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| Section | Weight | Objectives |
|---|---|---|
| Snowpark Container Services for Gen AI | 10% | - Custom model deployment
|
| Snowflake Gen AI Governance | 18% | - Access control and security
|
| Snowflake Cortex AI and LLM Functions | 38% | - Cortex AI functions
|
| Snowflake for Gen AI Overview | 24% | - Generative AI solution architecture
|
| Document Processing | 10% | - Document processing pipelines
|
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If you want to pass a high percentage of the Snowflake GES-C02 Exam, you should consider studying for the actual exam. These practice tests are designed to help you prepare for the exam and ensure you know the syllabus content. It will also help you improve your time management skills, as these tests are designed like an actual exam. Moreover, they will help you learn to answer all questions in the time allowed.
NEW QUESTION # 77
A Gen AI Specialist is designing a Gen AI-powered data pipeline in Snowflake that must meet these requirements:* Download PDFs from a website every hour.* Load the PDFs into an internal stage.* Extract text from the PDFs and load that text into a Snowflake table.* Use prompt engineering to generate an opinion on the text based on a given persona.* Automate the pipeline for maximum efficiency.
Which Snowflake features are required to create this data pipeline with stored procedures? (Choose three)
Answer: C,D,F
Explanation:
Building an automated, end-to-end ingestion and Generative AI processing pipeline within Snowflake stored procedures relies on three core capabilities:1. **External Access Integration:** Authorizes stored procedures (e.g., Python handlers using `urllib` or `requests`) to establish outbound network connections to external HTTPS endpoints, enabling hourly downloads of PDF files from third-party websites into Snowflake internal stages.2. **Streams and Tasks:** Tasks provide native cron-like scheduling to execute the stored procedures every hour. Streams capture change-data-capture (CDC) deltas on staging metadata tables to trigger downstream text transformation and extraction incrementally.3. **Cortex TRY_COMPLETE Function:** Foundation model completion functions allow prompt engineering to evaluate extracted text using customized persona instructions. Using `TRY_COMPLETE` ensures that potential runtime model errors or token limit exceptions return NULL rather than failing the automated batch pipeline.
Snowpipe automates micro-batch file loading rather than executing external web scrapes, Dynamic Tables cannot execute arbitrary external scraping procedures, and `SENTIMENT` returns a numeric polarity score rather than synthesizing persona-driven opinions.References & Study Guide Topics:* Snowflake Document Processing: Ingestion Pipelines and Staged Document Extraction* Data Engineering with Gen AI: External Access Integrations, Tasks, and `TRY_COMPLETE`===============
NEW QUESTION # 78
Which action is supported when using Snowflake Document AI?
Answer: B
Explanation:
Snowflake Document AI enforces specific operational constraints and service limits to maintain processing stability and low-latency throughput when invoking model builds.
When applying a published Document AI model build to staged files using the < model_build_name > !
PREDICT() inference method, Snowflake supports processing a maximum batch size of up to 1,000 documents within a single SQL statement execution. Exceeding this boundary in a batch query triggers a processing error, requiring developers to partition document queues or process them in smaller batches.
Regarding the remaining options:
* Document AI is designed for discrete field and key-value extraction; extracting large multi-page tables across arbitrary formats in a single unconstrained query is not natively supported as an end-to-end operation without specialized bounding-box post-processing.
* Document AI models do not permit concurrent collaborative editing where multiple users modify the same model build simultaneously, as training sessions enforce single-user authoring locks to prevent annotation conflicts.
* Modifying database or schema structures is a general DDL capability and not an action specific to Document AI execution.
References & Study Guide Topics:
* Snowflake Document Processing: Document AI Operational Limitations & Quotas
* Model Inference Architecture: Batch Processing Boundaries with !PREDICT
NEW QUESTION # 79
A Gen AI Specialist is developing an automated content generator in Snowflake that produces customized product descriptions. The descriptions will be based on columns in a products table, including brand, category, and features.
Which Snowflake Cortex LLM function should be used to generate the descriptions?
Answer: D
Explanation:
Generating dynamic, creative marketing content-such as customized product descriptions tailored from tabular database attributes-requires a general-purpose, prompt-driven large language model.
SNOWFLAKE.CORTEX.COMPLETE accepts a user-defined prompt alongside dynamic column parameters, allowing developers to engineer prompts that combine tabular fields (e.g., 'Write a compelling product description for Brand: ' || brand || ', Category: ' || category || ', Features: ' || features). The foundation model processes these inputs to synthesize fluent, context-aware descriptions.
The alternative functions are not designed for open-ended text synthesis:
* PARSE_DOCUMENT extracts raw text from document files in Snowflake stages.
* EMBED_TEXT_768 converts input strings into high-dimensional numerical vector embeddings for similarity matching.
* SUMMARIZE condenses existing text into a shorter synopsis rather than generating expanded, creative copy from attribute lists.
References & Study Guide Topics:
* Snowflake Gen AI Functions: Text Generation using SNOWFLAKE.CORTEX.COMPLETE
* Prompt Engineering in SQL: Dynamic Prompt Composition over Tabular Data
NEW QUESTION # 80
A Gen AI Specialist is writing a Python script that leverages Snowflake Cortex to answer users' questions.
The script contains this code:
Python
from snowflake.cortex import CompleteOptions, complete
user_question = < 'Prompt that could generate an unsafe response' >
Regardless of the content of the prompt (user_question) that is passed to snowflake.cortex.complete, the Specialist must ensure that the function filters potentially unsafe or harmful response language.
Which code will accomplish this?
Python
Answer: D
Explanation:
To prevent large language models from returning toxic, violent, sexually explicit, or otherwise hazardous content, Snowflake integrates Cortex Guard directly into its LLM completion endpoints. Within the Snowpark Python API, this capability is exposed through the snowflake.cortex.CompleteOptions class.
Passing options=CompleteOptions(guardrails=True) instructs the Cortex inference engine to route the generated output through an automated safety evaluation model prior to returning it to the calling application.
If an output violates safety boundaries, Cortex replaces the payload with a standardized safety rejection response, safeguarding downstream consumers regardless of the user's input prompt.
The other parameters shown serve entirely different functional roles: temperature governs sampling stochasticity and response creativity, stream dictates whether responses are returned incrementally via Server- Sent Events (SSE), and response_format accepts a structured JSON schema rather than safety flags.
References & Study Guide Topics:
* Snowflake Gen AI Governance: Cortex Guard Configuration in Snowpark Python
* Enterprise AI Safety: Filtering Harmful LLM Completions via CompleteOptions
NEW QUESTION # 81
Which SQL functions are designed and optimized to carry out and automate specific routine tasks within Snowflake Cortex? (Choose two)
Answer: D,E
Explanation:
Snowflake Cortex categorizes its AI functions into general-purpose text-generation functions (such as COMPLETE) and task-specific LLM functions that automate standard natural language processing and document extraction tasks out of the box without requiring manual prompt engineering:
* SENTIMENT (SNOWFLAKE.CORTEX.SENTIMENT): A specialized task function that analyzes English text input and automatically outputs a continuous sentiment score from -1 (negative) to 1 (positive).
* PARSE_DOCUMENT (SNOWFLAKE.CORTEX.PARSE_DOCUMENT): A purpose-built document processing function that extracts text, OCR data, and structured layouts from staged PDF, Word, or image files.
Conversely, ANOMALY_DETECTION and FORECAST are Snowflake ML Analysis functions based on traditional statistical and gradient-boosted time-series algorithms, rather than Cortex Generative AI functions.
Furthermore, the Cortex text classification function is named CLASSIFY_TEXT (or AI_CLASSIFY), whereas CLASSIFICATION refers to the structured machine learning classification class within Snowflake ML.
References & Study Guide Topics:
* Snowflake Gen AI Functions: Task-Specific LLM Functions vs. General Completion Models
* Cortex Function Hierarchy: Specialized NLP Utilities (SENTIMENT, PARSE_DOCUMENT)
NEW QUESTION # 82
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