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

Section	Objectives
Topic 1: Responsible AI and Governance	- AI safety and ethics 1. Content safety filtering 2. Bias and fairness considerations - Model governance 1. Auditability and compliance 2. Access control and monitoring
Topic 2: Foundations of Generative AI	- Large Language Models (LLMs) 1. Transformer architecture basics 2. Tokenization and embeddings 3. Pretraining vs fine-tuning concepts - Generative AI Concepts 1. Text generation principles 2. Hallucinations and mitigation strategies
Topic 3: Prompt Engineering	- Prompt design techniques 1. Role prompting and instruction tuning patterns 2. Zero-shot and few-shot prompting - Prompt evaluation 1. Iterative prompt refinement 2. Output quality assessment
Topic 4: Databricks Mosaic AI Platform	- MLflow for GenAI 1. Model registry usage 2. Experiment tracking - Model development and serving 1. Endpoint deployment concepts 2. Using Databricks Model Serving
Topic 5: Retrieval-Augmented Generation (RAG)	- Vector databases and embeddings 1. Vector index construction 2. Embedding generation and similarity search - RAG architecture 1. Context retrieval and grounding 2. Document ingestion pipelines

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Databricks Certified Generative AI Engineer Associate Sample Questions (Q26-Q31):

NEW QUESTION # 26
A Generative Al Engineer is tasked with developing a RAG application that will help a small internal group of experts at their company answer specific questions, augmented by an internal knowledge base. They want the best possible quality in the answers, and neither latency nor throughput is a huge concern given that the user group is small and they're willing to wait for the best answer. The topics are sensitive in nature and the data is highly confidential and so, due to regulatory requirements, none of the information is allowed to be transmitted to third parties.
Which model meets all the Generative Al Engineer's needs in this situation?

A. Dolly 1.5B
B. OpenAI GPT-4
C. BGE-large
D. Llama2-70B

Answer: C

Explanation:
* Problem Context: The Generative AI Engineer needs a model for a Retrieval-Augmented Generation (RAG) application that provides high-quality answers, where latency and throughput are not major concerns. The key factors are confidentiality and sensitivity of the data, as well as the requirement for all processing to be confined to internal resources without external data transmission.
* Explanation of Options:
Option A: Dolly 1.5B: This model does not typically support RAG applications as it's more focused on image generation tasks.
Option B: OpenAI GPT-4: While GPT-4 is powerful for generating responses, its standard deployment involves cloud-based processing, which could violate the confidentiality requirements due to external data transmission.
Option C: BGE-large: The BGE (Big Green Engine) large model is a suitable choice if it is configured to operate on-premises or within a secure internal environment that meets regulatory requirements. Assuming this setup, BGE-large can provide high-quality answers while ensuring that data is not transmitted to third parties, thus aligning with the project's sensitivity and confidentiality needs.
Option D: Llama2-70B: Similar to GPT-4, unless specifically set up for on-premises use, it generally relies on cloud-based services, which might risk confidential data exposure.
Given the sensitivity and confidentiality concerns, BGE-large is assumed to be configurable for secure internal use, making it the optimal choice for this scenario.

NEW QUESTION # 27
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. Return the extracted information in JSON format.
C. 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.
D. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in a human-readable format.

Answer: A

Explanation:
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 Bis 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.

NEW QUESTION # 28
A Generative Al Engineer needs to design an LLM pipeline to conduct multi-stage reasoning that leverages external tools. To be effective at this, the LLM will need to plan and adapt actions while performing complex reasoning tasks.
Which approach will do this?

A. Implement a framework like ReAct which allows the LLM to generate reasoning traces and perform task-specific actions that leverage external tools if necessary.
B. Use a Chain-of-Thought (CoT) prompting technique to guide the LLM through a series of reasoning steps, then manually input the results from external tools for the final answer.
C. Encourage the LLM to make multiple API calls in sequence without planning or structuring the calls, allowing the LLM to decide when and how to use external tools spontaneously.
D. Tram the LLM to generate a single, comprehensive response without interacting with any external tools, relying solely on its pre-trained knowledge.

Answer: A

NEW QUESTION # 29
After changing the response generating LLM in a RAG pipeline from GPT-4 to a model with a shorter context length that the company self-hosts, the Generative AI Engineer is getting the following error:

What TWO solutions should the Generative AI Engineer implement without changing the response generating model? (Choose two.)

A. Decrease the chunk size of embedded documents
B. Reduce the maximum output tokens of the new model
C. Use a smaller embedding model to generate
D. Reduce the number of records retrieved from the vector database
E. Retrain the response generating model using ALiBi

Answer: A,D

Explanation:
* Problem Context : After switching to a model with a shorter context length, the error message indicating that the prompt token count has exceeded the limit suggests that the input to the model is too large.
* Explanation of Options :
* Option A: Use a smaller embedding model to generate - This wouldn ' t necessarily address the issue of prompt size exceeding the model's token limit.
* Option B: Reduce the maximum output tokens of the new model - This option affects the output length, not the size of the input being too large.
* Option C: Decrease the chunk size of embedded documents - This would help reduce the size of each document chunk fed into the model, ensuring that the input remains within the model ' s context length limitations.
* Option D: Reduce the number of records retrieved from the vector database - By retrieving fewer records, the total input size to the model can be managed more effectively, keeping it within the allowable token limits.
* Option E: Retrain the response generating model using ALiBi - Retraining the model is contrary to the stipulation not to change the response generating model.
Options C and D are the most effective solutions to manage the model's shorter context length without changing the model itself, by adjusting the input size both in terms of individual document size and total documents retrieved.

NEW QUESTION # 30
A company has a typical RAG-enabled, customer-facing chatbot on its website.

Select the correct sequence of components a user ' s questions will go through before the final output is returned. Use the diagram above for reference.

A. 1.embedding model, 2.vector search, 3.context-augmented prompt, 4.response-generating LLM
B. 1.response-generating LLM, 2.context-augmented prompt, 3.vector search, 4.embedding model
C. 1.response-generating LLM, 2.vector search, 3.context-augmented prompt, 4.embedding model
D. 1.context-augmented prompt, 2.vector search, 3.embedding model, 4.response-generating LLM

Answer: A

Explanation:
To understand how a typical RAG-enabled customer-facing chatbot processes a user ' s question, let's go through the correct sequence as depicted in the diagram and explained in option A:
* Embedding Model (1) : The first step involves the user ' s question being processed through an embedding model. This model converts the text into a vector format that numerically represents the text. This step is essential for allowing the subsequent vector search to operate effectively.
* Vector Search (2) : The vectors generated by the embedding model are then used in a vector search mechanism. This search identifies the most relevant documents or previously answered questions that are stored in a vector format in a database.
* Context-Augmented Prompt (3) : The information retrieved from the vector search is used to create a context-augmented prompt. This step involves enhancing the basic user query with additional relevant information gathered to ensure the generated response is as accurate and informative as possible.
* Response-Generating LLM (4) : Finally, the context-augmented prompt is fed into a response- generating large language model (LLM). This LLM uses the prompt to generate a coherent and contextually appropriate answer, which is then delivered as the final output to the user.
Why Other Options Are Less Suitable :
* B, C, D : These options suggest incorrect sequences that do not align with how a RAG system typically processes queries. They misplace the role of embedding models, vector search, and response generation in an order that would not facilitate effective information retrieval and response generation.
Thus, the correct sequence is embedding model, vector search, context-augmented prompt, response- generating LLM , which is option A.

NEW QUESTION # 31
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