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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Evaluation and Monitoring | 12% | - Evaluate model and application performance - Iterate and improve solutions - Monitor application behavior and outputs |
| Topic 2: Governance | 8% | - Manage access and permissions - Apply Unity Catalog for data governance - Ensure compliance and security |
| Topic 3: Design Applications | 14% | - Design solution architecture - Decompose complex requirements into tasks - Select appropriate models, tools and approaches |
| Topic 4: Application Development | 30% | - Use Databricks Vector Search - Build RAG applications - Develop LLM chains and workflows - Integrate with MLflow |
| Topic 5: Data Preparation | 14% | - Implement data pipelines and transformations - Manage data quality and format - Prepare and process data for LLM use |
| Topic 6: Assembling and Deploying Apps | 22% | - Deploy models via Model Serving - Manage application lifecycle - Package and deploy applications |
>> Exam Sample Databricks-Generative-AI-Engineer-Associate Questions <<
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NEW QUESTION # 33
A Generative AI Engineer has set up an endpoint with AI Guardrails turned on to block any incoming requests that divulge PII. They also have inference tables enabled for this endpoint. If an end user sends their phone number in their prompt, what will appear in the inference table for that record?
Answer: D
Explanation:
Option D is the best-supported answer for the endpoint behavior described. An input guardrail configured to block detected PII prevents the offending request from proceeding normally and returns a blocking response.
Inference tables record request and response payloads, so the response represents the guardrail outcome rather than a hypothetical answer the model might otherwise have produced. Blocking a request is distinct from disabling its audit logging, and a blocked response is not inherently an empty response. This answer assumes the phone number is detected and the record is successfully logged. Databricks documents best-effort logging and version-dependent gateway behavior; newer guardrail implementations may return a structured blocking message rather than the same HTTP error format used by older endpoints. Guardrail documentation , inference-table documentation
NEW QUESTION # 34
A Generative AI Engineer is developing an LLM application that users can use to generate personalized birthday poems based on their names.
Which technique would be most effective in safeguarding the application, given the potential for malicious user inputs?
Answer: B
Explanation:
In this case, the Generative AI Engineer is developing an application to generate personalized birthday poems, but there's a need to safeguard against malicious user inputs. The best solution is to implement a safety filter (option A) to detect harmful or inappropriate inputs.
Safety Filter Implementation:
Safety filters are essential for screening user input and preventing inappropriate content from being processed by the LLM. These filters can scan inputs for harmful language, offensive terms, or malicious content and intervene before the prompt is passed to the LLM.
Graceful Handling of Harmful Inputs:
Once the safety filter detects harmful content, the system can provide a message to the user, such as "I'm unable to assist with this request," instead of processing or responding to malicious input. This protects the system from generating harmful content and ensures a controlled interaction environment.
Why Other Options Are Less Suitable:
B (Reduce Interaction Time): Reducing the interaction time won't prevent malicious inputs from being entered.
C (Continue the Conversation): While it's possible to acknowledge malicious input, it is not safe to continue the conversation with harmful content. This could lead to legal or reputational risks.
D (Increase Compute Power): Adding more compute doesn't address the issue of harmful content and would only speed up processing without resolving safety concerns.
Therefore, implementing a safety filter that blocks harmful inputs is the most effective technique for safeguarding the application.
NEW QUESTION # 35
After changing the response generating LLM in a RAG pipeline from GPT-4 to a model with a shorter context length that the company self-hosts, the Generative AI Engineer is getting the following error:
What TWO solutions should the Generative AI Engineer implement without changing the response generating model? (Choose two.)
Answer: D,E
Explanation:
* Problem Context : After switching to a model with a shorter context length, the error message indicating that the prompt token count has exceeded the limit suggests that the input to the model is too large.
* Explanation of Options :
* Option A: Use a smaller embedding model to generate - This wouldn ' t necessarily address the issue of prompt size exceeding the model's token limit.
* Option B: Reduce the maximum output tokens of the new model - This option affects the output length, not the size of the input being too large.
* Option C: Decrease the chunk size of embedded documents - This would help reduce the size of each document chunk fed into the model, ensuring that the input remains within the model ' s context length limitations.
* Option D: Reduce the number of records retrieved from the vector database - By retrieving fewer records, the total input size to the model can be managed more effectively, keeping it within the allowable token limits.
* Option E: Retrain the response generating model using ALiBi - Retraining the model is contrary to the stipulation not to change the response generating model.
Options C and D are the most effective solutions to manage the model's shorter context length without changing the model itself, by adjusting the input size both in terms of individual document size and total documents retrieved.
NEW QUESTION # 36
Generative AI Engineer at an electronics company just deployed a RAG application for customers to ask questions about products that the company carries. However, they received feedback that the RAG response often returns information about an irrelevant product.
What can the engineer do to improve the relevance of the RAG's response?
Answer: A
Explanation:
In a Retrieval-Augmented Generation (RAG) system, the key to providing relevant responses lies in the quality of the retrieved context. Here's why option A is the most appropriate solution:
* Context Relevance:The RAG model generates answers based on retrieved documents or context. If the retrieved information is about an irrelevant product, it suggests that the retrieval step is failing to select the right context. The Generative AI Engineer must first assess the quality of what is being retrieved and ensure it is pertinent to the query.
* Vector Search and Embedding Similarity:RAG typically uses vector search for retrieval, where embeddings of the query are matched against embeddings of product descriptions. Assessing the semantic similarity searchprocess ensures that the closest matches are actually relevant to the query.
* Fine-tuning the Retrieval Process:By improving theretrieval quality, such as tuning the embeddings or adjusting the retrieval strategy, the system can return more accurate and relevant product information.
* Why Other Options Are Less Suitable:
* B (Caching FAQs): Caching can speed up responses for frequently asked questions but won't improve the relevance of the retrieved content for less frequent or new queries.
* C (Use a Different LLM): Changing the LLM only affects the generation step, not the retrieval process, which is the core issue here.
* D (Different Semantic Search Algorithm): This could help, but the first step is to evaluate the current retrieval context before replacing the search algorithm.
Therefore, improving and assessing the quality of the retrieved context (option A) is the first step to fixing the issue of irrelevant product information.
NEW QUESTION # 37
A Generative Al Engineer has created a RAG application to look up answers to questions about a series of fantasy novels that are being asked on the author's web forum. The fantasy novel texts are chunked and embedded into a vector store with metadata (page number, chapter number, book title), retrieved with the user' s query, and provided to an LLM for response generation. The Generative AI Engineer used their intuition to pick the chunking strategy and associated configurations but now wants to more methodically choose the best values.
Which TWO strategies should the Generative AI Engineer take to optimize their chunking strategy and parameters? (Choose two.)
Answer: D,E
Explanation:
To optimize a chunking strategy for a Retrieval-Augmented Generation (RAG) application, the Generative AI Engineer needs a structured approach to evaluating the chunking strategy, ensuring that the chosen configuration retrieves the most relevant information and leads to accurate and coherent LLM responses. Here
' s why C and E are the correct strategies:
Strategy C: Evaluation Metrics (Recall, NDCG)
* Define an evaluation metric : Common evaluation metrics such as recall, precision, or NDCG (Normalized Discounted Cumulative Gain) measure how well the retrieved chunks match the user ' s query and the expected response.
* Recall measures the proportion of relevant information retrieved.
* NDCG is often used when you want to account for both the relevance of retrieved chunks and the ranking or order in which they are retrieved.
* Experiment with chunking strategies : Adjusting chunking strategies based on text structure (e.g., splitting by paragraph, chapter, or a fixed number of tokens) allows the engineer to experiment with various ways of slicing the text. Some chunks may better align with the user ' s query than others.
* Evaluate performance : By using recall or NDCG, the engineer can methodically test various chunking strategies to identify which one yields the highest performance. This ensures that the chunking method provides the most relevant information when embedding and retrieving data from the vector store.
Strategy E: LLM-as-a-Judge Metric
* Use the LLM as an evaluator : After retrieving chunks, the LLM can be used to evaluate the quality of answers based on the chunks provided. This could be framed as a " judge " function, where the LLM compares how well a given chunk answers previous user queries.
* Optimize based on the LLM ' s judgment : By having the LLM assess previous answers and rate their relevance and accuracy, the engineer can collect feedback on how well different chunking configurations perform in real-world scenarios.
* This metric could be a qualitative judgment on how closely the retrieved information matches the user ' s intent.
* Tune chunking parameters : Based on the LLM ' s judgment, the engineer can adjust the chunk size or structure to better align with the LLM ' s responses, optimizing retrieval for future queries.
By combining these two approaches, the engineer ensures that the chunking strategy is systematically evaluated using both quantitative (recall/NDCG) and qualitative (LLM judgment) methods. This balanced optimization process results in improved retrieval relevance and, consequently, better response generation by the LLM.
NEW QUESTION # 38
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