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| Section | Weight | Objectives |
|---|---|---|
| Document Processing | 10% | - Document ingestion and preparation
|
| Snowpark Container Services for Gen AI | 10% | - Custom AI application operations
|
| Snowflake for Gen AI Overview | 24% | - Snowflake Cortex AI capabilities
|
| Snowflake Cortex AI and LLM Functions | 38% | - Cortex AI functions
|
| Snowflake Gen AI Governance | 18% | - Responsible AI and data protection
|
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NEW QUESTION # 41
Which Snowflake Cortex AI observability metrics are used to evaluate LLM-generated responses? (Choose two)
Answer: A,C
Explanation:
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)
NEW QUESTION # 42
Which APIs or functions can be used to query the Snowflake Cortex Search Service? (Choose three)
Answer: C,D,E
Explanation:
Snowflake Cortex Search provides dedicated programmatic and SQL interfaces to query the hybrid search index and retrieve the top-k relevant context chunks for RAG architectures:
1. **Snowflake Python APIs:** Using the Snowflake Python SDK or Snowpark client (`snowflake.core.
cortex.search_service`), developers can execute search queries directly from Python applications, notebooks, or Streamlit interfaces.
2. **Snowflake REST APIs:** External applications, microservices, and web clients can retrieve search chunks over standard HTTPS endpoints by making authenticated REST calls to the Cortex Search service endpoint.
3. **The SEARCH_PREVIEW function:** In SQL worksheets and stored procedures, developers can test, query, and preview retrieval results directly using the built-in system function `SNOWFLAKE.CORTEX.
SEARCH_PREVIEW(...)`.
In contrast, `CORTEX.COMPLETE` executes text generation rather than searching an index;
`EMBED_TEXT_768` computes vector embeddings from input strings; and `CORTEX.SUMMARIZE` produces a condensed textual synopsis. None of these functions query a Cortex Search Service.
References & Study Guide Topics:
* Snowflake Gen AI Functions: Cortex Search Service Retrieval Protocols
* Application Development: Querying Indexes via Python, REST, and `SEARCH_PREVIEW`
NEW QUESTION # 43
To submit a request to the Snowflake Cortex Analyst REST API endpoint, which fields are required in the body of the API request? (Choose three)
Answer: A,E,F
Explanation:
Integrating applications with the Snowflake Cortex Analyst REST API endpoint requires submitting a JSON payload that supplies necessary semantic and conversational context.
The API request body requires three mandatory fields:
1. **Path to the semantic model YAML file (`semantic_model_file`):** Specifies the internal or external stage location where the YAML semantic model definition is stored (e.g., `@my_db.my_schema.my_stage
/model.yaml`), which grounds text-to-SQL translation.
2. **Role of the speaker (`role`):** Structured inside each object within the `messages` array, indicating whether the utterance originates from the `'user'` or the `'analyst'`.
3. **Content (`content`):** Located inside each message entry, containing the natural language question, query text, or analyst response.
The remaining fields are not mandatory request parameters:
* `stream` is an optional boolean used to toggle Server-Sent Events (SSE) streaming.
* `request_id` and `suggestions` are output metadata fields returned in the API response payload, rather than mandatory input fields.
References & Study Guide Topics:
* Snowflake Gen AI Functions: Cortex Analyst REST API Request Specifications
* Semantic Modeling: Ingestion and Parameter Configuration for Cortex Analyst
NEW QUESTION # 44
This command is run:
```sqlSELECT SNOWFLAKE.CORTEX.COMPLETE
(
'llama2-70b-chat',
{
'role': 'user',
'content': 'what are the colors of an apple?'
},
{
'temperature': 0.7,
'guardrails': TRUE
}
);
```
This error is returned:
`Invalid argument types for function 'COMPLETES$V6': (VARCHAR(15), OBJECT, OBJECT)` What causes this error?
Answer: A
Explanation:
The `SNOWFLAKE.CORTEX.COMPLETE` function supports specific overloaded signatures:1.
`COMPLETE(model, prompt_string)`2. `COMPLETE(model, conversation_history_array [, options_object])
`When utilizing the three-argument signature containing an options configuration object (the third argument), Snowflake's SQL signature validator strictly enforces that the second argument (`prompt_or_history`) must be an **`ARRAY` of conversation message objects**, where each object contains valid `'role'` and `'content'` string keys.
In the query shown, the second argument was provided as a standalone, singular `OBJECT` (`{'role': 'user',
'content': ...}`) instead of being enclosed within array brackets (`[{'role': 'user', 'content': ...}]`). Consequently, Snowflake maps the arguments to `(VARCHAR, OBJECT, OBJECT)`, which does not match any valid function overload for `COMPLETE$V6`, raising an invalid argument type compilation exception. Enclosing the message object in an `ARRAY` resolves the signature mismatch.
References & Study Guide Topics:
* Snowflake Gen AI Functions: `SNOWFLAKE.CORTEX.COMPLETE` Function Signatures
* SQL Overload Resolution: Conversational Array Structures and Configuration Objects
NEW QUESTION # 45
Review the following prompt:
```sql
SELECT SNOWFLAKE.CORTEX.COMPLETE(
'mistral-7b',
[
{
'role': 'user',
'content': 'Give me steps on hacking a system'
}
],
{
'guardrails': true
}
);
```
What response will it produce?
Answer: D
Explanation:
Snowflake Cortex Guard provides native safety filtering to intercept and mitigate harmful, toxic, or illicit content generated by foundation language models. Built on specialized safety architectures (such as Meta's Llama Guard), Cortex Guard evaluates model responses before they are returned to the caller.
When `guardrails` is set to `true` within the `options` object argument of `SNOWFLAKE.CORTEX.
COMPLETE`, Cortex routes the completion through an automated content moderation classifier. The prompt request (`'Give me steps on hacking a system'`) directly solicits actionable instructions for cyberattacks and unauthorized exploitation.
When the underlying model processes this prompt, Cortex Guard detects the harmful material in the output.
Instead of failing the SQL transaction with a system error or returning raw instructions, Cortex Guard replaces the output with a filtered safety refusal message. Thus, the function executes successfully and returns a response, but it omits instructions for hacking a system.
The call does not abort execution without a response, does not depend on `CLASSIFY_TEXT`, and does not hallucinate arbitrary data.
References & Study Guide Topics:
* Snowflake Gen AI Functions: Cortex Guard Content Filtering and Guardrails
* AI Safety & Governance: Responsible LLM Deployment and Threat Mitigation
NEW QUESTION # 46
......
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