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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Generative AI Engineer Associate |
| Exam Number: | Databricks-Generative-AI-Engineer-Associate |
| Exam Duration: | 90 minutes |
| Exam Format: | Multiple choice, Multiple select |
| Available Languages: | English |
| Related Certifications: | Databricks Certified Data Engineer Associate Databricks Certified Machine Learning Associate |
| Exam Price: | USD 200 |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 45-60 |
| Recommended Training: | Databricks Academy - Generative AI Courses Mosaic AI Documentation |
| Exam Registration: | Databricks Certification Portal |
| Sample Questions: | Databricks Databricks-Generative-AI-Engineer-Associate Sample Questions |
| Exam Way: | Online proctored exam |
| Pre Condition: | No strict prerequisites, but experience with Python, machine learning fundamentals, and Databricks platform is recommended. |
| Official Syllabus URL: | https://www.databricks.com/learn/certification |
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NEW QUESTION # 12
Generative AI Engineer at an electronics company just deployed a RAG application for customers to ask questions about products that the company carries. However, they received feedback that the RAG response often returns information about an irrelevant product.
What can the engineer do to improve the relevance of the RAG's response?
Answer: D
Explanation:
In a Retrieval-Augmented Generation (RAG) system, the key to providing relevant responses lies in the quality of the retrieved context. Here's why option A is the most appropriate solution:
* Context Relevance:The RAG model generates answers based on retrieved documents or context. If the retrieved information is about an irrelevant product, it suggests that the retrieval step is failing to select the right context. The Generative AI Engineer must first assess the quality of what is being retrieved and ensure it is pertinent to the query.
* Vector Search and Embedding Similarity:RAG typically uses vector search for retrieval, where embeddings of the query are matched against embeddings of product descriptions. Assessing the semantic similarity searchprocess ensures that the closest matches are actually relevant to the query.
* Fine-tuning the Retrieval Process:By improving theretrieval quality, such as tuning the embeddings or adjusting the retrieval strategy, the system can return more accurate and relevant product information.
* Why Other Options Are Less Suitable:
* B (Caching FAQs): Caching can speed up responses for frequently asked questions but won't improve the relevance of the retrieved content for less frequent or new queries.
* C (Use a Different LLM): Changing the LLM only affects the generation step, not the retrieval process, which is the core issue here.
* D (Different Semantic Search Algorithm): This could help, but the first step is to evaluate the current retrieval context before replacing the search algorithm.
Therefore, improving and assessing the quality of the retrieved context (option A) is the first step to fixing the issue of irrelevant product information.
NEW QUESTION # 13
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: D
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 # 14
A Generative AI Engineer has a provisioned throughput model serving endpoint as part of a RAG application and would like to monitor the serving endpoint's incoming requests and outgoing responses. The current approach is to include a micro-service in between the endpoint and the user interface to write logs to a remote server.
Which Databricks feature should they use instead which will perform the same task?
Answer: D
Explanation:
Problem Context: The goal is to monitor theserving endpointfor incoming requests and outgoing responses in aprovisioned throughput model serving endpointwithin aRetrieval-Augmented Generation (RAG) application. The current approach involves using a microservice to log requests and responses to a remote server, but the Generative AI Engineer is looking for a more streamlined solution within Databricks.
Explanation of Options:
* Option A: Vector Search: This feature is used to perform similarity searches within vector databases.
It doesn't provide functionality for logging or monitoring requests and responses in a serving endpoint, so it's not applicable here.
* Option B: Lakeview: Lakeview is not a feature relevant to monitoring or logging request-response cycles for serving endpoints. It might be more related to viewing data in Databricks Lakehouse but doesn't fulfill the specific monitoring requirement.
* Option C: DBSQL: Databricks SQL (DBSQL) is used for running SQL queries on data stored in Databricks, primarily for analytics purposes. It doesn't provide the direct functionality needed to monitor requests and responses in real-time for an inference endpoint.
* Option D: Inference Tables: This is the correct answer.Inference Tablesin Databricks are designed to store the results and metadata of inference runs. This allows the system to logincoming requests and outgoing responsesdirectly within Databricks, making it an ideal choice for monitoring the behavior of a provisioned serving endpoint. Inference Tables can be queried and analyzed, enabling easier monitoring and debugging compared to a custom microservice.
Thus,Inference Tablesare the optimal feature for monitoring request and response logs within the Databricks infrastructure for a model serving endpoint.
NEW QUESTION # 15
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: D
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 # 16
Which TWO chain components are required for building a basic LLM-enabled chat application that includes conversational capabilities, knowledge retrieval, and contextual memory?
Answer: D,F
Explanation:
Building a basic LLM-enabled chat application with conversational capabilities, knowledge retrieval, and contextual memory requires specific components that work together to process queries, maintain context, and retrieve relevant information. Databricks' Generative AI Engineer documentation outlines key components for such systems, particularly in the context of frameworks like LangChain or Databricks' MosaicML integrations. Let's evaluate the required components:
* Understanding the Requirements:
* Conversational capabilities: The app must generate natural, coherent responses.
* Knowledge retrieval: It must access external or domain-specific knowledge.
* Contextual memory: It must remember prior interactions in the conversation.
* Databricks Reference:"A typical LLM chat application includes a memory component to track conversation history and a retrieval mechanism to incorporate external knowledge"("Databricks Generative AI Cookbook," 2023).
* Evaluating the Options:
* A. (Q): This appears incomplete or unclear (possibly a typo). Without further context, it's not a valid component.
* B. Vector Stores: These store embeddings of documents or knowledge bases, enabling semantic search and retrieval of relevant information for the LLM. This is critical for knowledge retrieval in a chat application.
* Databricks Reference:"Vector stores, such as those integrated with Databricks' Lakehouse, enable efficient retrieval of contextual data for LLMs"("Building LLM Applications with Databricks").
* C. Conversation Buffer Memory: This component stores the conversation history, allowing the LLM to maintain context across multiple turns. It's essential for contextual memory.
* Databricks Reference:"Conversation Buffer Memory tracks prior user inputs and LLM outputs, ensuring context-aware responses"("Generative AI Engineer Guide").
* D. External tools: These (e.g., APIs or calculators) enhance functionality but aren't required for a basicchat app with the specified capabilities.
* E. Chat loaders: These might refer to data loaders for chat logs, but they're not a core chain component for conversational functionality or memory.
* F. React Components: These relate to front-end UI development, not the LLM chain's backend functionality.
* Selecting the Two Required Components:
* Forknowledge retrieval, Vector Stores (B) are necessary to fetch relevant external data, a cornerstone of Databricks' RAG-based chat systems.
* Forcontextual memory, Conversation Buffer Memory (C) is required to maintain conversation history, ensuring coherent and context-aware responses.
* While an LLM itself is implied as the core generator, the question asks for chain components beyond the model, making B and C the minimal yet sufficient pair for a basic application.
Conclusion: The two required chain components areB. Vector StoresandC. Conversation Buffer Memory, as they directly address knowledge retrieval and contextual memory, respectively, aligning with Databricks' documented best practices for LLM-enabled chat applications.
NEW QUESTION # 17
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