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

Topic	Details
Topic 1	Design Applications: The topic focuses on designing a prompt that elicits a specifically formatted response. It also focuses on selecting model tasks to accomplish a given business requirement. Lastly, the topic covers chain components for a desired model input and output.
Topic 2	Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal licensing requirements in this topic.
Topic 3	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.

Databricks Certified Generative AI Engineer Associate Sample Questions (Q25-Q30):

NEW QUESTION # 25
A Generative Al Engineer is building a RAG application that answers questions about internal documents for the company SnoPen AI.
The source documents may contain a significant amount of irrelevant content, such as advertisements, sports news, or entertainment news, or content about other companies.
Which approach is advisable when building a RAG application to achieve this goal of filtering irrelevant information?

A. Keep all articles because the RAG application needs to understand non-company content to avoid answering questions about them.
B. Include in the system prompt that any information it sees will be about SnoPenAI, even if no data filtering is performed.
C. Consolidate all SnoPen AI related documents into a single chunk in the vector database.
D. Include in the system prompt that the application is not supposed to answer any questions unrelated to SnoPen Al.

Answer: D

Explanation:
In a Retrieval-Augmented Generation (RAG) application built to answer questions about internal documents, especially when the dataset contains irrelevant content, it's crucial to guide the system to focus on the right information. The best way to achieve this is byincluding a clear instruction in the system prompt(option C).
* System Prompt as Guidance:The system prompt is an effective way to instruct the LLM to limit its focus to SnoPen AI-related content. By clearly specifying that the model should avoid answering questions unrelated to SnoPen AI, you add an additional layer of control that helps the model stay on- topic, even if irrelevant content is present in the dataset.
* Why This Approach Works:The prompt acts as a guiding principle for the model, narrowing its focus to specific domains. This prevents the model from generating answers based on irrelevant content, such as advertisements or news unrelated to SnoPen AI.
* Why Other Options Are Less Suitable:
* A (Keep All Articles): Retaining all content, including irrelevant materials, without any filtering makes the system prone to generating answers based on unwanted data.
* B (Include in the System Prompt about SnoPen AI): This option doesn't address irrelevant content directly, and without filtering, the model might still retrieve and use irrelevant data.
* D (Consolidating Documents into a Single Chunk): Grouping documents into a single chunk makes the retrieval process less efficient and won't help filter out irrelevant content effectively.
Therefore, instructing the system in the prompt not to answer questions unrelated to SnoPen AI (option C) is the best approach to ensure the system filters out irrelevant information.

NEW QUESTION # 26
A Generative Al Engineer is developing a RAG system for their company to perform internal document Q&A for structured HR policies, but the answers returned are frequently incomplete and unstructured It seems that the retriever is not returning all relevant context The Generative Al Engineer has experimented with different embedding and response generating LLMs but that did not improve results.
Which TWO options could be used to improve the response quality?
Choose 2 answers

A. Use a larger embedding model
B. Increase the document chunk size
C. Split the document by sentence
D. Add the section header as a prefix to chunks
E. Fine tune the response generation model

Answer: B,D

Explanation:
The problem describes a Retrieval-Augmented Generation (RAG) system for HR policy Q&A where responses are incomplete and unstructured due to the retriever failing to return sufficient context. The engineer has already tried different embedding and response-generating LLMs without success, suggesting the issue lies in the retrieval process-specifically, how documents are chunked and indexed. Let's evaluate the options.
* Option A: Add the section header as a prefix to chunks
* Adding section headers provides additional context to each chunk, helping the retriever understand the chunk's relevance within the document structure (e.g., "Leave Policy: Annual Leave" vs. just "Annual Leave"). This can improve retrieval precision for structured HR policies.
* Databricks Reference:"Metadata, such as section headers, can be appended to chunks to enhance retrieval accuracy in RAG systems"("Databricks Generative AI Cookbook," 2023).
* Option B: Increase the document chunk size
* Larger chunks include more context per retrieval, reducing the chance of missing relevant information split across smaller chunks. For structured HR policies, this can ensure entire sections or rules are retrieved together.
* Databricks Reference:"Increasing chunk size can improve context completeness, though it may trade off with retrieval specificity"("Building LLM Applications with Databricks").
* Option C: Split the document by sentence
* Splitting by sentence creates very small chunks, which could exacerbate the problem by fragmenting context further. This is likely why the current system fails-it retrieves incomplete snippets rather than cohesive policy sections.
* Databricks Reference: No specific extract opposes this, but the emphasis on context completeness in RAG suggests smaller chunks worsen incomplete responses.
* Option D: Use a larger embedding model
* A larger embedding model might improve vector quality, but the question states that experimenting with different embedding models didn't help. This suggests the issue isn't embedding quality but rather chunking/retrieval strategy.
* Databricks Reference: Embedding models are critical, but not the focus when retrieval context is the bottleneck.
* Option E: Fine tune the response generation model
* Fine-tuning the LLM could improve response coherence, but if the retriever doesn't provide complete context, the LLM can't generate full answers. The root issue is retrieval, not generation.
* Databricks Reference: Fine-tuning is recommended for domain-specific generation, not retrieval fixes ("Generative AI Engineer Guide").
Conclusion: Options A and B address the retrieval issue directly by enhancing chunk context-either through metadata (A) or size (B)-aligning with Databricks' RAG optimization strategies. C would worsen the problem, while D and E don't target the root cause given prior experimentation.

NEW QUESTION # 27
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. Use a smaller embedding model to generate
B. Retrain the response generating model using ALiBi
C. Reduce the maximum output tokens of the new model
D. Reduce the number of records retrieved from the vector database
E. Decrease the chunk size of embedded documents

Answer: D,E

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.
OptionsCandDare 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 # 28
A Generative AI Engineer is testing a simple prompt template in LangChain using the code below, but is getting an error.

Assuming the API key was properly defined, what change does the Generative AI Engineer need to make to fix their chain?

Answer: D

Explanation:
To fix the error in the LangChain code provided for using a simple prompt template, the correct approach is Option C. Here's a detailed breakdown of why Option C is the right choice and how it addresses the issue:
* Proper Initialization: In Option C, the LLMChain is correctly initialized with the LLM instance specified as OpenAI(), which likely represents a language model (like GPT) from OpenAI. This is crucial as it specifies which model to use for generating responses.
* Correct Use of Classes and Methods:
* The PromptTemplate is defined with the correct format, specifying that adjective is a variable within the template. This allows dynamic insertion of values into the template when generating text.
* The prompt variable is properly linked with the PromptTemplate, and the final template string is passed correctly.
* The LLMChain correctly references the prompt and the initialized OpenAI() instance, ensuring that the template and the model are properly linked for generating output.
Why Other Options Are Incorrect:
* Option A: Misuses the parameter passing in generate method by incorrectly structuring the dictionary.
* Option B: Incorrectly uses prompt.format method which does not exist in the context of LLMChain and PromptTemplate configuration, resulting in potential errors.
* Option D: Incorrect order and setup in the initialization parameters for LLMChain, which would likely lead to a failure in recognizing the correct configuration for prompt and LLM usage.
Thus, Option C is correct because it ensures that the LangChain components are correctly set up and integrated, adhering to proper syntax and logical flow required by LangChain's architecture. This setup avoids common pitfalls such as type errors or method misuses, which are evident in other options.

NEW QUESTION # 29
A Generative AI Engineer is tasked with deploying an application that takes advantage of a custom MLflow Pyfunc model to return some interim results.
How should they configure the endpoint to pass the secrets and credentials?

A. Use spark.conf.set ()
B. Pass variables using the Databricks Feature Store API
C. Add credentials using environment variables
D. Pass the secrets in plain text

Answer: C

Explanation:
Context: Deploying an application that uses an MLflow Pyfunc model involves managing sensitive information such as secrets and credentials securely.
Explanation of Options:
* Option A: Use spark.conf.set(): While this method can pass configurations within Spark jobs, using it for secrets is not recommended because it may expose them in logs or Spark UI.
* Option B: Pass variables using the Databricks Feature Store API: The Feature Store API is designed for managing features for machine learning, not for handling secrets or credentials.
* Option C: Add credentials using environment variables: This is a common practice for managing credentials in a secure manner, as environment variables can be accessed securely by applications without exposing them in the codebase.
* Option D: Pass the secrets in plain text: This is highly insecure and not recommended, as it exposes sensitive information directly in the code.
Therefore,Option Cis the best method for securely passing secrets and credentials to an application, protecting them from exposure.

NEW QUESTION # 30
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Free PDF Quiz Newest Databricks - Databricks-Generative-AI-Engineer-Associate - Test Databricks Certified Generative AI Engineer Associate Pass4sure

Use Desktop Databricks Databricks-Generative-AI-Engineer-Associate Practice Test Software To Identify Gaps In Knowledge

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

Databricks Certified Generative AI Engineer Associate Sample Questions (Q25-Q30):