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Databricks Databricks-Generative-AI-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Evaluation and Monitoring	12%	- Iterate and improve solutions - Monitor application behavior and outputs - Evaluate model and application performance
Data Preparation	14%	- Prepare and process data for LLM use - Implement data pipelines and transformations - Manage data quality and format
Assembling and Deploying Apps	22%	- Package and deploy applications - Manage application lifecycle - Deploy models via Model Serving
Design Applications	14%	- Select appropriate models, tools and approaches - Decompose complex requirements into tasks - Design solution architecture
Governance	8%	- Apply Unity Catalog for data governance - Manage access and permissions - Ensure compliance and security
Application Development	30%	- Use Databricks Vector Search - Build RAG applications - Develop LLM chains and workflows - Integrate with MLflow

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Databricks Certified Generative AI Engineer Associate Databricks-Generative-AI-Engineer-Associate Prüfungsfragen mit Lösungen (Q16-Q21):

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

Antwort: A

Begründung:
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

17. Frage
A Generative AI Engineer just deployed an LLM application at a digital marketing company that assists with answering customer service inquiries.
Which metric should they monitor for their customer service LLM application in production?

A. Final perplexity scores for the training of the model
B. Number of customer inquiries processed per unit of time
C. Energy usage per query
D. HuggingFace Leaderboard values for the base LLM

Antwort: B

Begründung:
When deploying an LLM application for customer service inquiries, the primary focus is on measuring the operational efficiency and quality of the responses. Here's whyAis the correct metric:
* Number of customer inquiries processed per unit of time: This metric tracks the throughput of the customer service system, reflecting how many customer inquiries the LLM application can handle in a given time period (e.g., per minute or hour). High throughput is crucial in customer service applications where quick response times are essential to user satisfaction and business efficiency.
* Real-time performance monitoring: Monitoring the number of queries processed is an important part of ensuring that the model is performing well under load, especially during peak traffic times. It also helps ensure the system scales properly to meet demand.
Why other options are not ideal:
* B. Energy usage per query: While energy efficiency is a consideration, it is not the primary concern for a customer-facing application where user experience (i.e., fast and accurate responses) is critical.
* C. Final perplexity scores for the training of the model: Perplexity is a metric for model training, but it doesn't reflect the real-time operational performance of an LLM in production.
* D. HuggingFace Leaderboard values for the base LLM: The HuggingFace Leaderboard is more relevant during model selection and benchmarking. However, it is not a direct measure of the model's performance in a specific customer service application in production.
Focusing on throughput (inquiries processed per unit time) ensures that the LLM application is meeting business needs for fast and efficient customer service responses.

18. Frage
A Generative Al Engineer is building an LLM-based application that has an important transcription (speech-to-text) task. Speed is essential for the success of the application Which open Generative Al models should be used?

A. whisper-large-v3 (1.6B)
B. DBRX
C. L!ama-2-70b-chat-hf
D. MPT-30B-lnstruct

Antwort: A

Begründung:
The task requires an open generative AI model for a transcription (speech-to-text) task where speed is essential. Let's assess the options based on their suitability for transcription and performance characteristics, referencing Databricks' approach to model selection.
Option A: Llama-2-70b-chat-hf
Llama-2 is a text-based LLM optimized for chat and text generation, not speech-to-text. It lacks transcription capabilities.
Databricks Reference: "Llama models are designed for natural language generation, not audio processing" ("Databricks Model Catalog").
Option B: MPT-30B-Instruct
MPT-30B is another text-based LLM focused on instruction-following and text generation, not transcription. It's irrelevant for speech-to-text tasks.
Databricks Reference: No specific mention, but MPT is categorized under text LLMs in Databricks' ecosystem, not audio models.
Option C: DBRX
DBRX, developed by Databricks, is a powerful text-based LLM for general-purpose generation. It doesn't natively support speech-to-text and isn't optimized for transcription.
Databricks Reference: "DBRX excels at text generation and reasoning tasks" ("Introducing DBRX," 2023)-no mention of audio capabilities.
Option D: whisper-large-v3 (1.6B)
Whisper, developed by OpenAI, is an open-source model specifically designed for speech-to-text transcription. The "large-v3" variant (1.6 billion parameters) balances accuracy and efficiency, with optimizations for speed via quantization or deployment on GPUs-key for the application's requirements.
Databricks Reference: "For audio transcription, models like Whisper are recommended for their speed and accuracy" ("Generative AI Cookbook," 2023). Databricks supports Whisper integration in its MLflow or Lakehouse workflows.
Conclusion: Only D. whisper-large-v3 is a speech-to-text model, making it the sole suitable choice. Its design prioritizes transcription, and its efficiency (e.g., via optimized inference) meets the speed requirement, aligning with Databricks' model deployment best practices.

19. Frage
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.

Antwort: A

Begründung:
* 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.

20. Frage
A Generative AI Engineer is testing a simple prompt template in LangChain using the code below, but is getting an error:
Python
from langchain.chains import LLMChain
from langchain_community.llms import OpenAI
from langchain_core.prompts import PromptTemplate
prompt_template = " Tell me a {adjective} joke "
prompt = PromptTemplate(input_variables=[ " adjective " ], template=prompt_template)
# ... (Error-prone section)
Assuming the API key was properly defined, what change does the Generative AI Engineer need to make to fix their chain?

A. prompt_template = " Tell me a {adjective} joke "
prompt = PromptTemplate(input_variables=[ " adjective " ], template=prompt_template) llm = OpenAI() llm_chain = LLMChain(prompt=prompt, llm=llm) llm_chain.generate([{ " adjective " : " funny " }])
B. (Incorrect structure)
C. (Incorrect structure)
D. (Incorrect structure)

Antwort: A

21. Frage
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