundamental nature and constraints of image repositories within Snowflake. Which of the following statements accurately describe image repositories in Snowflake's Snowpark Container Services?

Correct Answer: D
Option D is correct because an image registry is an OCIv2 compliant service for storing OCI-compliant container images, and an image repository is defined as a storage unit within that image registry service. Option A is incorrect as image registries are for all OCI-compliant container images, not exclusively Docker images. Option B is incorrect because dropping individual images from a repository is currently not supported; instead, dropping a repository removes all images within it. Option C is incorrect as the maximum layer size for an image registry varies by cloud provider (160 GiB for AWS and 195 GiB for Azure). Option E is incorrect because Snowpark Container Services is designed to run various containerized workloads, including custom third-party container images and open-source LLMs, providing flexibility beyond Snowflake's pre-trained ML models.
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Question 48

A financial analytics team is using to extract specific financial metrics (e.g., revenue, profit margin) from quarterly reports and requires the output in a strict JSON format for automated ingestion into a data warehouse. They've encountered issues where the LLM sometimes generates malformed JSON or includes extraneous text. Which of the following approaches will help ensure deterministic, schema-compliant JSON outputs and mitigate these 'hallucinations' related to format?

Correct Answer: A,B,C,D
Option A is correct because 'AI_COMPLETE Structured Outputs allows supplying a JSON schema via the 'response_format' argument, which defines the structure, data types, and constraints, ensuring responses conform to the schema and reducing post-processing. Option B is correct as it specifically states requirements for OpenAI (GPT) models when using 'response_format' , including 'additionalProperties: false' and explicitly listing all properties in the 'required' field. Option C is correct because, for medium-to-complex tasks, adding 'Respond in JSON' to the prompt can further improve JSON adherence accuracy, even when 'response_format' is specified. Option D is correct as setting 'temperature' to 0 is recommended for the most consistent results, which is crucial for deterministic JSON outputs, regardless of task or model. Option E is incorrect because 'CLASSIFY_TEXT is for categorizing text, not for validating JSON structure, making it an inappropriate tool for this task.
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Question 49

A data engineering team is building a Retrieval Augmented Generation (RAG) pipeline that heavily relies on 'SNOWFLAKE.CORTEX.EMBED_TEXT 768' to process millions of documents daily. They need to optimize for both cost and retrieval quality. Which of the following statements are true regarding the cost and performance of 'EMBED_TEXT 768' in Snowflake? (Select all that apply)

Correct Answer: B,C,D
Option B is correct because Snowflake recommends executing queries that call Cortex AISQL functions, including , with a smaller warehouse (no larger than MEDIUM). Larger warehouses do not increase performance for these functions. Option C is correct because the 'snowflake-arctic-embed-m-vl .5 model, which can be used with 'EMBED TEXT 768', has a context window of 512 tokens. Input text exceeding this limit is truncated before embedding. Option D is correct because, for best search results with Cortex Search and RAG, Snowflake recommends splitting the text into chunks of no more than 512 tokens. This smaller chunk size typically results in higher retrieval and downstream LLM response quality. Option A is incorrect because for functions, only 'input tokens' are counted towards the billable total, not output tokens. Option E is incorrect because the cost for models (such as 'e5-base-v2 , snowflake-arctic-embed-m' , 'snowflake-arctic-embed-m-v1.5') is 0.03 Credits per one million Tokens processed. The 1.50 Credits per one million Tokens applies to the ' TRANSLATE' function, not 'EMBED_TEXT_768'.
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Question 50

An ML Engineer is logging a custom PyCaret model to the Snowflake Model Registry, with the intention of deploying it to Snowpark Container Services (SPCS) for GPU-powered inference. The PyCaret model is wrapped in a 'custom_model.ModelContext'. Which of the following statements correctly describe the considerations for the call and the model's environment?

Correct Answer: B,C,D
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