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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 |
| Real Exam Qty: | 45 |
| Exam Price: | USD 200 |
| Passing Score: | 700/1000 or 70% |
| Exam Duration: | 90 minutes |
| Available Languages: | Brazilian Portuguese, Japanese, Korean, English |
| Exam Format: | Multiple choice |
| 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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質問 # 81
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?
正解：D
解説：
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.
質問 # 82
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?
正解：A
解説：
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
質問 # 83
A Generative AI Engineer is building an LLM to generate article summaries in the form of a type of poem, such as a haiku, given the article content. However, the initial output from the LLM does not match the desired tone or style.
Which approach will NOT improve the LLM's response to achieve the desired response?
正解：C
解説：
The task at hand is to improve the LLM's ability to generate poem-like article summaries with the desired tone and style. Using a neutralizer to normalize the tone and style of the underlying documents (option B) will not help improve the LLM's ability to generate the desired poetic style. Here's why:
Neutralizing Underlying Documents:
A neutralizer aims to reduce or standardize the tone of input data. However, this contradicts the goal, which is to generate text with a specific tone and style (like haikus). Neutralizing the source documents will strip away the richness of the content, making it harder for the LLM to generate creative, stylistic outputs like poems.
Why Other Options Improve Results:
A (Explicit Instructions in the Prompt): Directly instructing the LLM to generate text in a specific tone and style helps align the output with the desired format (e.g., haikus). This is a common and effective technique in prompt engineering.
C (Few-shot Examples): Providing examples of the desired output format helps the LLM understand the expected tone and structure, making it easier to generate similar outputs.
D (Fine-tuning the LLM): Fine-tuning the model on a dataset that contains examples of the desired tone and style is a powerful way to improve the model's ability to generate outputs that match the target format.
Therefore, using a neutralizer (option B) is not an effective method for achieving the goal of generating stylized poetic summaries.
質問 # 84
A Generative Al Engineer is helping a cinema extend its website's chat bot to be able to respond to questions about specific showtimes for movies currently playing at their local theater. They already have the location of the user provided by location services to their agent, and a Delta table which is continually updated with the latest showtime information by location. They want to implement this new capability In their RAG application.
Which option will do this with the least effort and in the most performant way?
正解：D
解説：
The task is to extend a cinema chatbot to provide movie showtime information using a RAG application, leveraging user location and a continuously updated Delta table, with minimal effort and high performance.
Let's evaluate the options.
* Option A: Create a Feature Serving Endpoint from a FeatureSpec that references an online store synced from the Delta table. Query the Feature Serving Endpoint as part of the agent logic / tool implementation
* Databricks Feature Serving provides low-latency access to real-time data from Delta tables via an online store. Syncing the Delta table to a Feature Serving Endpoint allows the chatbot to query showtimes efficiently, integrating seamlessly into the RAG agent'stool logic. This leverages Databricks' native infrastructure, minimizing effort and ensuring performance.
* Databricks Reference:"Feature Serving Endpoints provide real-time access to Delta table data with low latency, ideal for production systems"("Databricks Feature Engineering Guide," 2023).
* Option B: Query the Delta table directly via a SQL query constructed from the user's input using a text-to-SQL LLM in the agent logic / tool
* Using a text-to-SQL LLM to generate queries adds complexity (e.g., ensuring accurate SQL generation) and latency (LLM inference + SQL execution). While feasible, it's less performant and requires more effort than a pre-built serving solution.
* Databricks Reference:"Direct SQL queries are flexible but may introduce overhead in real-time applications"("Building LLM Applications with Databricks").
* Option C: Write the Delta table contents to a text column, then embed those texts using an embedding model and store these in the vector index. Look up the information based on the embedding as part of the agent logic / tool implementation
* Converting structured Delta table data (e.g., showtimes) into text, embedding it, and using vector search is inefficient for structured lookups. It's effort-intensive (preprocessing, embedding) and less precise than direct queries, undermining performance.
* Databricks Reference:"Vector search excels for unstructured data, not structured tabular lookups"("Databricks Vector Search Documentation").
* Option D: Set up a task in Databricks Workflows to write the information in the Delta table periodically to an external database such as MySQL and query the information from there as part of the agent logic / tool implementation
* Exporting to an external database (e.g., MySQL) adds setup effort (workflow, external DB management) and latency (periodic updates vs. real-time). It's less performant and more complex than using Databricks' native tools.
* Databricks Reference:"Avoid external systems when Delta tables provide real-time data natively"("Databricks Workflows Guide").
Conclusion: Option A minimizes effort by using Databricks Feature Serving for real-time, low-latency access to the Delta table, ensuring high performance in a production-ready RAG chatbot.
質問 # 85
A Generative Al Engineer is building a production-ready LLM system which replies directly to customers. The solution makes use of the Foundation Model API via provisioned throughput. They are concerned that the LLM could potentially respond in a toxic or otherwise unsafe way. They also wish to perform this with the least amount of effort.
Which approach will do this?
正解：D
解説：
The task is to prevent toxic or unsafe responses in an LLM system using the Foundation Model API with minimal effort. Let's assess the options.
Option A: Host Llama Guard on Foundation Model API and use it to detect unsafe responses Llama Guard is a safety-focused model designed to detect toxic or unsafe content. Hosting it via the Foundation Model API (a Databricks service) integrates seamlessly with the existing system, requiring minimal setup (just deployment and a check step), and leverages provisioned throughput for performance.
Databricks Reference: "Foundation Model API supports hosting safety models like Llama Guard to filter outputs efficiently" ("Foundation Model API Documentation," 2023).
Option B: Add some LLM calls to their chain to detect unsafe content before returning text Using additional LLM calls (e.g., prompting an LLM to classify toxicity) increases latency, complexity, and effort (crafting prompts, chaining logic), and lacks the specificity of a dedicated safety model.
Databricks Reference: "Ad-hoc LLM checks are less efficient than purpose-built safety solutions" ("Building LLM Applications with Databricks").
Option C: Add a regex expression on inputs and outputs to detect unsafe responses Regex can catch simple patterns (e.g., profanity) but fails for nuanced toxicity (e.g., sarcasm, context-dependent harm), requiring significant manual effort to maintain and update rules.
Databricks Reference: "Regex-based filtering is limited for complex safety needs" ("Generative AI Cookbook").
Option D: Ask users to report unsafe responses
User reporting is reactive, not preventive, and places burden on users rather than the system. It doesn't limit unsafe outputs proactively and requires additional effort for feedback handling.
Databricks Reference: "Proactive guardrails are preferred over user-driven monitoring" ("Databricks Generative AI Engineer Guide").
Conclusion: Option A (Llama Guard on Foundation Model API) is the least-effort, most effective approach, leveraging Databricks' infrastructure for seamless safety integration.
質問 # 86
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