GES-C01試験の準備方法 | 実用的なGES-C01試験番号試験 | 実際的なSnowPro® Specialty: Gen AI Certification Exam参考書内容最近のわずかの数年間で、SnowflakeのGES-C01認定試験は日常生活でますます大きな影響をもたらすようになりました。将来の重要な問題はどうやって一回で効果的にSnowflakeのGES-C01認定試験に合格するかのことになります。この質問を解決したいのなら、GoShikenのSnowflakeのGES-C01試験トレーニング資料を利用すればいいです。この資料を手に入れたら、一回で試験に合格することができるようになりますから、あなたはまだ何を持っているのですか。速くGoShikenのSnowflakeのGES-C01試験トレーニング資料を買いに行きましょう。 Snowflake SnowPro® Specialty: Gen AI Certification Exam 認定 GES-C01 試験問題 (Q38-Q43):質問 # 38
An ML Engineer has developed a custom PyTorch model for image processing that requires GPU acceleration and specific PyPl packages ('torch' , 'torchvision'). They want to deploy it as a service on Snowpark Container Services (SPCS) using the Snowflake Model Registry. Which of the following statements are true regarding the deployment of this model to SPCS and its requirements? (Select all that apply.)
A. Option E
B. Option B
C. Option D
D. Option A
E. Option C
正解：B、C、E
解説：
StatementA is incorrect. While Snowflake recommends using only 'conda_dependencies' or only 'pip_requirements' (not both) to avoid package conflicts, the scenario explicitly mentions PyPl packages. If using 'pip_requirements', all required packages should be listed there. The example incorrectly assumes 'torchvision' would necessarily be best sourced from Conda and dictates avoiding 'pip_requirements' entirely, which is an oversimplification of the recommendation. Statement B is correct. To utilize GPU acceleration in SPCS, a compute pool configured with a GPU instance family (e.g., *GPU must be created and then referenced by name in the 'service_compute_poor' argument when creating the service. Statement C is correct. Snowflake's warehouse nodes have restricted directory access, and '/tmpP is recommended as a safe and writeable location for models that need to write files during execution. This principle extends to SPCS containers. Statement D is correct. The 'create_service' method for deploying models to SPCS takes a gpu_requests argument, which specifies the number of GPUs to allocate to the service. Setting this (e.g., to "s) is crucial for ensuring the model runs on GPU hardware. Statement E is incorrect. The 'relax_version' option, which modifies version constraints, defaults to 'True' in 'log_moder' While often beneficial, it is not mandatory to explicitly set it to 'True' for every deployment scenario.
質問 # 39
A data scientist is optimising a Cortex Analyst application to improve the accuracy of literal searches within user queries, especially for high-cardinality dimension values. They decide to integrate Cortex Search for this purpose. Which of the following statements are true about this integration and the underlying data types in Snowflake? (Select all that apply)
A. For optimal RAG retrieval performance with Cortex Search, it is generally recommended to split text into chunks of no more than 512 tokens, even when using embedding models with larger context windows such as 'snowflake-arctic-embed-l-v2.0-8k'.
B. The cost for embedding data into a Cortex Search Service is primarily incurred per output token generated by the embedding model, as these represent the final vector embeddings, rather than input tokens.
C. Cortex Search Services, when configured as a source for Snowflake dynamic tables, automatically refresh their search index with continuous data updates, maintaining low-latency search results.
D. To integrate Cortex Search with a logical dimension, the semantic model YAML must include a block within the dimension's definition, specifying the service name and optionally a 'literal_column' .
E. The "VECTOR data type in Snowflake, used to store embeddings generated for Cortex Search, is fully supported as a clustering key in standard tables and as a primary key in hybrid tables to accelerate vector similarity searches.
正解：A、D
解説：
Option A is correct. Cortex Analyst can leverage Cortex Search Services to improve literal search by including a configuration block within a dimension's definition in the semantic model YAML. This block specifies the service name and an optional 'literal_column'. Option B is correct. Snowflake recommends splitting text in your search column into chunks of no more than 512 tokens for best search results with Cortex Search, even when using models with larger context windows like 'snowflake-arctic-embed-l-v2.0-8k' This practice typically leads to higher retrieval and downstream LLM response quality in RAG scenarios. Option C is incorrect. The 'VECTOR data type is allowed in hybrid tables but is explicitly not supported as a primary key, secondary index key, or clustering key in Snowflake. Option D is incorrect. For EMBED_TEXT functions, which are used to generate embeddings for Cortex Search, only 'input tokens' are counted towards the billable total, not output tokens. The Cortex Search service itself is billed per GBImonth of indexed data. Option E is incorrect. Snowflake Cortex functions, including Cortex Search, do not support dynamic tables.
質問 # 40
A data platform administrator needs to retrieve a consolidated overview of credit consumption for all Snowflake Cortex AI functions (e.g., LLM functions, Document AI, Cortex Search) across their entire account for the past week. They are interested in the aggregated daily credit usage rather than specific token counts per query. Which Snowflake account usage views should the administrator primarily leverage to gather this information?
A. Option E
B. Option B
C. Option A
D. Option C
E. Option D
正解：B
解説：
質問 # 41
data scientist is designing a Retrieval Augmented Generation (RAG) system in Snowflake for querying a large knowledge base of internal documents. They plan to store document embeddings and use vector similarity for retrieval. Which statement accurately describes the use of vector functions and associated costs in this RAG architecture?
A. Snowpark Python fully supports all Snowflake vector similarity functions, including
B. Storing vector embeddings, created using functions like
C. The
D.
E. When using
正解：E
解説：
Option A is incorrect because
is a vector similarity function, not an embedding generation function. Embedding generation functions like
incur compute costs based on input tokens. Option B is incorrect. While there are storage costs associated with storing data in Snowflake, the sources do not state that storing vector embeddings themselves incurs 'token-based costs for the storage itself; rather, the EMBED_TEXT functions incur compute costs per input token during the embedding creation process. Option C is correct. Vector similarity functions, including
, do not incur token-based costs. This contrasts with embedding creation which does incur such costs. Option D is incorrect. Snowflake Cortex provides four vector similarity functions:
; no single one is stated as the 'only recommended' or universally superior. Option E is incorrect. The Snowpark Python library explicitly states it does not support the
function, making the claim of full support for all similarity functions false.
質問 # 42
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?
A. Ensure the training dataset used for fine-tuning includes diverse documents representing various layouts, data variations, and explicit examples of 'NULL' values or empty cells where appropriate.
B. Actively involve subject matter experts (SMEs) or document owners throughout the iterative process to help define data values, provide annotations, and evaluate the model's effectiveness.
C. Set the 'temperature' parameter to a higher value (e.g., 0.7) during '!PREDICT calls to encourage more creative and diverse interpretations by the model.
D. Prioritize extensive prompt engineering by creating highly detailed and complex questions with intricate logic to guide the LLM's understanding of the extraction task.
E. Limit the fine-tuning training data exclusively to perfectly formatted and clean documents to ensure the model learns from ideal examples without noise.
正解：A、B
解説：
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 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.
