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- Databricks Databricks-Generative-AI-Engineer-Associate Exam Syllabus Topics:
- Topic Details
- Topic 1
- 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 2
- 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.
- Topic 3
- 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 4
- 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 5
- 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.
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- Databricks Certified Generative AI Engineer Associate Sample Questions (Q11-Q16):
- NEW QUESTION # 11
- A Generative AI Engineer is deploying an agent using Mosaic AI Model Serving. The agent needs to access various Databricks resources, including Vector Search, Databricks SQL, and Functions. They need to find the easiest and best-practice way to authenticate the deployed agent to access these resources.
- What approach should they choose?
- A. Define resource dependencies while logging the agent and deploy it with the Agent Framework.
- B. Embed authentication credentials within the agent's code to access the required resources.
- C. Log the authentication token while logging the agent; this token will be automatically used for authentication.
- D. Set appropriate permissions on the Model Serving endpoint for the agent, as these permissions will be used when connecting to other resources.
- Answer: A
- Explanation:
- For agents deployed through Model Serving, Databricks supports automatic authentication passthrough for declared resources. The engineer records the agent's dependencies through the resources parameter when logging the model. During deployment, Databricks checks the deployer's access, creates a service principal for the agent model version, grants the required resource access, and supplies short-lived credentials.
- Credential rotation is handled automatically. Supported dependencies include Vector Search indexes, SQL warehouses, and Unity Catalog functions. This approach avoids embedding secrets in source code or recording authentication tokens as model artifacts. Permissions on the serving endpoint govern access to that endpoint; they do not independently establish all downstream resource permissions. All required dependencies, including relevant underlying resources, must be declared correctly. Databricks documentation
- NEW QUESTION # 12
- 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.context-augmented prompt, 2.vector search, 3.embedding model, 4.response-generating LLM
- C. 1.response-generating LLM, 2.vector search, 3.context-augmented prompt, 4.embedding model
- D. 1.response-generating LLM, 2.context-augmented prompt, 3.vector search, 4.embedding model
- 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 # 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?
- A. Please call your local emergency services.
- B. Here are a few relevant articles for your browsing. Let me know if you have questions after reading them.
- C. Please provide your age, recent activities, and any other symptoms you have noticed along with your headaches and dizziness.
- D. Headaches can be tough. Hope you feel better soon!
- Answer: A
- 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 Al Engineer is creating an LLM system that will retrieve news articles from the year 1918 and related to a user's query and summarize them. The engineer has noticed that the summaries are generated well but often also include an explanation of how the summary was generated, which is undesirable.
- Which change could the Generative Al Engineer perform to mitigate this issue?
- A. Provide few shot examples of desired output format to the system and/or user prompt.
- B. Tune the chunk size of news articles or experiment with different embedding models.
- C. Split the LLM output by newline characters to truncate away the summarization explanation.
- D. Revisit their document ingestion logic, ensuring that the news articles are being ingested properly.
- Answer: A
- Explanation:
- To mitigate the issue of the LLM including explanations of how summaries are generated in its output, the best approach is to adjust the training or prompt structure. Here's why Option D is effective:
- Few-shot Learning: By providing specific examples of how the desired output should look (i.e., just the summary without explanation), the model learns the preferred format. This few-shot learning approach helps the model understand not only what content to generate but also how to format its responses.
- Prompt Engineering: Adjusting the user prompt to specify the desired output format clearly can guide the LLM to produce summaries without additional explanatory text. Effective prompt design is crucial in controlling the behavior of generative models.
- Why Other Options Are Less Suitable:
- A: While technically feasible, splitting the output by newline and truncating could lead to loss of important content or create awkward breaks in the summary.
- B: Tuning chunk sizes or changing embedding models does not directly address the issue of the model's tendency to generate explanations along with summaries.
- C: Revisiting document ingestion logic ensures accurate source data but does not influence how the model formats its output.
- By using few-shot examples and refining the prompt, the engineer directly influences the output format, making this approach the most targeted and effective solution.
- NEW QUESTION # 15
- A Generative AI Engineer developed an LLM application using the provisioned throughput Foundation Model API. Now that the application is ready to be deployed, they realize their volume of requests are not sufficiently high enough to create their own provisioned throughput endpoint. They want to choose a strategy that ensures the best cost-effectiveness for their application.
- What strategy should the Generative AI Engineer use?
- A. Deploy the model using pay-per-token throughput as it comes with cost guarantees
- B. Change to a model with a fewer number of parameters in order to reduce hardware constraint issues
- C. Throttle the incoming batch of requests manually to avoid rate limiting issues
- D. Switch to using External Models instead
- Answer: A
- Explanation:
- Problem Context: The engineer needs a cost-effective deployment strategy for an LLM application with relatively low request volume.
- Explanation of Options:
- Option A: Switching to external models may not provide the required control or integration necessary for specific application needs.
- Option B: Using a pay-per-token model is cost-effective, especially for applications with variable or low request volumes, as it aligns costs directly with usage.
- Option C: Changing to a model with fewer parameters could reduce costs, but might also impact the performance and capabilities of the application.
- Option D: Manually throttling requests is a less efficient and potentially error-prone strategy for managing costs.
- OptionBis ideal, offering flexibility and cost control, aligning expenses directly with the application's usage patterns.
- NEW QUESTION # 16
- .....
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