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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowflake Gen AI and LLM Functions | 38% | - Cortex AI Functions
|
| Topic 2: Snowflake Document Processing | 15% | - Document Processing Pipelines
|
| Topic 3: Snowflake for Gen AI Overview | 18% | - Generative AI Fundamentals and Snowflake Cortex
|
| Topic 4: Snowflake Gen AI Governance | 29% | - AI Quality and Observability
|
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質問 # 79
Which Snowflake data type is returned by the SNOWFLAKE.CORTEX.EMBED_TEXT_768 function?
正解：A
解説：
Snowflake provides dedicated vector embedding functions-such as `SNOWFLAKE.CORTEX.
EMBED_TEXT_768`-to transform unstructured text into numerical vector representations for semantic search and Retrieval-Augmented Generation (RAG).
The `EMBED_TEXT_768` function processes an input string using supported 768-dimension embedding architectures (such as `e5-base-v2` or `snowflake-arctic-embed-m`) and returns the embedding as Snowflake's native `VECTOR` data type-specifically `VECTOR(FLOAT, 768)`.
The `VECTOR` data type is optimized to store fixed-length arrays of floating-point values in a compact binary representation within micro-partitions. This format allows native vector functions-such as
`VECTOR_COSINE_SIMILARITY`, `VECTOR_L1_DISTANCE`, and `VECTOR_L2_DISTANCE`-to run hardware-accelerated vector mathematics directly inside virtual warehouses.
While embeddings can be cast to generic semi-structured types like `ARRAY` or `VARIANT`, the native output returned by `EMBED_TEXT_768` is strictly a `VECTOR`. It is not returned as a `VARCHAR`,
`VARIANT`, or `OBJECT`.
References & Study Guide Topics:
* Snowflake Gen AI Functions: Vector Embeddings and Ingestion
* Data Types: Native `VECTOR(FLOAT, dim)` Structures and Optimizations
質問 # 80
A Generative AI Specialist builds a model using Python libraries. After training the model, they log it into a Snowflake Model Registry.
Where is the Model Registry stored within Snowflake?
正解：A
解説：
In Snowflake, machine learning models are integrated into the data architecture as schema-level entities.
When a developer logs a trained model using the Snowpark ML API (`Registry.log_model()`), the model is registered inside a designated **database and schema as a first-class Snowflake object**.
Because models are first-class schema objects, they inherit standard Snowflake management and governance capabilities:
* They can be inspected and managed using standard DDL commands, such as `SHOW MODELS`, `ALTER MODEL`, and `DROP MODEL`.
* Access control is enforced via Snowflake Role-Based Access Control (RBAC), using permissions like
`USAGE`, `READ`, and `OWNERSHIP` granted directly on the model entity.
* Models can be invoked for inference directly within SQL statements using ` < model_name > ! < method_name > ()` syntax, without requiring external hosting services.
Models are not stored as standalone XML files in stages, are not stored in image repositories, and are not scoped only to web monitoring views.
References & Study Guide Topics:
* Snowflake for Gen AI Overview: Snowflake Model Registry Storage Architecture
* Schema Objects: First-Class ML Entities and RBAC Integration
```
質問 # 81
Which Snowflake Cortex AI observability metrics are used to evaluate LLM-generated responses? (Choose two)
正解：B、E
解説：
Snowflake Cortex AI Observability provides specialized, automated evaluation metrics designed to systematically assess the quality, safety, and reliability of large language model completions in production and evaluation workflows.
Two core LLM evaluation metrics native to Snowflake Cortex are:
* Correctness: Measures factual alignment and semantic fidelity between the LLM-generated output and an established ground-truth response or reference context, identifying factual discrepancies, omissions, or model hallucinations.
* Coherence: Evaluates the structural and logical integrity of the generated output, verifying that the text is well-organized, fluent, logically consistent, and free of contradictions or abrupt narrative breaks.
Traditional statistical classification metrics-such as Accuracy, Precision, and F1 Score-are mathematical indicators used to evaluate supervised classification models against rigid true/false categorical labels; they are not the qualitative, generative assessment metrics implemented in Cortex AI Observability for evaluating unstructured LLM response text.
References & Study Guide Topics:
* Snowflake Gen AI Functions: Cortex AI Observability Evaluation Framework
* LLM Quality Benchmarking: Qualitative Metrics (Correctness, Coherence, Hallucination Detection)
質問 # 82
What type of costs are incurred when you use the Snowflake Cortex Search Service?
正解：A
解説：
Snowflake Cortex Search Services employ a specialized cost model designed around keeping search indexes ready for sub-second, multi-tenant semantic queries. The distinct primary cost category explicitly defined and billed for this operational readiness is serving compute.
Serving compute covers the cost of hosting the search index in high-performance serving infrastructure. It is billed based on the total uncompressed storage size of the indexed data (including the generated dense vector embeddings and sparse lexical tokens) on a per-gigabyte, per-month basis. Crucially, serving compute accrues continuously as long as the Cortex Search Service is active, independent of actual query invocation volume.
This ensures that when a client application, Streamlit app, or RAG pipeline queries the service, results can be retrieved without cold-start latency.
Compute pools are provisioned infrastructure units used strictly within Snowpark Container Services, whereas data transfer applies to egress across cloud provider regions. While Cortex Search relies on serverless operations behind the scenes, Snowflake formally designates the core running cost category as serving compute.
References & Study Guide Topics:
* Snowflake Gen AI Functions: Cortex Search Service Cost Models
* Resource Sizing: Serving Compute Metering and Lifecycle Management
質問 # 83
Which arguments are required for the SNOWFLAKE.CORTEX.COMPLETE function? (Choose two)
正解：B、C
解説：
Comprehensive and Detailed 150 to 250 words of Explanation From [SnowPro Specialty: Gen AI/Course Guide/topics]:
The `SNOWFLAKE.CORTEX.COMPLETE` function provides general-purpose foundation model inference through two primary function signatures:
1. `SNOWFLAKE.CORTEX.COMPLETE(model, prompt)` for single-turn scalar text interactions.
2. `SNOWFLAKE.CORTEX.COMPLETE(model, conversation_history [, options])` for conversational multi- turn workflows.
Across both variants, the function strictly requires two mandatory positional arguments:
* **`model`:** A string designating the target model family and size (e.g., `'llama3.1-70b'`, `'mistral-large2'`, or `'snowflake-arctic'`).
* **`prompt_or_history`:** Either a scalar string representing the input prompt or an array of message objects containing `role` and `content` attributes representing the cumulative conversational history.
All other parameters-such as `top_p`, `temperature`, `max_tokens`, and `response_format`-are completely optional configuration hyperparameters. They are passed together as key-value pairs within an optional third
`OBJECT` argument (`options`) to fine-tune generation behavior, and their omission does not prevent successful function execution.
References & Study Guide Topics:
* Snowflake Gen AI Functions: `SNOWFLAKE.CORTEX.COMPLETE` Signature and Syntax
* Cortex Foundation Models: Mandatory Arguments vs. Optional Hyperparameter Objects
質問 # 84
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