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| Section | Objectives |
|---|---|
| Topic 1: Cortex AI Functions & Application | - Built-in LLM functions
|
| Topic 2: Gen AI Governance & Security | - Privileges, roles, access control
|
| Topic 3: Document Processing | - AI_PARSE_DOCUMENT, AI_EXTRACT pipelines
|
| Topic 4: Model Deployment & Management | - Snowpark Container Services & Model Registry
|
| Topic 5: Snowflake for Gen AI Overview | - Snowflake Gen AI principles and features
|
| Topic 6: RAG & Vector Search with Cortex Search | - Vector types, semantic search, RAG pipelines
|
>> Snowflake GES-C02日本語対策問題集 <<
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質問 # 57
Which workflow should be used to analyze data in unstructured documents with Snowflake Cortex AI features, including Retrieval-Augmented Generation (RAG)?
正解：B
解説：
Constructing an enterprise Retrieval-Augmented Generation (RAG) architecture over unstructured files in Snowflake requires a multi-stage data engineering pipeline. Because foundation models cannot parse raw binary documents directly via SQL functions, text content must first be systematically ingested and transformed.
The canonical Snowflake workflow follows three structured operations:
* Extraction: Staged unstructured files (such as PDF, DOCX, or scanned images) are processed using SNOWFLAKE.CORTEX.PARSE_DOCUMENT to extract raw textual content and structural layout representations into text fields.
* Vectorization and Storage: The extracted text is partitioned into semantically cohesive passages, which are vectorized using embedding functions such as SNOWFLAKE.CORTEX.EMBED_TEXT_768 (or EMBED_TEXT_1024) and indexed into Snowflake tables using the native VECTOR data type.
* Retrieval and Inference: At query runtime, incoming user prompts are converted into query vectors, matched against document chunks via vector distance functions (e.g., VECTOR_COSINE_SIMILARITY), and the top semantic chunks are injected as contextual grounding into SNOWFLAKE.CORTEX.COMPLETE to generate verified answers.
The alternative workflows are invalid because embedding and sentiment functions cannot accept raw binary files directly.
References & Study Guide Topics:
* Snowflake Document Processing: Unstructured Ingestion via PARSE_DOCUMENT
* Generative AI Architectures: End-to-End RAG Pipelines with Embeddings and Completion
質問 # 58
A Gen AI Specialist is building a pipeline in Snowflake to gather data that will be used to summarize customer chat messages.
The following table, CHAT_MESSAGES, was created:
| CONVERSATION_ID | MESSAGE_TEXT |
| :--- | :--- |
| 1 | Hi, I need help with my order. |
| 2 | It's not been delivered yet. |
| 2 | How can I reset my password? |
| 3 | I received the wrong item. |
| 3 | Can I exchange it? |
The Specialist wants to use the Snowflake Cortex COMPLETE function with the llama3-8b model to generate a summary of each conversation. The prompt should begin with: "Summarize this chat:" followed by the full text of all messages in that chat conversation.
Which query will achieve this?
```sql
正解：A
解説：
In relational schemas storing chat histories, conversations span multiple rows keyed by a common
`CONVERSATION_ID`. Generating a single summary per conversation requires aggregating all associated message rows before passing the text into the foundation model.
Option C correctly combines SQL aggregation with Cortex inference:
1. `GROUP BY conversation_id` rolls the dataset up so each conversation is processed as a single record.
2. `LISTAGG(message_text)` concatenates all message strings for that conversation into a unified text transcript.
3. The concatenated transcript is appended to the instruction `'Summarize this chat:'` and passed to
`SNOWFLAKE.CORTEX.COMPLETE('llama3-8b', ...)` to generate a conversation-level summary.
Option A executes inference on individual messages rather than full conversations. Option B fails to include the mandatory `GROUP BY` clause required for `ARRAY_AGG`. Option D omits both message aggregation and the required prompt instruction prefix.
References & Study Guide Topics:
* Snowflake Gen AI Functions: Aggregating Tabular Data for LLM Context
* SQL Feature Composition: Combining `LISTAGG` and `GROUP BY` with Cortex Functions
質問 # 59
A Gen AI Specialist is developing a Gen AI application in Snowflake and must debug a particular record that produced an inaccurate response. The Specialist needs to see, step by step, how the input prompt was processed, what context was retrieved, which tools were called, and how the LLM responded.
Which Snowflake AI observability feature should be used?
正解：D
質問 # 60
What role do event tables serve in the Snowflake Cortex Analyst observability features?
正解：C
解説：
Snowflake Cortex Analyst integrates natively with Snowflake's telemetry infrastructure to capture granular operational telemetry. The primary mechanism for persisting and querying this telemetry is the **Event Table**, which uses standard OpenTelemetry log and trace standards.
When an application or user interacts with Cortex Analyst via its REST API or Snowsight interface, Snowflake automatically emits structured event records into the account's configured event table. These logs capture comprehensive details for every interaction turn, including:
* The user's input question and chronological dialogue turns.
* The ANSI SQL statements generated by the underlying text-to-SQL reasoning agents.
* Operational warnings, errors, processing latencies, request identifiers, and raw API request/response payloads.
Administrators and engineers use these logged events to audit analytical activity, detect SQL generation failures, evaluate query accuracy, and fine-tune semantic model specifications. Event tables do not serve as semantic model version archives, nor do they capture generic user authentication sessions or warehouse hardware performance metrics.
References & Study Guide Topics:
* Snowflake Gen AI Governance: Telemetry and Event Table Architecture
* Cortex Analyst Observability: Auditing Generated SQL, Requests, and Error Logs
質問 # 61
A team has developed a machine learning model outside of Snowflake using a third-party framework. The model needs to be deployed within Snowflake to support:
* Centralized version control
* Metadata tracking
Which Snowflake feature should be used to meet these requirements?
正解：D
解説：
The Snowflake Model Registry provides centralized machine learning lifecycle governance and deployment inside the Snowflake Data Cloud. Designed to manage models regardless of where they were trained, it natively supports logging artifacts from third-party frameworks, including scikit-learn, PyTorch, TensorFlow, XGBoost, and Hugging Face.
The Model Registry satisfies both organizational requirements:
1. **Centralized Version Control:** Models are stored as schema-level entities that support multiple version deployments under a unified parent object. Teams can manage versions (e.g., v1, v2), assign default versions, and govern rollbacks through standard SQL or Python commands.
2. **Metadata Tracking:** During logging, the Model Registry captures operational metadata, such as training dataset references, performance metrics, input/output schemas, target runtime dependencies, and custom tags. This metadata remains queryable and governed under Snowflake's RBAC framework.
ML Observability focuses on monitoring production models for data drift and latency; Container Runtime provides execution compute for training jobs; and the Snowflake Feature Store manages feature pipelines rather than versioning model artifacts.
References & Study Guide Topics:
* Snowflake Gen AI Governance: Snowflake Model Registry Governance and MLOps
* Enterprise Model Management: Artifact Versioning and Metadata Tracking
質問 # 62
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