Databricks-Generative-AI-Engineer-Associate在線考題 & Databricks-Generative-AI-Engineer-Associate考古題介紹

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Databricks-Generative-AI-Engineer-Associate在線考題, Databricks-Generative-AI-Engineer-Associate考古題介紹, Databricks-Generative-AI-Engineer-Associate資訊, Databricks-Generative-AI-Engineer-Associate最新試題, Databricks-Generative-AI-Engineer-Associate套裝

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Databricks Databricks-Generative-AI-Engineer-Associate 考試大綱：

主題 簡介
主題 1
  • Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal
  • licensing requirements in this topic.
主題 2
  • Assembling and Deploying Applications: In this topic, Generative AI Engineers get knowledge about coding a chain using a pyfunc mode, coding a simple chain using langchain, and coding a simple chain according to requirements. Additionally, the topic focuses on basic elements needed to create a RAG application. Lastly, the topic addresses sub-topics about registering the model to Unity Catalog using MLflow.
主題 3
  • Design Applications: The topic focuses on designing a prompt that elicits a specifically formatted response. It also focuses on selecting model tasks to accomplish a given business requirement. Lastly, the topic covers chain components for a desired model input and output.

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Databricks-Generative-AI-Engineer-Associate考古題介紹 & Databricks-Generative-AI-Engineer-Associate資訊

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最新的 Generative AI Engineer Databricks-Generative-AI-Engineer-Associate 免費考試真題 (Q64-Q69):

問題 #64
A Generative AI Engineer is experimenting with using parameters to configure an agent in Mosaic Agent Framework. However, they are struggling to get the agent to respond with relevant information with this configuration:
config = { " prompt_template " : " You are a trivia bot. Generate a question based on the user ' s input:
{user_input} " , " input_vars " : [ " user_input " ], " parameters " : { " temperature " : 0.01, " max_tokens " :
500}}
Which error is causing the problem?

  • A. The prompt does not set the retriever schema
  • B. The prompt does not list available agents for the LLM to call
  • C. The prompt is not wrapped in ChatModel
  • D. The prompt does not parse the user ' s input vars

答案：D

解題說明：
In the Mosaic AI Agent Framework and underlying LangChain-based configurations, the " input_vars " or " input_variables " must be correctly mapped and referenced within the template. If the configuration dictionary identifies user_input as the variable but the logic executing the chain does not correctly " inject " the runtime value into the {user_input} placeholder, the LLM will receive a literal string (or an empty value) rather than the user ' s actual question. This results in the model failing to provide relevant information because it essentially doesn ' t know what the user asked. Engineering standards require ensuring that the key used in the input_vars list matches the key in the JSON payload sent to the model serving endpoint. If there is a mismatch or a failure to parse, the prompt remains static, leading to generic or irrelevant responses.


問題 #65
A Generative Al Engineer is deciding between using LSH (Locality Sensitive Hashing) and HNSW (Hierarchical Navigable Small World) for indexing their vector database Their top priority is semantic accuracy Which approach should the Generative Al Engineer use to evaluate these two techniques?

  • A. Compare the Recall-Onented-Understudy for Gistmg Evaluation (ROUGE) scores of returned results for a representative sample of test inputs
  • B. Compare the cosine similarities of the embeddings of returned results against those of a representative sample of test inputs
  • C. Compare the Bilingual Evaluation Understudy (BLEU) scores of returned results for a representative sample of test inputs
  • D. Compare the Levenshtein distances of returned results against a representative sample of test inputs

答案：B


問題 #66
A Generative AI Engineer needs to allocate costs for an agent deployed via Agent Framework behind AI Gateway. The finance team requires daily reporting by workspace and endpoint, including token usage and request counts. The engineer wants to query this data with SQL and join it to an internal cost-center mapping table.
Which table type best serves their needs?

  • A. System Serving, Served Entities table.
  • B. MLflow experiment run tables.
  • C. AI Gateway usage table.
  • D. Inference Tables.

答案：C

解題說明：
The AI Gateway usage table is designed to capture operational consumption information for SQL-based reporting. Its records include identifiers for the workspace, endpoint, and request, together with token-usage information and timestamps. The engineer can aggregate consumption by day and endpoint, then join those results to the company's cost-center mapping. Request counting should respect the table's request and invocation identifiers because a single logical request can involve multiple inference calls. Served-entity metadata describes deployed entities rather than providing the primary consumption records. Inference tables emphasize request and response payloads, while MLflow runs organize experiments and evaluations. Token consumption supports cost allocation, but calculating actual monetary charges may additionally require applicable pricing or billing records. Databricks documentation


問題 #67
A Generative Al Engineer is setting up a Databricks Vector Search that will lookup news articles by topic within 10 days of the date specified An example query might be "Tell me about monster truck news around January 5th 1992". They want to do this with the least amount of effort.
How can they set up their Vector Search index to support this use case?

  • A. Split articles by 10 day blocks and return the block closest to the query.
  • B. Create separate indexes by topic and add a classifier model to appropriately pick the best index.
  • C. Include metadata columns for article date and topic to support metadata filtering.
  • D. pass the query directly to the vector search index and return the best articles.

答案：C

解題說明：
The task is to set up a Databricks Vector Search index for news articles, supporting queries like "monster truck news around January 5th, 1992," with minimal effort. The index must filter by topic and a 10-day date range. Let's evaluate the options.
* Option A: Split articles by 10-day blocks and return the block closest to the query
* Pre-splitting articles into 10-day blocks requires significant preprocessing and index management (e.g., one index per block). It's effort-intensive and inflexible for dynamic date ranges.
* Databricks Reference:"Static partitioning increases setup complexity; metadata filtering is preferred"("Databricks Vector Search Documentation").
* Option B: Include metadata columns for article date and topic to support metadata filtering
* Adding date and topic as metadata in the Vector Search index allows dynamic filtering (e.g., date
± 5 days, topic = "monster truck") at query time. This leverages Databricks' built-in metadata filtering, minimizing setup effort.
* Databricks Reference:"Vector Search supports metadata filtering on columns like date or category for precise retrieval with minimal preprocessing"("Vector Search Guide," 2023).
* Option C: Pass the query directly to the vector search index and return the best articles
* Passing the full query (e.g., "Tell me about monster truck news around January 5th, 1992") to Vector Search relies solely on embeddings, ignoring structured filtering for date and topic. This risks inaccurate results without explicit range logic.
* Databricks Reference:"Pure vector similarity may not handle temporal or categorical constraints effectively"("Building LLM Applications with Databricks").
* Option D: Create separate indexes by topic and add a classifier model to appropriately pick the best index
* Separate indexes per topic plus a classifier model adds significant complexity (index creation, model training, maintenance), far exceeding "least effort." It's overkill for this use case.
* Databricks Reference:"Multiple indexes increase overhead; single-index with metadata is simpler"("Databricks Vector Search Documentation").
Conclusion: Option B is the simplest and most effective solution, using metadata filtering in a single Vector Search index to handle date ranges and topics, aligning with Databricks' emphasis on efficient, low-effort setups.


問題 #68
A Generative Al Engineer would like an LLM to generate formatted JSON from emails. This will require parsing and extracting the following information: order ID, date, and sender email. Here's a sample email:

They will need to write a prompt that will extract the relevant information in JSON format with the highest level of output accuracy.
Which prompt will do that?

  • A. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in JSON format.
    Here's an example: {"date": "April 16, 2024", "sender_email": "sarah.lee925@gmail.com", "order_id": "RE987D"}
  • B. You will receive customer emails and need to extract date, sender email, and order ID. You should return the date, sender email, and order ID information in JSON format.
  • C. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in a human-readable format.
  • D. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in JSON format.

答案：A

解題說明：
* Problem Context: The goal is to parse emails to extract certain pieces of information and output this in a structured JSON format. Clarity and specificity in the prompt design will ensure higher accuracy in the LLM's responses.
* Explanation of Options:
Option A: Provides a general guideline but lacks an example, which helps an LLM understand the exact format expected.
Option B: Includes a clear instruction and a specific example of the output format. Providing an example is crucial as it helps set the pattern and format in which the information should be structured, leading to more accurate results.
Option C: Does not specify that the output should be in JSON format, thus not meeting the requirement.
Option D: While it correctly asks for JSON format, it lacks an example that would guide the LLM on how to structure the JSON correctly.
Therefore, Option B is optimal as it not only specifies the required format but also illustrates it with an example, enhancing the likelihood of accurate extraction and formatting by the LLM.


問題 #69
......

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