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Databricks Databricks-Generative-AI-Engineer-Associate Exam Overview:

Certification Vendor:Databricks
Exam Name:Databricks Certified Generative AI Engineer Associate Exam
Exam Number:Databricks-Generative-AI-Engineer-Associate
Certificate Validity Period:2 years
Exam Price:USD 200
Real Exam Qty:45
Exam Duration:90 minutes
Available Languages:English, Brazilian Portuguese, Japanese, Korean
Passing Score:700/1000 or 70%
Exam Format:Multiple choice
Recommended Training:Generative AI Engineering with Databricks
Exam Registration:Databricks Certification Registration
Sample Questions:Databricks Databricks-Generative-AI-Engineer-Associate Sample Questions
Exam Way:Online proctored or in-person test center
Pre Condition:No formal prerequisites; recommended 6+ months hands-on experience building generative AI solutions
Official Syllabus URL:https://www.databricks.com/learn/certification/genai-engineer-associate

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

TopicDetails
Topic 1
  • Data Preparation: Generative AI Engineers covers a chunking strategy for a given document structure and model constraints. The topic also focuses on filter extraneous content in source documents. Lastly, Generative AI Engineers also learn about extracting document content from provided source data and format.
Topic 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.
Topic 3
  • Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal
  • licensing requirements in this topic.
Topic 4
  • Application Development: In this topic, Generative AI Engineers learn about tools needed to extract data, Langchain
  • similar tools, and assessing responses to identify common issues. Moreover, the topic includes questions about adjusting an LLM's response, LLM guardrails, and the best LLM based on the attributes of the application.
Topic 5
  • Evaluation and Monitoring: This topic is all about selecting an LLM choice and key metrics. Moreover, Generative AI Engineers learn about evaluating model performance. Lastly, the topic includes sub-topics about inference logging and usage of Databricks features.

Databricks Certified Generative AI Engineer Associate Sample Questions (Q80-Q85):

NEW QUESTION # 80
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: A


NEW QUESTION # 81
A Generative AI Engineer is developing a patient-facing healthcare-focused chatbot. If the patient's question is not a medical emergency, the chatbot should solicit more information from the patient to pass to the doctor' s office and suggest a few relevant pre-approved medical articles for reading. If the patient's question is urgent, direct the patient to calling their local emergency services.
Given the following user input:
"I have been experiencing severe headaches and dizziness for the past two days." Which response is most appropriate for the chatbot to generate?

Answer: C

Explanation:
* Problem Context: The task is to design responses for a healthcare-focused chatbot that appropriately addresses the urgency of a patient's symptoms.
* Explanation of Options:
* Option A: Suggesting articles might be suitable for less urgent inquiries but is inappropriate for symptoms that could indicate a serious condition.
* Option B: Given the description of severe symptoms like headaches and dizziness, directing the patient to emergency services is prudent. This aligns with medical guidelines that recommend immediate professional attention for such severe symptoms.
* Option C: Offering well-wishes does not address the potential seriousness of the symptoms and lacks appropriate action.
* Option D: While gathering more information is part of a detailed assessment, the immediate need here suggests a more urgent response.
Given the potential severity of the described symptoms,Option Bis the most appropriate, ensuring the chatbot directs patients to seek urgent care when needed, potentially saving lives.


NEW QUESTION # 82
A Generative Al Engineer is tasked with developing an application that is based on an open source large language model (LLM). They need a foundation LLM with a large context window.
Which model fits this need?

Answer: B

Explanation:
* Problem Context: The engineer needs an open-source LLM with a large context window to develop an application.
* Explanation of Options:
* Option A: DistilBERT: While an efficient and smaller version of BERT, DistilBERT does not provide a particularly large context window.
* Option B: MPT-30B: This model, while large, is not specified as being particularly notable for its context window capabilities.
* Option C: Llama2-70B: Known for its large model size and extensive capabilities, including a large context window. It is also available as an open-source model, making it ideal for applications requiring extensive contextual understanding.
* Option D: DBRX: This is not a recognized standard model in the context of large language models with extensive context windows.
Thus,Option C(Llama2-70B) is the best fit as it meets the criteria of having a large context window and being available for open-source use, suitable for developing robust language understanding applications.


NEW QUESTION # 83
A Generative AI Engineer is building a multi-turn chat app with LangGraph on Databricks. The app must persist chat history-messages, roles, timestamps, and session IDs-for many concurrent users, support SQL queries, and stay governed in Unity Catalog. The engineer also wants ACID guarantees, low-latency reads and writes, and an easy way to sync chat data into Delta tables for analytics and model training.
Which approach fits these requirements?

Answer: B

Explanation:
Lakebase provides a Postgres database suited to transactional application state, including concurrent chat- history reads and writes. A Postgres-backed LangGraph checkpoint or memory component can persist conversation state across requests and application restarts. SQL support and transactional guarantees address the application's operational requirements. Databricks also documents registering Lakebase databases in Unity Catalog and feeding Lakebase changes into Delta tables for downstream analytics. These integrations require appropriate configuration and permissions; they are not automatic simply because a table exists.
MLflow runs are intended for experiment and application tracking rather than serving as a low-latency conversation database. Per-turn Spark writes introduce unnecessary processing overhead, while driver- memory storage with periodic snapshots risks losing recent conversation state. Databricks documentation


NEW QUESTION # 84
A Generative Al Engineer is creating an LLM-based application. The documents for its retriever have been chunked to a maximum of 512 tokens each. The Generative Al Engineer knows that cost and latency are more important than quality for this application. They have several context length levels to choose from.
Which will fulfill their need?

Answer: A

Explanation:
When prioritizing cost and latency over quality in a Large Language Model (LLM)-based application, it is crucial to select a configuration that minimizes both computational resources and latency while still providing reasonable performance. Here's whyDis the best choice:
* Context length: The context length of 512 tokens aligns with the chunk size used for the documents (maximum of 512 tokens per chunk). This is sufficient for capturing the needed information and generating responses without unnecessary overhead.
* Smallest model size: The model with a size of 0.13GB is significantly smaller than the other options.
This small footprint ensures faster inference times and lower memory usage, which directly reduces both latency and cost.
* Embedding dimension: While the embedding dimension of 384 is smaller than the other options, it is still adequate for tasks where cost and speed are more important than precision and depth of understanding.
This setup achieves the desired balance between cost-efficiency and reasonable performance in a latency- sensitive, cost-conscious application.


NEW QUESTION # 85
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