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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Evaluation and Monitoring | 12% | - Evaluate model quality and performance - Monitor deployed applications |
| Topic 2: Assembling and Deploying Apps | 22% | - Use Vector Search - Package and deploy models - Build and deploy RAG pipelines |
| Topic 3: Data Preparation | 14% | - Prepare data for RAG applications - Manage data quality and chunking |
| Topic 4: Application Development | 30% | - Develop LLM chains - Integrate Databricks tools (LangChain, etc.) - Implement prompt engineering |
| Topic 5: Design Applications | 14% | - Define functional requirements - Design and architect applications - Select appropriate models |
| Topic 6: Governance | 8% | - Apply safety filters and guidelines - Manage permissions and logging |
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NEW QUESTION # 69
A Generative AI Engineer has been reviewing issues with their company's LLM-based question-answering assistant and has determined that a technique called prompt chaining could help alleviate some performance concerns. However, to suggest this to their team, they have to clearly explain how it works and how it can benefit their question-answering assistant. Which explanation do they communicate to the team?
Answer: C
Explanation:
Prompt chaining is a fundamental design pattern in LLM application development used to handle complexity. Instead of sending a single, massive, and highly complex prompt to an LLM-which often results in reasoning errors or hallucinations-chaining breaks the logic into a sequence of smaller, targeted steps. For example, a legal assistant might first chain a step to "identify the legal jurisdiction," followed by a step to "extract relevant statutes," and finally a step to "summarize the findings." This modularity improves reliability because each prompt has a narrower focus, making it easier for the model to follow instructions accurately. While it may actually increase latency (contradicting B) and cost (contradicting D) due to multiple API calls, the primary engineering benefit is the significant boost in the quality and robustness of the output. It also allows for intermediate validation and error handling between steps, which is impossible in a single-call architecture.
NEW QUESTION # 70
A Generative AI Engineer at a legal firm is designing a RAG system to analyze historical legal cases. The system needs to process millions of court opinions and legal documents, already organized by time and topic, to track how interpretations of specific laws have evolved over time. All of these documents are in plain-text. The engineer needs to choose a chunking method that would most effectively preserve continuity and the temporal nature of the cases. Which method do they choose?
Answer: B
Explanation:
In the context of legal document analysis where the "evolution of interpretation" is the primary goal, preserving narrative continuity is paramount. Windowed summarization with overlapping chunks is the most effective method for this use case. Overlapping (e.g., 10-15% of the chunk size) ensures that sentences or concepts split at the boundary of one chunk are preserved in the next, preventing the loss of critical context that often occurs in legal jargon. Furthermore, windowed summarization allows the system to condense long-form court opinions into manageable parts while maintaining the chronological "thread" of the argument. While sentence-level embeddings with metadata (D) are useful for filtering, they often lack the sufficient context required to understand the nuances of a legal ruling. A windowed approach provides the LLM with enough surrounding text to understand the "why" behind a legal evolution, rather than just the "when."
NEW QUESTION # 71
A Generative AI Engineer has deployed a RAG application to production. Its Vector Search index is built from a Delta table that receives incremental updates every hour from an upstream ETL pipeline. The team wants the index to automatically reflect source-table changes without manual re-indexing and without changing the endpoint used by downstream applications.
Which approach should the engineer use?
Answer: D
Explanation:
D identifies the index type designed to synchronize with a source Delta table. Delta Sync supports incremental maintenance, avoiding a custom process that repeatedly rebuilds the complete index or manually pushes every changed vector. A Direct Vector Access index transfers update responsibility to the application.
However, this question has a configuration ambiguity: Delta Sync also has a synchronization mode.
Continuous mode processes arriving changes automatically, while triggered mode requires an explicit synchronization request, which can be scheduled after ETL. If the existing index is already a triggered Delta Sync index, B is the relevant configuration change. D is therefore the intended architectural answer, with continuous synchronization or an automated trigger needed to deliver the requested operational behavior.
Databricks API documentation
NEW QUESTION # 72
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?
Answer: B
Explanation:
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's tool 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.
NEW QUESTION # 73
A Generative AI Engineer is developing a chatbot designed to assist users with insurance-related queries. The chatbot is built on a large language model (LLM) and is conversational. However, to maintain the chatbot's focus and to comply with company policy, it must not provide responses to questions about politics. Instead, when presented with political inquiries, the chatbot should respond with a standard message:
"Sorry, I cannot answer that. I am a chatbot that can only answer questions around insurance." Which framework type should be implemented to solve this?
Answer: B
Explanation:
In this scenario, the chatbot must avoid answering political questions and instead provide a standard message for such inquiries. Implementing a Safety Guardrail is the appropriate solution for this:
What is a Safety Guardrail?
Safety guardrails are mechanisms implemented in Generative AI systems to ensure the model behaves within specific bounds. In this case, it ensures the chatbot does not answer politically sensitive or irrelevant questions, which aligns with the business rules.
Preventing Responses to Political Questions:
The Safety Guardrail is programmed to detect specific types of inquiries (like political questions) and prevent the model from generating responses outside its intended domain. When such queries are detected, the guardrail intervenes and provides a pre-defined response: "Sorry, I cannot answer that. I am a chatbot that can only answer questions around insurance." How It Works in Practice:
The LLM system can include a classification layer or trigger rules based on specific keywords related to politics. When such terms are detected, the Safety Guardrail blocks the normal generation flow and responds with the fixed message.
Why Other Options Are Less Suitable:
B (Security Guardrail): This is more focused on protecting the system from security vulnerabilities or data breaches, not controlling the conversational focus.
C (Contextual Guardrail): While context guardrails can limit responses based on context, safety guardrails are specifically about ensuring the chatbot stays within a safe conversational scope.
D (Compliance Guardrail): Compliance guardrails are often related to legal and regulatory adherence, which is not directly relevant here.
Therefore, a Safety Guardrail is the right framework to ensure the chatbot only answers insurance-related queries and avoids political discussions.
NEW QUESTION # 74
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