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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Generative AI Engineer Associate |
| Exam Number: | Generative AI Engineer Associate |
| Exam Format: | Multiple Choice, Multiple Select |
| Passing Score: | Scaled Score (approx. 70%) |
| Related Certifications: | Databricks Certified Data Engineer Associate Databricks Certified Machine Learning Associate |
| Available Languages: | Japanese, Korean, English, Portuguese (Brazil) |
| Real Exam Qty: | 45 |
| Exam Duration: | 90 minutes |
| Certificate Validity Period: | 2 years |
| Exam Price: | $200 USD |
| Sample Questions: | Databricks Databricks-Generative-AI-Engineer-Associate Sample Questions |
| Exam Way: | Online (Proctored) or Test Center |
| Pre Condition: | None (Recommended: 6+ months hands-on experience) |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/genai-engineer-associate |
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NEW QUESTION # 87
Which indicator should be considered to evaluate the safety of the LLM outputs when qualitatively assessing LLM responses for a translation use case?
Answer: B
Explanation:
* Problem Context: When assessing the safety and effectiveness of LLM outputs in a translation use case, it is essential to ensure that the translations accurately and relevantly convey the intended message. The evaluation should focus on how well the LLM understands and processes different languages and contexts.
* Explanation of Options:
* Option A: The ability to generate responses in code- This is not relevant to translation quality or safety.
* Option B: The similarity to the previous language- While ensuring that translations preserve the original's intent is important, this doesn't directly address the overall quality or safety of the translation.
* Option C: The latency of the response and the length of text generated- These operational metrics are less critical in assessing the qualitative aspects of translation safety.
* Option D: The accuracy and relevance of the responses- This is crucial in translation to ensure that the translated content is true to the original in meaning and appropriateness. Accuracy and relevance directly impact the effectiveness and safety of translations, especially in sensitive or nuanced contexts.
Thus,Option Dis the most important indicator when evaluating the safety of LLM outputs in translation, focusing on the core aspects that determine the utility and trustworthiness of translated content.
NEW QUESTION # 88
A team wants to serve a code generation model as an assistant for their software developers. It should support multiple programming languages. Quality is the primary objective.
Which of the Databricks Foundation Model APIs, or models available in the Marketplace, would be the best fit?
Answer: A
Explanation:
For a code generation model that supports multiple programming languages and where quality is the primary objective,CodeLlama-34Bis the most suitable choice. Here's the reasoning:
* Specialization in Code Generation:CodeLlama-34B is specifically designed for code generation tasks.
This model has been trained with a focus on understanding and generating code, which makes it particularly adept at handling various programming languages and coding contexts.
* Capacity and Performance:The "34B" indicates a model size of 34 billion parameters, suggesting a high capacity for handling complex tasks and generating high-quality outputs. The large model size typically correlates with better understanding and generation capabilities in diverse scenarios.
* Suitability for Development Teams:Given that the model is optimized for code, it will be able to assist software developers more effectively than general-purpose models. It understands coding syntax, semantics, and the nuances of different programming languages.
* Why Other Options Are Less Suitable:
* A (Llama2-70b): While also a large model, it's more general-purpose and may not be as fine- tuned for code generation as CodeLlama.
* B (BGE-large): This model may not specifically focus on code generation.
* C (MPT-7b): Smaller than CodeLlama-34B and likely less capable in handling complex code generation tasks at high quality.
Therefore, for a high-quality, multi-language code generation application,CodeLlama-34B(option D) is the best fit.
NEW QUESTION # 89
A Generative AI Engineer has been reviewing issues with their company ' s LLM-based question-answering assistant and has determined that a technique called prompt chaining could help alleviate some performance concerns. However, to suggest this to their team, they have to clearly explain how it works and how it can benefit their question-answering assistant. Which explanation do they communicate to the team?
Answer: D
Explanation:
Prompt chaining is a fundamental design pattern in LLM application development used to handle complexity.
Instead of sending a single, massive, and highly complex prompt to an LLM-which often results in reasoning errors or hallucinations-chaining breaks the logic into a sequence of smaller, targeted steps. For example, a legal assistant might first chain a step to " identify the legal jurisdiction, " followed by a step to " extract relevant statutes, " and finally a step to " summarize the findings. " This modularity improves reliability because each prompt has a narrower focus, making it easier for the model to follow instructions accurately. While it may actually increase latency (contradicting B) and cost (contradicting D) due to multiple API calls, the primary engineering benefit is the significant boost in the quality and robustness of the output.
It also allows for intermediate validation and error handling between steps, which is impossible in a single- call architecture.
NEW QUESTION # 90
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?
Answer: D
Explanation:
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.
NEW QUESTION # 91
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?
Answer: C
NEW QUESTION # 92
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