Neil King Neil King's Profile Page

Neil King Neil King

0 Course Enrolled • 0 Course Completed

Biography

Snowflake GES-C01 VCE Dumps & Testking IT echter Test von GES-C01

Snowflake GES-C01 Dumps von ZertFragen sind ganz gleich wie die richtigen Zertifizierungsprüfungen. Die beinhalten alle Prüfungsfragen und Testantworten in aktueller Prüfung. Und die Software-Version simuliert die gleiche Atmosphäre der aktuellen Prüfungen. Bei der Nutzung der ZertFragen Dumps, können Sie ganz sorglos die Snowflake GES-C01 Prüfung ablegen und sehr gute Note bekommen.

Die Fragenkataloge von ZertFragen enthalten die Lernmaterialien und Simulationsfragen zur Snowflake GES-C01 Zertifizierungsprüfung. Noch wichtiger bieten wir die originalen GES-C01 Fragen Und Antworten.

>> GES-C01 Simulationsfragen <<

GES-C01 Fragen Und Antworten - GES-C01 Online Prüfung

Im 21. Jahrhundert ist die Technik hoch entwickelt und die Information weit verbreitet. Das Internet ist nicht nur eine Unterhaltungsplattform, sondern auch eine weltklassige elektronische Bibliothek. Bei ZertFragen können Sie Ihre eigene Schatzkammer für IT-Infoamationskenntnisse finden. Wählen Sie die Fragenkataloge zur Snowflake GES-C01 Zertifizierungsprüfung von ZertFragen, armen Sie zugleich auch die schöne Zukunft um. Wenn Sie unsere Fragenkataloge zur Snowflake GES-C01 Zertifizierungsprüfung kaufen, garantieren wir Ihenen, dass Sie die GES-C01 Prüfung sicherlich bestehen können.

Snowflake SnowPro® Specialty: Gen AI Certification Exam GES-C01 Prüfungsfragen mit Lösungen (Q298-Q303):

298. Frage
A business analyst is using a Cortex Analyst-powered conversational application to query structured data in Snowflake. They initially ask, 'What was the total profit from California last quarter?' and then follow up with, 'What about New York?' The application successfully provides accurate answers to both questions. Which of the following statements explain how Cortex Analyst supports this multi-turn conversational experience and maintains accuracy? (Select all that apply)

A. The semantic model YAML file, which defines logical tables, dimensions, and measures, is crucial for Cortex Analyst to bridge the gap between business terminology and underlying technical schema, thereby improving text-to-SQL conversion accuracy for both initial and follow-up queries.
B. Cortex Analyst stores the full, verbatim history of all previous user prompts and LLM responses, which are then passed to every subsequent LLM call to ensure complete context retention without any summarization.
C. The accuracy of the SQL queries generated by Cortex Analyst for follow-up questions is significantly enhanced by its integration with a Verified Query Repository (VQR), which stores pre-verified natural language questions and their corresponding SQL queries.
D. For multi-turn conversations, Cortex Analyst primarily relies on semantic search over sample values defined in the semantic model to infer context and generate SQL, making explicit conversation history management unnecessary.
E. To handle follow-up questions, Cortex Analyst leverages an internal LLM summarization agent (e.g., Llama 3.1 70B) to reframe the current-turn question by retrieving context from the conversation history, rather than simply passing the entire history.

Antwort: A,C,E

Begründung:
Option A is incorrect. Cortex Analyst does not simply pass the full, verbatim history of all previous prompts and responses to every subsequent LLM call. This 'primitive way' could lead to longer inference times, more non-determinism, and degraded performance due to multitasking. Instead, it uses an LLM summarization agent to manage context. Option B is correct. Cortex Analyst supports multi-turn conversations by recognizing follow-up questions and using an LLM summarization agent (such as Llama 3.1 70B, which showed high accuracy in this role) to retrieve context from the conversation history and reframe the current-turn question. Option C is correct. The Verified Query Repository (VQR) is a collection of pre-verified questions and corresponding SQL queries that helps improve the accuracy and trustworthiness of Cortex Analyst's results by using relevant SQL queries for similar questions. Option D is incorrect. While semantic search over sample values can improve literal search for Cortex Analyst, it is not the primary mechanism for managing the context of multi-turn conversations. Context management relies on an LLM summarization agent. Option E is correct. Semantic models, captured in lightweight YAML files, are critical for Cortex Analyst. They provide richer semantic information than basic database schemas, bridging the gap between business user language and technical database definitions, which is essential for accurate text-to-SQL conversions in both initial and follow-up queries.

299. Frage
A security engineer is developing an application that uses the Snowflake Cortex REST API to interact with LLMs, specifically to obtain structured outputs for text classification and to ensure secure communication. They are focusing on the /api/v2/cortex/ inference : complete endpoint. Which of the following statements correctly describe aspects of this interaction?

A. To strictly enforce a JSON schema for the LLM's response, the response_format parameter must be included in the request body, supplied as a JSON schema object, which helps reduce post-processing efforts.
B. To ensure the most consistent and deterministic structured output from the LLM, it is recommended to set the temperature option to a higher value, such as 0.7 or 1.0, in the request payload.
C. For models like OpenAI (GPT) used via the Cortex REST API with structured output, the JSON schema in the response_format field must include "additionalProperties": false and a "required" field listing all properties at every node.
D. The Cortex REST API for LLM inference always returns the complete LLM response as a single, fully-formed JSON object once generation is finished, regardless of any streaming options.
E. Authentication for Cortex REST API requests is primarily handled through an Authorization: Bearer header, where the token can be a JSON Web Token (JWT), OAuth token, or programmatic access token.

Antwort: A,C,E

Begründung:

300. Frage
A financial institution needs to process thousands of incoming PDF loan application forms daily, extracting applicant names, loan amounts, and submission dates, and loading them into a Snowflake table. They aim for continuous processing with minimal manual intervention. Which of the following statements correctly describe how Document AI can be used in an automated SQL pipeline for this purpose?

A. The pipeline can leverage the <model build name> ! PREDICT method within a CREATE TASK statement to automatically process new PDFs as they arrive in an internal or external stage, once the Document AI model build is published.
B. Document AI's PREDICT method natively supports all PDF files up to 500 MB and 500 pages, allowing for large-scale, single-query processing without requiring users to split documents into smaller chunks.
C. The extracted information, including confidence scores and values, is returned as a JSON object, which can then be parsed into separate columns in a Snowflake table using SQL functions like LATERAL FLATTEN.
D. The SNOWFLAKE .DOCUMENT_INTELLIGENCE_CREATOR database role alone is sufficient for defining the model build and configuring the processing pipeline, without needing additional CREATE MODEL privileges on the schema.
E. To ensure continuous data ingestion and processing, a STREAM can be created on the stage to detect new PDF documents, triggering the TASK for extraction and subsequent loading into a Snowflake table.

Antwort: A,C,E

Begründung:
Option A is correct because DocumentAI supports creating automated pipelines with tasks that call the method to extract information from documents in a stage. Option B is correct as streams are used to detect new data (e.g., PDFs) in a stage, and tasks can be set up to execute when new data is available in the stream, enabling continuous processing. Option E is correct because the 'PREDICT method returns its results as a JSON object, which typically contains 'score' and 'value' fields for extracted entities, and this JSON output can be parsed into separate columns using 'LATERAL FLATTEN'. Option C is incorrect as, in addition to the 'SNOWFLAKE.DOCUMENT_INTELLIGENCE_CREATOR database role, the role used must also have 'CREATE SNOWFLAKE.ML.DOCUMENT_INTELLIGENCE and 'CREATE MODEL' privileges on the schema where the model build is located. Option D is incorrect because DocumentAI has specific limitations on document size (max 50 MB) and page count (max 125 pages per document), and also limits processing to a maximum of 1000 documents in one query.

301. Frage
A data engineer is tasked with defining a semantic model for Cortex Analyst to enable natural language queries over sales dat a. They are creating a YAML file to describe the logical structure. Which of the following statements correctly describe the configuration of this semantic model? (Select all that apply)

A. Dimensions such as 'product_category' can include a configuration, which can specify an optional 'literal_column' that, if omitted, defaults to the search index column.
B. To ensure high accuracy for specific common questions, 'verified_querieS can be included, where the 'sqr field must reference the underlying physical table and column names (ee.g., not the logical names defined in the semantic model.
C. The 'VARIANT data type is a supported data type for columns defined as 'dimensions' or 'facts' within the semantic model, allowing flexible storage of complex attributes.
D. A 'metric' like 'total_revenue' can be defined using an 'expr' that references other logical columns, such as 'sum(profity from a logical 'sales_data' table, within the semantic model.
E. The 'tables' section must define logical tables that directly map to Snowflake physical tables or views, with 'base_table' specifying the fully qualified name (database, schema, table).

Antwort: A,D,E

Begründung:
Option A is correct. A logical table, a foundational concept of Snowflake's semantic model, represents either a physical database table or a view and its 'base_table' field specifies the fully qualified name of the underlying physical table. Option B is correct. Dimensions can specify a block to integrate with Cortex Search, and the 'literal_column' field within this block is optional and defaults to the search index. Option C is incorrect. The 'VARIANT, 'OBJECT, GEOGRAPHY' , and 'ARRAY' data types are explicitly not supported for dimensions, time dimensions, or facts within a semantic model. Option D is incorrect. Verified queries must use the names of the logical tables and columns as defined in the semantic model, not those in the underlying physical dataset. For example, sales_data' for a logical table named 'sales_data'. Option E is correct. Metrics can be defined using an SQL expression ('expr') that can reference logical columns (facts, dimensions, or time dimensions) within the same logical table or from another logical table in the semantic model.

302. Frage
A data engineering team is designing a pipeline in Snowflake to translate a continuous stream of multi-language customer support tickets into English using 'SNOWFLAKE.CORTEX.TRANSLATE. They are concerned about potential language identification issues and the overall cost implications. Which of the following statements are true regarding the use of 'SNOWFLAKE.CORTEX.TRANSLATE for this scenario? (Select all that apply)

A. If the source language of a ticket is unknown or contains mixed languages (e.g., 'Spanglish'), the function can still process it by specifying an empty string ') for the source _ language argument.
B. The fixed billing rate for the 'TRANSLATE function is 1.50 Credits per one million Tokens processed.
C. Snowflake Cortex functions, including 'TRANSLATE, add an internal prompt to the user's input text, which increases the total input token count for billing purposes beyond the raw text length.
D. The 'TRANSLATE' function is exclusively billed based on the number of input tokens, as it primarily analyzes existing text rather than generating new content.
E. For cost efficiency, Snowflake recommends using a larger warehouse (e.g., XL or 2XL) for executing queries that call 'TRANSLATE functions, as this significantly reduces the per-token processing cost.

Antwort: A,B,C

Begründung:
Option A is correct because 'SNOWFLAKE.CORTEX.TRANSLATE can handle mixed-language input or unknown source languages by specifying an empty string for the 'source_language' argument. Option B is incorrect; for functions that generate new text, such as TRANSLATE , both input and output tokens are billable. Option C is incorrect; Snowflake recommends executing queries that call Cortex AISQL functions with a smaller warehouse (no larger than MEDIUM), as larger warehouses do not increase performance. Option D is correct, as TRANSLATE (among other Cortex functions) adds an internal prompt to the input text, resulting in a higher billed input token count than the raw text provided. Option E is correct, as the cost for the ' Translate' function is 1.50 Credits per one million Tokens processed.

303. Frage
......

Es ist ganz normal, vor der Prüfung Angst zu haben, besonders vor der schwierig Prüfung wie Snowflake GES-C01. Wir wissen, dass allein mit der Ermutigung können Ihnen nicht selbstbewusst machen. Deshalb bieten wir die praktische Prüfungssoftware, um Ihnen zu helfen, Snowflake GES-C01 zu bestehen. Sie können zuerst die Demo der Snowflake GES-C01 gratis probieren. Wir glauben, dass Sie bestimmt unsere Bemühungen und Professionellsein von der Demo empfinden!

GES-C01 Fragen Und Antworten: https://www.zertfragen.com/GES-C01_prufung.html

Snowflake GES-C01 Simulationsfragen IT-Fachleute sehr beliebt, Snowflake GES-C01 Simulationsfragen Aber ihre Schwierigkeit nimmt doch nicht ab, Snowflake GES-C01 Simulationsfragen Wir hoffen, dass Sie unsere Professionalität und Ehrlichkeit davon empfinden, Snowflake GES-C01 Simulationsfragen Jetzt ist die Zeit für Änderungen, Die Schulungsunterlagen zur Snowflake GES-C01 Zertifizierungsprüfung von ZertFragen sind solche erfolgreichen Schulungsunterlagen.

In der Halle der Lampen holte er ihn ein, unter den Augen von zwei Dutzend GES-C01 Simulationsfragen erschreckten Septas, Und der Gestank wurde noch schlimmer, IT-Fachleute sehr beliebt, Aber ihre Schwierigkeit nimmt doch nicht ab.

GES-C01 Torrent Anleitung - GES-C01 Studienführer & GES-C01 wirkliche Prüfung

Wir hoffen, dass Sie unsere Professionalität GES-C01 und Ehrlichkeit davon empfinden, Jetzt ist die Zeit für Änderungen, Die Schulungsunterlagen zur Snowflake GES-C01 Zertifizierungsprüfung von ZertFragen sind solche erfolgreichen Schulungsunterlagen.

Neil King Neil King

Biography

Feature Links

Support

Contact Us