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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Generative AI Engineer Associate |
| Exam Number: | Databricks-Generative-AI-Engineer-Associate |
| Exam Format: | Multiple select, Multiple choice |
| Real Exam Qty: | 45-60 |
| Exam Duration: | 90 minutes |
| Certificate Validity Period: | 2 years |
| Exam Price: | USD 200 |
| Available Languages: | English |
| Related Certifications: | Databricks Certified Machine Learning Associate Databricks Certified Data Engineer Associate |
| Recommended Training: | Mosaic AI Documentation Databricks Academy - Generative AI Courses |
| Exam Registration: | Databricks Certification Portal |
| Sample Questions: | Databricks Databricks-Generative-AI-Engineer-Associate Sample Questions |
| Exam Way: | Online proctored exam |
| Pre Condition: | No strict prerequisites, but experience with Python, machine learning fundamentals, and Databricks platform is recommended. |
| Official Syllabus URL: | https://www.databricks.com/learn/certification |
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NEW QUESTION # 56
A Generative AI Engineer has created a RAG application which can help employees retrieve answers from an internal knowledge base, such as Confluence pages or Google Drive. The prototype application is now working with some positive feedback from internal company testers. Now the Generative Al Engineer wants to formally evaluate the system's performance and understand where to focus their efforts to further improve the system.
How should the Generative AI Engineer evaluate the system?
Answer: B
Explanation:
* Problem Context: After receiving positive feedback for the RAG application prototype, the next step is to formally evaluate the system to pinpoint areas for improvement.
* Explanation of Options:
* Option A: While cosine similarity scores are useful, they primarily measure similarity rather than the overall performance of an RAG system.
* Option B: This option provides a systematic approach to evaluation by testing both retrieval and generation components separately. This allows for targeted improvements and a clear understanding of each component's performance, using MLflow's metrics for a structured and standardized assessment.
* Option C: Benchmarking multiple LLMs does not focus on evaluating the existing system's components but rather on comparing different models.
* Option D: Using an LLM as a judge is subjective and less reliable for systematic performance evaluation.
OptionBis the most comprehensive and structured approach, facilitating precise evaluations and improvements on specific components of the RAG system.
NEW QUESTION # 57
A Generative Al Engineer is building a system that will answer questions on currently unfolding news topics.
As such, it pulls information from a variety of sources including articles and social media posts. They are concerned about toxic posts on social media causing toxic outputs from their system.
Which guardrail will limit toxic outputs?
Answer: C
Explanation:
The system answers questions on unfolding news topics using articles and social media, with a concern about toxic outputs from toxic inputs. A guardrail must limit toxicity in the LLM's responses. Let's evaluate the options.
* Option A: Use only approved social media and news accounts to prevent unexpected toxic data from getting to the LLM
* Curating input sources (e.g., verified accounts) reduces exposure to toxic content at the data ingestion stage, directly limiting toxic outputs. This is a proactive guardrail aligned with data quality control.
* Databricks Reference:"Control input data quality to mitigate unwanted LLM behavior, such as toxicity"("Building LLM Applications with Databricks," 2023).
* Option B: Implement rate limiting
* Rate limiting controls request frequency, not content quality. It prevents overload but doesn't address toxicity in social media inputs or outputs.
* Databricks Reference: Rate limiting is for performance, not safety:"Use rate limits to manage compute load"("Generative AI Cookbook").
* Option C: Reduce the amount of context items the system will include in consideration for its response
* Reducing context might limit exposure to some toxic items but risks losing relevant information, and it doesn't specifically target toxicity. It's an indirect, imprecise fix.
* Databricks Reference: Context reduction is for efficiency, not safety:"Adjust context size based on performance needs"("Databricks Generative AI Engineer Guide").
* Option D: Log all LLM system responses and perform a batch toxicity analysis monthly
* Logging and analyzing responses is reactive, identifying toxicity after it occurs rather than preventing it. Monthly analysis doesn't limit real-time toxic outputs.
* Databricks Reference: Monitoring is for auditing, not prevention:"Log outputs for post-hoc analysis, but use input filters for safety"("Building LLM-Powered Applications").
Conclusion: Option A is the most effective guardrail, proactively filtering toxic inputs from unverified sources, which aligns with Databricks' emphasis on data quality as a primary safety mechanism for LLM systems.
NEW QUESTION # 58
A Generative AI Engineer is deploying a customer-facing, fine-tuned LLM on their public website. Given the large investment the company put into fine-tuning this model, and the proprietary nature of the tuning data, they are concerned about model inversion attacks. Which of the following Databricks AI Security Framework (DASF) risk mitigation strategies are most relevant to this use case?
Answer: B
Explanation:
Model inversion attacks occur when an attacker uses the model's outputs to reconstruct the sensitive training data used during the fine-tuning process. To mitigate this in a public-facing application, implementing AI Guardrails is the most relevant strategy. Guardrails act as a programmable "filter" between the LLM and the end-user. They can be configured to detect if a model's response contains patterns that look like proprietary training data or PII (Personally Identifiable Information). While ACLs (B) and ABAC (D) protect the model's infrastructure (who can invoke the API), they do not inspect the content of the output, which is where the inversion attack actually manifests. Databricks provides integrated guardrail capabilities (via Mosaic AI Gateway) specifically to enforce compliance and prevent the leakage of sensitive internal knowledge that may have been baked into the model weights during fine-tuning.
NEW QUESTION # 59
A Generative AI Engineer is building a compound AI system for an organization. The goal is to automate the processing of incoming customer event reports against a coding system and corporate-guidelines documentation. The system must handle three distinct user-request types: answering questions from guidelines documents, extracting specific event codes from reviewers' notes, and routing ambiguous requests to the appropriate specialized handler. All three capabilities must operate under a single entry point that interprets user intent and delegates accordingly.
Which Agent Brick should serve as the top-level orchestrator in this architecture?
Answer: C
Explanation:
The Multi-Agent Supervisor is designed to coordinate specialized agents and tools behind a unified entry point. It interprets the incoming request, selects suitable capabilities, delegates work, and combines results when necessary. In this scenario, document questions can be handled by a knowledge-retrieval specialist, while event-code extraction can be assigned to a specialized extraction capability. The supervisor provides the coordination needed to select between those tasks and handle requests requiring multiple steps. A Knowledge Assistant's conversational interface does not itself make it the appropriate general-purpose orchestrator.
Adding vector indexes also does not establish specialized extraction and routing behavior. Although option A names the same component, its justification misses the central requirement: coordinated delegation across distinct capabilities. Databricks documentation
NEW QUESTION # 60
A Generative Al Engineer is setting up a Databricks Vector Search that will lookup news articles by topic within 10 days of the date specified An example query might be "Tell me about monster truck news around January 5th 1992". They want to do this with the least amount of effort.
How can they set up their Vector Search index to support this use case?
Answer: C
Explanation:
The task is to set up a Databricks Vector Search index for news articles, supporting queries like "monster truck news around January 5th, 1992," with minimal effort. The index must filter by topic and a 10-day date range. Let's evaluate the options.
* Option A: Split articles by 10-day blocks and return the block closest to the query
* Pre-splitting articles into 10-day blocks requires significant preprocessing and index management (e.g., one index per block). It's effort-intensive and inflexible for dynamic date ranges.
* Databricks Reference:"Static partitioning increases setup complexity; metadata filtering is preferred"("Databricks Vector Search Documentation").
* Option B: Include metadata columns for article date and topic to support metadata filtering
* Adding date and topic as metadata in the Vector Search index allows dynamic filtering (e.g., date
± 5 days, topic = "monster truck") at query time. This leverages Databricks' built-in metadata filtering, minimizing setup effort.
* Databricks Reference:"Vector Search supports metadata filtering on columns like date or category for precise retrieval with minimal preprocessing"("Vector Search Guide," 2023).
* Option C: Pass the query directly to the vector search index and return the best articles
* Passing the full query (e.g., "Tell me about monster truck news around January 5th, 1992") to Vector Search relies solely on embeddings, ignoring structured filtering for date and topic. This risks inaccurate results without explicit range logic.
* Databricks Reference:"Pure vector similarity may not handle temporal or categorical constraints effectively"("Building LLM Applications with Databricks").
* Option D: Create separate indexes by topic and add a classifier model to appropriately pick the best index
* Separate indexes per topic plus a classifier model adds significant complexity (index creation, model training, maintenance), far exceeding "least effort." It's overkill for this use case.
* Databricks Reference:"Multiple indexes increase overhead; single-index with metadata is simpler"("Databricks Vector Search Documentation").
Conclusion: Option B is the simplest and most effective solution, using metadata filtering in a single Vector Search index to handle date ranges and topics, aligning with Databricks' emphasis on efficient, low-effort setups.
NEW QUESTION # 61
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