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Free PDF GES-C01 - SnowPro® Specialty: Gen AI Certification Exam Perfect Latest Test Materials

Are you planning to crack the Snowflake GES-C01 certification test but don't know where to get updated and actual Snowflake GES-C01 exam dumps to get success on the first try? If you are, then you are on the right platform. FreeDumps has come up with Real GES-C01 Questions that are according to the current content of the GES-C01 exam.

Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q183-Q188):

NEW QUESTION # 183
An ML Engineer has developed a custom PyTorch model for GPU-powered inference and successfully built an OCI-compliant image locally. They now need to push this image to a Snowflake image repository and configure a Snowpark Container Service to use it. The Snowflake account identifier is my org_name_my_account_id_prod. Which set of commands correctly demonstrates tagging the local image and pushing it to the repository?

Answer: B,E

Explanation:


NEW QUESTION # 184
A Gen AI Specialist is tasked with preparing a Cortex Analyst semantic model to provide a predefined set of initial questions for new business users to explore their dat a. They want these specific questions to always be displayed, irrespective of what the user might initially type, as part of an onboarding experience. Which of the following actions, or combinations of actions, must the Specialist take to successfully configure a semantic model to display a full set of predefined "onboarding questions" in Cortex Analyst?

Answer: D,E

Explanation:
To configure onboarding questions, a Verified Query Repository (VQR) must be defined in the semantic model, and the specific questions must be included as 'verified_queries' . Therefore, option A is correct. For each desired question to function as an onboarding question, the flag must be set to 'true' within its VQR entry, which ensures these questions are returned regardless of user input similarity. This makes option B correct. Verified SQL queries in the VQR must use the names of logical tables and columns defined in the semantic model, not the underlying physical column names, so option C is incorrect. Cortex Analyst will return all questions marked as onboarding questions, even if there are more than five, if the feature is configured in Customizable Mode, making option D incorrect. The task_description' is an optional argument in functions like 'CLASSIFY_TEXT' and is not used by Cortex Analyst to generate onboarding questions via LLMs; onboarding questions are drawn from the VQR.


NEW QUESTION # 185
A data team is refining their Cortex Analyst semantic model to improve the accuracy of responses for specific, frequently asked questions and to enable better literal value searches. Consider a semantic model being developed to address these requirements. Which two configurations or features are directly relevant and correctly applied in the semantic model YAML for these purposes?

Answer: B,E

Explanation:
Option A is correct. Cortex Search Services can be integrated into a dimension's definition (using the field with 'service' and fields) to improve literal matching by performing semantic search over the underlying column, which enhances Cortex Analyst's ability to find literal values for filtering. Option B is correct. The 'verified_querieS section allows pre-defining accurate SQL queries for specific natural language questions. Setting 'use_as_onboarding_question true' ensures these queries are used when relevant and presented as suggested questions to users. Option C is incorrect; while metrics can reference logical columns, 'relationships' between logical tables are necessary for defining joins, especially across different underlying base tables. Option D is incorrect; 'custom_instructions' are provided at the model level to give general context to the LLM for SQL query generation, not embedded within individual dimension definitions. Option E is incorrect; the 'sample_valueS field is recommended for dimensions with relatively low-cardinality (approximately 1-10 distinct values) to aid in semantic search for literals, not for high-cardinality dimensions.


NEW QUESTION # 186
A data engineering team is setting up an automated pipeline to extract information from new invoices using Document AI. They've created a database and schema Cinvoice_db.invoice_schema') and a Document AI model build ('invoice_extractor'). They then created an internal stage for documents. When they attempt to run the 'invoice_extractor!PREDlCT' method on documents uploaded to 'invoice_stage' , they consistently receive the following error:

Given this error message, which 'corrective SQL command' addresses the most likely misconfiguration of the 'invoice_stage' to allow Document AI processing?

Answer: C

Explanation:


NEW QUESTION # 187
A new Gen AI team member attempts to use Document AI to process a batch of 1 ,500 scanned image files (JPG) that are 70 MB each, stored in an internal stage that was created without specifying an encryption type. Their '!PREDICT' queries consistently fail with various errors. Which of the following are valid reasons for the '!PREDICT' queries to fail in this scenario?

Answer: A,B,D,E

Explanation:
Option A is correct because internal stages used with Document AI must specify 'ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE')' when created. Option B is correct as the database role is required for the account role to use Document AI functions to extract information. Option C is correct because Document AI supports processing a maximum of 1 ,000 documents in one query, so 1 ,500 documents would exceed this limit. Option D is correct because documents processed by Document AI must be 50 MB or less in size, and the 70 MB files exceed this limit. Option E is incorrect because JPG is listed as a supported file format for Document AI.


NEW QUESTION # 188
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