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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Generative AI Engineer Associate Exam |
| Exam Number: | Databricks-Generative-AI-Engineer-Associate |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 90 minutes |
| Passing Score: | 700/1000 or 70% |
| Exam Format: | Multiple choice |
| Exam Price: | USD 200 |
| Real Exam Qty: | 45 |
| Available Languages: | Korean, Japanese, English, Brazilian Portuguese |
| Recommended Training: | Generative AI Engineering with Databricks |
| Exam Registration: | Databricks Certification Registration |
| Sample Questions: | Databricks Databricks-Generative-AI-Engineer-Associate Sample Questions |
| Exam Way: | Online proctored or in-person test center |
| Pre Condition: | No formal prerequisites; recommended 6+ months hands-on experience building generative AI solutions |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/genai-engineer-associate |
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NEW QUESTION # 68
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?
Answer: D
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 # 69
A team wants to serve a code generation model as an assistant for their software developers. It should support multiple programming languages. Quality is the primary objective.
Which of the Databricks Foundation Model APIs, or models available in the Marketplace, would be the best fit?
Answer: D
Explanation:
For a code generation model that supports multiple programming languages and where quality is the primary objective,CodeLlama-34Bis the most suitable choice. Here's the reasoning:
* Specialization in Code Generation:CodeLlama-34B is specifically designed for code generation tasks.
This model has been trained with a focus on understanding and generating code, which makes it particularly adept at handling various programming languages and coding contexts.
* Capacity and Performance:The "34B" indicates a model size of 34 billion parameters, suggesting a high capacity for handling complex tasks and generating high-quality outputs. The large model size typically correlates with better understanding and generation capabilities in diverse scenarios.
* Suitability for Development Teams:Given that the model is optimized for code, it will be able to assist software developers more effectively than general-purpose models. It understands coding syntax, semantics, and the nuances of different programming languages.
* Why Other Options Are Less Suitable:
* A (Llama2-70b): While also a large model, it's more general-purpose and may not be as fine- tuned for code generation as CodeLlama.
* B (BGE-large): This model may not specifically focus on code generation.
* C (MPT-7b): Smaller than CodeLlama-34B and likely less capable in handling complex code generation tasks at high quality.
Therefore, for a high-quality, multi-language code generation application,CodeLlama-34B(option D) is the best fit.
NEW QUESTION # 70
An AI developer team wants to fine-tune an open-weight model to have exceptional performance on a code generation use case. They are trying to choose the best model to start with. They want to minimize model hosting costs and are using Hugging Face model cards and spaces to explore models. Which TWO model attributes and metrics should the team focus on to make their selection?
Answer: C,D
Explanation:
To optimize for code generation performance and hosting costs , a Generative AI engineer must look at specific metrics.
* Big Code Models Leaderboard (A): This is the industry-standard benchmark for code-specific LLMs (like StarCoder or CodeLlama). It measures performance on tasks like HumanEval and MBPP, providing a direct indicator of how well the model handles programming logic.
* Number of model parameters (B): This is the primary driver of hosting costs. Larger models (e.g.,
70B) require more GPU memory (VRAM) and more expensive compute instances (like A100s/H100s) than smaller models (e.g., 7B or 13B). To minimize costs, the team should look for the smallest model that achieves a high score on the Big Code Leaderboard.
Note: MTEB (C) is for embeddings, and Chatbot Arena (D) is for general-purpose chat, neither of which is the primary metric for specialized code generation fine-tuning.
NEW QUESTION # 71
A Generative AI Engineer I using the code below to test setting up a vector store:
Assuming they intend to use Databricks managed embeddings with the default embedding model, what should be the next logical function call?
Answer: C
Explanation:
Context : The Generative AI Engineer is setting up a vector store using Databricks ' VectorSearchClient.
This is typically done to enable fast and efficient retrieval of vectorized data for tasks like similarity searches.
Explanation of Options :
* Option A: vsc.get_index() : This function would be used to retrieve an existing index, not create one, so it would not be the logical next step immediately after creating an endpoint.
* Option B: vsc.create_delta_sync_index() : After setting up a vector store endpoint, creating an index is necessary to start populating and organizing the data. The create_delta_sync_index() function specifically creates an index that synchronizes with a Delta table, allowing automatic updates as the data changes. This is likely the most appropriate choice if the engineer plans to use dynamic data that is updated over time.
* Option C: vsc.create_direct_access_index() : This function would create an index that directly accesses the data without synchronization. While also a valid approach, it ' s less likely to be the next logical step if the default setup (typically accommodating changes) is intended.
* Option D: vsc.similarity_search() : This function would be used to perform searches on an existing index; however, an index needs to be created and populated with data before any search can be conducted.
Given the typical workflow in setting up a vector store, the next step after creating an endpoint is to establish an index, particularly one that synchronizes with ongoing data updates, hence Option B .
NEW QUESTION # 72
A Generative AI Engineer is building a Databricks-hosted assistant that must (1) query Unity Catalog tables with row and column permissions enforced, and (2) avoid managing any external infrastructure. The team wants the LLM to use governed data access through tools exposed via MCP.
Which MCP server choice meets these constraints?
Answer: D
Explanation:
A managed Databricks MCP server provides tools for accessing Databricks services without requiring the team to host an external server. For structured table queries, a suitable managed SQL or Genie tool uses Databricks-governed data access. Unity Catalog remains responsible for authorization instead of delegating security decisions to the language model. When permissions must reflect the individual requester, the application must use the appropriate user authorization configuration so row filters and column masks are evaluated for that identity. Passing credentials in prompts exposes secrets unnecessarily. A self-managed virtual machine violates the infrastructure requirement. Direct JDBC access also fails the requested MCP architecture and introduces custom integration work. Managed hosting simplifies deployment while preserving the platform's access-control model. Databricks tools documentation , authentication documentation
NEW QUESTION # 73
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