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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 |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 90 minutes |
| Available Languages: | English |
| Exam Price: | USD 200 |
| Real Exam Qty: | 45-60 |
| Related Certifications: | Databricks Certified Machine Learning Associate Databricks Certified Data Engineer Associate |
| Recommended Training: | Databricks Academy - Generative AI Courses Mosaic AI Documentation |
| 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 |
>> Latest Databricks-Generative-AI-Engineer-Associate Dumps <<
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NEW QUESTION # 76
A Generative AI Engineer at an automotive company would like to build a question-answering chatbot to help customers answer specific questions about their vehicles. They have:
* A catalog with hundreds of thousands of cars manufactured since the 1960s
* Historical searches with user queries and successful matches
* Descriptions of their own cars in multiple languages
They have already selected an open-source LLM and created a test set of user queries. They need to discard techniques that will not help them build the chatbot. Which do they discard?
Answer: D
Explanation:
According to Generative AI engineering standards for Retrieval-Augmented Generation (RAG), chunking strategy is a critical optimization variable. Setting the chunk size to match the model ' s maximum context window (e.g., 4k or 8k tokens) is a poor practice and should be discarded. Large chunks introduce significant
" noise " into the LLM ' s context, as only a small portion of a massive chunk usually contains the answer to a specific query. This leads to the " lost in the middle " phenomenon where LLMs struggle to extract relevant information from bloated contexts. Furthermore, large chunks reduce the precision of the vector search.
Standard best practices involve using smaller, semantically meaningful chunks (typically 256-512 tokens) with overlap to maintain context. In contrast, metadata filtering (B) is essential for narrowing searches to specific car years, fine-tuning embeddings (C) improves retrieval accuracy for domain-specific technical terms, and few-shot examples (D) guide the LLM ' s output format and tone.
NEW QUESTION # 77
A Generative Al Engineer is creating an LLM system that will retrieve news articles from the year 1918 and related to a user's query and summarize them. The engineer has noticed that the summaries are generated well but often also include an explanation of how the summary was generated, which is undesirable.
Which change could the Generative Al Engineer perform to mitigate this issue?
Answer: A
Explanation:
To mitigate the issue of the LLM including explanations of how summaries are generated in its output, the best approach is to adjust the training or prompt structure. Here's why Option D is effective:
* Few-shot Learning: By providing specific examples of how the desired output should look (i.e., just the summary without explanation), the model learns the preferred format. This few-shot learning approach helps the model understand not only what content to generate but also how to format its responses.
* Prompt Engineering: Adjusting the user prompt to specify the desired output format clearly can guide the LLM to produce summaries without additional explanatory text. Effective prompt design is crucial in controlling the behavior of generative models.
Why Other Options Are Less Suitable:
* A: While technically feasible, splitting the output by newline and truncating could lead to loss of important content or create awkward breaks in the summary.
* B: Tuning chunk sizes or changing embedding models does not directly address the issue of the model's tendency to generate explanations along with summaries.
* C: Revisiting document ingestion logic ensures accurate source data but does not influence how the model formats its output.
By using few-shot examples and refining the prompt, the engineer directly influences the output format, making this approach the most targeted and effective solution.
NEW QUESTION # 78
A Generative Al Engineer is developing a RAG application and would like to experiment with different embedding models to improve the application performance.
Which strategy for picking an embedding model should they choose?
Answer: B
Explanation:
The task involves improving a Retrieval-Augmented Generation (RAG) application's performance by experimenting with embedding models. The choice of embedding model impacts retrieval accuracy, which is critical for RAG systems. Let's evaluate the options based on Databricks Generative AI Engineer best practices.
Option A: Pick an embedding model trained on related domain knowledge
Embedding models trained on domain-specific data (e.g., industry-specific corpora) produce vectors that better capture the semantics of the application's context, improving retrieval relevance. For RAG, this is a key strategy to enhance performance.
Databricks Reference: "For optimal retrieval in RAG systems, select embedding models aligned with the domain of your data" ("Building LLM Applications with Databricks," 2023).
Option B: Pick the most recent and most performant open LLM released at the time LLMs are not embedding models; they generate text, not embeddings for retrieval. While recent LLMs may be performant for generation, this doesn't address the embedding step in RAG. This option misunderstands the component being selected.
Databricks Reference: Embedding models and LLMs are distinct in RAG workflows: "Embedding models convert text to vectors, while LLMs generate responses" ("Generative AI Cookbook").
Option C: Pick the embedding model ranked highest on the Massive Text Embedding Benchmark (MTEB) leaderboard hosted by HuggingFace The MTEB leaderboard ranks models across general tasks, but high overall performance doesn't guarantee suitability for a specific domain. A top-ranked model might excel in generic contexts but underperform on the engineer's unique data.
Databricks Reference: General performance is less critical than domain fit: "Benchmark rankings provide a starting point, but domain-specific evaluation is recommended" ("Databricks Generative AI Engineer Guide").
Option D: Pick an embedding model with multilingual support to support potential multilingual user questions Multilingual support is useful only if the application explicitly requires it. Without evidence of multilingual needs, this adds complexity without guaranteed performance gains for the current use case.
Databricks Reference: "Choose features like multilingual support based on application requirements" ("Building LLM-Powered Applications").
Conclusion: Option A is the best strategy because it prioritizes domain relevance, directly improving retrieval accuracy in a RAG system-aligning with Databricks' emphasis on tailoring models to specific use cases.
NEW QUESTION # 79
Databricks offers a number of built-in AI judges that provide metrics and rationale for different types of quality issues a Generative AI application may have.
Which of the following pairs of judges both require a ground-truth label in the evaluation dataset field expected_response to execute?
Answer: B
Explanation:
In the Agent Evaluation terminology used by this question, correctness compares the generated response with the expected answer, while context_sufficiency determines whether the retrieved context contains enough information to produce that expected answer. Both therefore depend on ground-truth information supplied through expected_response. Groundedness instead examines whether the response is supported by the retrieved context; it does not inherently need a reference answer. Chunk relevance and relevance to the query assess alignment with the user's request, while guideline adherence evaluates compliance with specified instructions. Databricks explicitly distinguishes the ground-truth-dependent judges in its migration documentation. In MLflow 3, names and schemas have evolved, including expectations.expected_response and explicitly selected scorers, so the question should be interpreted using its stated legacy judge terminology.
Databricks documentation
NEW QUESTION # 80
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: C,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 whyCandEare 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.
* Recallmeasures the proportion of relevant information retrieved.
* NDCGis 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 # 81
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