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NEW QUESTION # 76
A Generative Al Engineer is tasked with developing an application that is based on an open source large language model (LLM). They need a foundation LLM with a large context window.
Which model fits this need?
Answer: A
Explanation:
* Problem Context: The engineer needs an open-source LLM with a large context window to develop an application.
* Explanation of Options:
* Option A: DistilBERT: While an efficient and smaller version of BERT, DistilBERT does not provide a particularly large context window.
* Option B: MPT-30B: This model, while large, is not specified as being particularly notable for its context window capabilities.
* Option C: Llama2-70B: Known for its large model size and extensive capabilities, including a large context window. It is also available as an open-source model, making it ideal for applications requiring extensive contextual understanding.
* Option D: DBRX: This is not a recognized standard model in the context of large language models with extensive context windows.
Thus,Option C(Llama2-70B) is the best fit as it meets the criteria of having a large context window and being available for open-source use, suitable for developing robust language understanding applications.
NEW QUESTION # 77
A Generative AI Engineer is building an interactive catalog for a company's inventory system that allows users to search for any item using a plain-text description. There are currently about 17,000 items, and new items are not frequently added. They need a solution that will be the most cost-effective and easy for the company to maintain.
Which solution should the engineer choose?
Answer: C
Explanation:
Option D best matches the small catalog, infrequent updates, and maintenance requirements. Databricks- managed embeddings remove the need to build and maintain a separate embedding-generation pipeline. A Delta Sync index connects the searchable content to its source Delta table. Triggered synchronization updates the index when explicitly invoked, making it suitable when inventory changes are occasional. Continuous synchronization maintains a running streaming pipeline and therefore introduces additional synchronization costs that this scenario does not justify. Self-managed embeddings add engineering responsibilities without a stated benefit. Storage-optimized endpoints primarily address much larger vector collections. Triggered synchronization reduces synchronization overhead; it does not eliminate the separate costs of serving the vector index or computing embeddings. Databricks documentation
NEW QUESTION # 78
A Generative AI Engineer is integrating Mosaic AI Vector Search into a Retrieval-Augmented Generation (RAG) system. The source data, comprising simple text entries, is stored in a Delta table. To simplify the workflow, the engineer plans to use an embedding model served via a Mosaic AI Model Serving endpoint to automatically compute embeddings during data synchronization from the Delta table to the vector search index.
Which method should the engineer use to achieve this integration?
Answer: D
Explanation:
A Delta Sync index with managed embeddings supports the workflow described. The engineer identifies the source text column and configures the embedding model endpoint. Databricks computes embeddings as part of synchronization and maintains the corresponding searchable index. This removes the requirement to calculate vectors independently and write them into the source table before indexing. With self-managed embeddings, the application or upstream pipeline must already provide the embedding vectors. A Direct Vector Access index requires explicit updates through an API or SDK and does not automatically synchronize with the source Delta table. "Hybrid embedding computing" is not the relevant index configuration; hybrid search combines retrieval techniques. Managed embeddings and Delta synchronization therefore address both automatic vector generation and source-data integration. Databricks documentation
NEW QUESTION # 79
A Generative Al Engineer is building a RAG application that answers questions about internal documents for the company SnoPen AI.
The source documents may contain a significant amount of irrelevant content, such as advertisements, sports news, or entertainment news, or content about other companies.
Which approach is advisable when building a RAG application to achieve this goal of filtering irrelevant information?
Answer: C
Explanation:
In a Retrieval-Augmented Generation (RAG) application built to answer questions about internal documents, especially when the dataset contains irrelevant content, it's crucial to guide the system to focus on the right information. The best way to achieve this is by including a clear instruction in the system prompt (option C).
System Prompt as Guidance:
The system prompt is an effective way to instruct the LLM to limit its focus to SnoPen AI-related content. By clearly specifying that the model should avoid answering questions unrelated to SnoPen AI, you add an additional layer of control that helps the model stay on-topic, even if irrelevant content is present in the dataset.
Why This Approach Works:
The prompt acts as a guiding principle for the model, narrowing its focus to specific domains. This prevents the model from generating answers based on irrelevant content, such as advertisements or news unrelated to SnoPen AI.
Why Other Options Are Less Suitable:
A (Keep All Articles): Retaining all content, including irrelevant materials, without any filtering makes the system prone to generating answers based on unwanted data.
B (Include in the System Prompt about SnoPen AI): This option doesn't address irrelevant content directly, and without filtering, the model might still retrieve and use irrelevant data.
D (Consolidating Documents into a Single Chunk): Grouping documents into a single chunk makes the retrieval process less efficient and won't help filter out irrelevant content effectively.
Therefore, instructing the system in the prompt not to answer questions unrelated to SnoPen AI (option C) is the best approach to ensure the system filters out irrelevant information.
NEW QUESTION # 80
A Generative Al Engineer is using an LLM to classify species of edible mushrooms based on text descriptions of certain features. The model is returning accurate responses in testing and the Generative Al Engineer is confident they have the correct list of possible labels, but the output frequently contains additional reasoning in the answer when the Generative Al Engineer only wants to return the label with no additional text.
Which action should they take to elicit the desired behavior from this LLM?
Answer: D
Explanation:
The LLM classifies mushroom species accurately but includes unwanted reasoning text, and the engineer wants only the label. Let's assess how to control output format effectively.
Option A: Use few shot prompting to instruct the model on expected output format Few-shot prompting provides examples (e.g., input: description, output: label). It can work but requires crafting multiple examples, which is effort-intensive and less direct than a clear instruction.
Databricks Reference: "Few-shot prompting guides LLMs via examples, effective for format control but requires careful design" ("Generative AI Cookbook").
Option B: Use zero shot prompting to instruct the model on expected output format Zero-shot prompting relies on a single instruction (e.g., "Return only the label") without examples. It's simpler than few-shot but may not consistently enforce succinctness if the LLM's default behavior is verbose.
Databricks Reference: "Zero-shot prompting can specify output but may lack precision without examples" ("Building LLM Applications with Databricks").
Option C: Use zero shot chain-of-thought prompting to prevent a verbose output format Chain-of-Thought (CoT) encourages step-by-step reasoning, which increases verbosity-opposite to the desired outcome. This contradicts the goal of label-only output.
Databricks Reference: "CoT prompting enhances reasoning but often results in detailed responses" ("Databricks Generative AI Engineer Guide").
Option D: Use a system prompt to instruct the model to be succinct in its answer A system prompt (e.g., "Respond with only the species label, no additional text") sets a global instruction for the LLM's behavior. It's direct, reusable, and effective for controlling output style across queries.
Databricks Reference: "System prompts define LLM behavior consistently, ideal for enforcing concise outputs" ("Generative AI Cookbook," 2023).
Conclusion: Option D is the most effective and straightforward action, using a system prompt to enforce succinct, label-only responses, aligning with Databricks' best practices for output control.
NEW QUESTION # 81
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