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Databricks Certified Generative AI Engineer Associate Exam Questions
- 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: 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 3: 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 4: 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 5: Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal/licensing requirements in this topic.
- Topic 6: 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.
Free Databricks Databricks Certified Generative AI Engineer Associate Exam Actual Questions
Note: Premium Questions for Databricks Certified Generative AI Engineer Associate were last updated On May. 11, 2026 (see below)
A Generative AI Engineer is designing an LLM-powered live sports commentary platform. The platform provides real-time updates and LLM-generated analyses for any users who would like to have live summaries, rather than reading a series of potentially outdated news articles.
Which tool below will give the platform access to real-time data for generating game analyses based on the latest game scores?
Problem Context: The engineer is developing an LLM-powered live sports commentary platform that needs to provide real-time updates and analyses based on the latest game scores. The critical requirement here is the capability to access and integrate real-time data efficiently with the platform for immediate analysis and reporting.
Explanation of Options:
Option A: DatabricksIQ: While DatabricksIQ offers integration and data processing capabilities, it is more aligned with data analytics rather than real-time feature serving, which is crucial for immediate updates necessary in a live sports commentary context.
Option B: Foundation Model APIs: These APIs facilitate interactions with pre-trained models and could be part of the solution, but on their own, they do not provide mechanisms to access real-time game scores.
Option C: Feature Serving: This is the correct answer as feature serving specifically refers to the real-time provision of data (features) to models for prediction. This would be essential for an LLM that generates analyses based on live game data, ensuring that the commentary is current and based on the latest events in the sport.
Option D: AutoML: This tool automates the process of applying machine learning models to real-world problems, but it does not directly provide real-time data access, which is a critical requirement for the platform.
Thus, Option C (Feature Serving) is the most suitable tool for the platform as it directly supports the real-time data needs of an LLM-powered sports commentary system, ensuring that the analyses and updates are based on the latest available information.
A Generative AI Engineer is developing an agent system using a popular agent-authoring library. The agent comprises multiple parallel and sequential chains. The engineer encounters challenges as the agent fails at one of the steps, making it difficult to debug the root cause. They need to find an appropriate approach to research this issue and discover the cause of failure. Which approach do they choose?
For complex agentic systems (like those built with LangGraph or Autogen), standard logging is often insufficient because the 'state' of the agent changes dynamically. MLflow Tracing is the designated Generative AI engineering standard for debugging these systems. Tracing provides a visual, hierarchical timeline of every call made during an agent's execution---including internal LLM reasoning, tool calls, and data transformations. When a step fails, the trace allows the engineer to click into that specific node to see the exact input sent to the LLM and the raw output received. This is much faster and more comprehensive than manually deconstructing the agent (D) or adding manual logs (C). While mlflow.evaluate (B) is useful for measuring performance across a whole dataset, it is not a tool for real-time debugging of a single execution failure.
All of the following are Python APIs used to query Databricks foundation models. When running in an interactive notebook, which of the following libraries does not automatically use the current session credentials?
When working within a Databricks notebook, several high-level SDKs are 'Databricks-aware.' The MLflow Deployments SDK (C) and the Databricks Python SDK (D) are designed to automatically look for the DATABRICKS_HOST and DATABRICKS_TOKEN environment variables provided by the notebook context. The OpenAI client (A), when configured for Databricks via Mosaic AI Gateway, also typically handles authentication via workspace integration in recent versions. However, the REST API via the requests library (B) is a generic Python HTTP client. It has no intrinsic knowledge of the Databricks environment. To use it, an engineer must manually extract the token (e.g., via dbutils.notebook.entry_point...) and explicitly pass it in the Authorization: Bearer <token> header of the request. Without this manual step, the requests library will fail with a 401 Unauthorized error.
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?
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 why D is 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.
A Generative Al Engineer is developing a RAG application and would like to experiment with different embedding models to improve the application performance.
Which strategy for picking an embedding model should they choose?
The task involves improving a Retrieval-Augmented Generation (RAG) application's performance by experimenting with embedding models. The choice of embedding model impacts retrieval accuracy, which is critical for RAG systems. Let's evaluate the options based on Databricks Generative AI Engineer best practices.
Option A: Pick an embedding model trained on related domain knowledge
Embedding models trained on domain-specific data (e.g., industry-specific corpora) produce vectors that better capture the semantics of the application's context, improving retrieval relevance. For RAG, this is a key strategy to enhance performance.
Databricks Reference: 'For optimal retrieval in RAG systems, select embedding models aligned with the domain of your data' ('Building LLM Applications with Databricks,' 2023).
Option B: Pick the most recent and most performant open LLM released at the time
LLMs are not embedding models; they generate text, not embeddings for retrieval. While recent LLMs may be performant for generation, this doesn't address the embedding step in RAG. This option misunderstands the component being selected.
Databricks Reference: Embedding models and LLMs are distinct in RAG workflows: 'Embedding models convert text to vectors, while LLMs generate responses' ('Generative AI Cookbook').
Option C: Pick the embedding model ranked highest on the Massive Text Embedding Benchmark (MTEB) leaderboard hosted by HuggingFace
The MTEB leaderboard ranks models across general tasks, but high overall performance doesn't guarantee suitability for a specific domain. A top-ranked model might excel in generic contexts but underperform on the engineer's unique data.
Databricks Reference: General performance is less critical than domain fit: 'Benchmark rankings provide a starting point, but domain-specific evaluation is recommended' ('Databricks Generative AI Engineer Guide').
Option D: Pick an embedding model with multilingual support to support potential multilingual user questions
Multilingual support is useful only if the application explicitly requires it. Without evidence of multilingual needs, this adds complexity without guaranteed performance gains for the current use case.
Databricks Reference: 'Choose features like multilingual support based on application requirements' ('Building LLM-Powered Applications').
Conclusion: Option A is the best strategy because it prioritizes domain relevance, directly improving retrieval accuracy in a RAG system---aligning with Databricks' emphasis on tailoring models to specific use cases.
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