Title: GES-C01 zu bestehen mit allseitigen Garantien [Print This Page] Author: rongree689 Time: 4 hour before Title: GES-C01 zu bestehen mit allseitigen Garantien Zertpruefung ist eine gute Website, die effiziente Ausbildung zur Snowflake GES-C01 Zertifizierungspr¨¹fung bietet. Und Zertpruefung verspricht, dass Sie die Snowflake GES-C01 Zertifizierungspr¨¹fung bestehen können. Sonst geben wir Ihnen eine volle R¨¹ckerstattung. Vorm Kauf unserer Produkte können Sie im Internet teilweise die Demo zur Snowflake GES-C01 Zertifizierungspr¨¹fung von Zertpruefung kostenlos herunterladen. Dann werden Sie mehr Vertrauen in unsere Prodzkte setzen. Sie können sich dann gut auf Ihre Snowflake GES-C01 Zertifizierungspr¨¹fung vorbereiten.
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A data analytics team aims to enhance their understanding of customer feedback stored in a Snowflake table called CUSTOMER_FEEDBACK. This table has a REVIEW_TEXT column containing raw customer comments and a CUSTOMER_SEGMENT column. The team wants to classify each review into predefined categories and then generate a concise summary of all reviews for each customer segment. Which of the following Snowflake Cortex AI functions and approaches should they use?
A. Option B
B. Option C
C. Option E
D. Option D
E. Option A
Antwort: B,C,E
Begr¨¹ndung:
70. Frage
An organization is building a new knowledge base system within Snowflake, which relies on 'SNOWFLAKE.CORTEX.EMBED_TEXT_1024' to generate and store embeddings for documents in a 'VECTOR(FLOAT, 1024)' column. They plan to use these embeddings for semantic search and integrate them into various data processing workflows. Which of the following statements accurately describe limitations or specific compatibility aspects of 'EMBED TEXT 1024' or the 'VECTOR' data type within Snowflake?
A. The 'VECTOR data type, used to store the output of is fully supported as primary or secondary index keys in Snowflake's hybrid tables.
B. The 'VECTOR data type is not supported in 'VARIANT columns, which means direct storage of embeddings alongside other semi-structured metadata in a single "VARIANT column is not possible.
C. To compare the generated 1024-dimension vectors for similarity, only the 'VECTOR COSINE SIMILARITY function is officially supported by Snowpark Python.
D. The 'EMBED function in the Cortex REST API can be used to process a list of text strings, where each individual string can be up to 4096 characters long.
E. When 'EMBED_TEXT 1024' is invoked within a Snowflake dynamic table's SELECT statement, it allows for continuous, automated updates of embeddings as new data arrives.
Antwort: B,D
Begr¨¹ndung:
Option C is correct. The 'VECTOR data type, which stores the output of ' , is explicitly not supported in 'VARIANT columns. This means embeddings cannot be stored directly within a 'VARIANT column. Option E is correct. When invoking the EMBED' function via the Cortex REST API, the 'text' argument accepts a list of text strings, and each string in that list can be up to 4096 characters long. Option A is incorrect because while 'VECTOR data types are allowed in hybrid tables, they are not supported as primary keys or secondary index keys. Option B is incorrect because Snowflake Cortex functions, including , do not support dynamic tables. Option D is incorrect because the Snowpark Python library does 'not' support the 'VECTOR_COSINE SIMILARITY function. It does, however, support , and SQL supports multiple vector similarity functions including
71. Frage
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. 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.
B. The Python API for calling the service requires the Snowflake session object directly, bypassing HTTP endpoint authentication.
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 'Authorization' header with a 'Snowflake Token=""' value is a valid method for authenticating requests to the public endpoint programmatically.
E. The public endpoint of the SPCS service can be accessed directly without any authentication, as it's a public endpoint.
Antwort: A,D
Begr¨¹ndung:
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.
72. Frage
A data scientist needs to fine-tune a 'mistral-7b' LLM using Snowflake Cortex for a specific text summarization task. They have prepared a training dataset in a Snowflake table, with text in a column named 'source_text' and the desired summaries in a column named 'expected_summary' . They also want to understand the cost implications. Which SQL statement will correctly initiate the fine-tuning job, and how will the cost be primarily calculated?
A. The fine-tuning job is initiated by:
B. The fine-tuning job is initiated by:
C. The fine-tuning job is initiated by:
D. The fine-tuning job is initiated by:
E. The fine-tuning job is initiated by providing the prompt and completion data directly as arrays within the 'FINETUNE' function call, avoiding the need for a separate training data table, and costs are only for the storage of the fine-tuned model.
Antwort: B
Begr¨¹ndung:
Option B is correct. The 'SNOWFLAKE.CORTEX.FINETUNE function requires the training data query result to include columns named 'prompt' and 'completion'. Using SELECT ... AS prompt, ... AS completion" aliases the existing columns to the required names. The cost for fine-tuning is based on the number of tokens used in training, specifically calculated as 'Fine-tuning trained tokens = number of input tokens ' number of epochs trainecf. Additionally, running 'AI_COMPLETE on a fine-tuned model incurs compute costs for both input and output tokens processed. Option A is incorrect because the columns need to be aliased to 'prompt' and 'completion', and it misstates the cost calculation for fine-tuning training itself. Option C is incorrect because the syntax for FINETUNE is a SQL function, not a 'CREATE SNOWFLAKE.ML.FINETUNE' command, and Cortex Fine-tuning incurs compute costs based on tokens, not a fixed-rate subscription model. Option D is incorrect because the first argument for the function is 'CREATE', not 'TRAIN', and training costs are based on tokens, not GPU compute hours, although compute resources are utilized. Option E is incorrect because the training data must come from a Snowflake table or view, and costs are incurred for training and inference, not just storage.
73. Frage
A data application developer is using the Snowflake Cortex COMPLETE function to power a multi-turn conversational AI application. They want to ensure responses are creative but not excessively long, adhere to a specific JSON structure, and are filtered for safety. Given the following SQL query snippet, which statements accurately describe the impact of the specified options?
A. The
B. The
C. Including a
D. Setting
E. For a multi-turn conversation, previous user prompts and model responses should be passed in the
Antwort: A,B,C
Begr¨¹ndung:
Option A is incorrect because a higher temperature, such as 0.8, controls the randomness of the output by influencing which possible token is chosen at each step, resulting in more diverse and random output, not deterministic and focused. Option B is correct because the 'max_tokens' option sets the maximum number of output tokens in the response, and small values can result in truncated responses. Option C is correct because the 'guardrails: TRUE option enables Cortex Guard to filter potentially unsafe and harmful responses from a language model. Option D is correct because AI_COMPLETE Structured Outputs allows you to supply a JSON schema that completion responses must follow, and every model supported by AI_COMPLETE supports structured output. Option E is incorrect because to provide a stateful conversational experience, all previous user prompts and model responses should be passed in the 'prompt_or_history' array, but the number of tokens processed increases for each round, and costs increase proportionally. The 'COMPLETE' function is the older version of 'AI_COMPLETE'.
74. Frage
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