EXAM DATABRICKS-GENERATIVE-AI-ENGINEER-ASSOCIATE REVIEW, RELIABLE DATABRICKS-GENERATIVE-AI-ENGINEER-ASSOCIATE TEST REVIEW

Exam Databricks-Generative-AI-Engineer-Associate Review, Reliable Databricks-Generative-AI-Engineer-Associate Test Review

Exam Databricks-Generative-AI-Engineer-Associate Review, Reliable Databricks-Generative-AI-Engineer-Associate Test Review

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The most distinguished feature of Easy4Engine's study guides is that they provide you the most workable solution to grasp the core information of the certification syllabus in an easy to learn set of Databricks-Generative-AI-Engineer-Associate study questions. Far more superior in quality than any online courses free, the questions and answers contain information drawn from the best available sources. They are relevant to the Databricks-Generative-AI-Engineer-Associate Exam standards and are made on the format of the actual Databricks-Generative-AI-Engineer-Associate exam.

Databricks Databricks-Generative-AI-Engineer-Associate Exam Syllabus Topics:

TopicDetails
Topic 1
  • Assembling and Deploying Applications: In this topic, Generative AI Engineers get knowledge about coding a chain using a pyfunc mode, coding a simple chain using langchain, and coding a simple chain according to requirements. Additionally, the topic focuses on basic elements needed to create a RAG application. Lastly, the topic addresses sub-topics about registering the model to Unity Catalog using MLflow.
Topic 2
  • Design Applications: The topic focuses on designing a prompt that elicits a specifically formatted response. It also focuses on selecting model tasks to accomplish a given business requirement. Lastly, the topic covers chain components for a desired model input and output.
Topic 3
  • Data Preparation: Generative AI Engineers covers a chunking strategy for a given document structure and model constraints. The topic also focuses on filter extraneous content in source documents. Lastly, Generative AI Engineers also learn about extracting document content from provided source data and format.
Topic 4
  • Evaluation and Monitoring: This topic is all about selecting an LLM choice and key metrics. Moreover, Generative AI Engineers learn about evaluating model performance. Lastly, the topic includes sub-topics about inference logging and usage of Databricks features.
Topic 5
  • Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal
  • licensing requirements in this topic.

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Databricks-Generative-AI-Engineer-Associate - Latest Exam Databricks Certified Generative AI Engineer Associate Review

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Databricks Certified Generative AI Engineer Associate Sample Questions (Q11-Q16):

NEW QUESTION # 11
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?

  • A. DBRX
  • B. Llama2-70B
  • C. MPT-30B
  • D. DistilBERT

Answer: B

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 # 12
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.)

  • A. Decrease the chunk size of embedded documents
  • B. Retrain the response generating model using ALiBi
  • C. Use a smaller embedding model to generate
  • D. Reduce the number of records retrieved from the vector database
  • E. Reduce the maximum output tokens of the new model

Answer: A,D

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.
OptionsCandDare 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 # 13
A Generative Al Engineer is tasked with improving the RAG quality by addressing its inflammatory outputs.
Which action would be most effective in mitigating the problem of offensive text outputs?

  • A. Curate upstream data properly that includes manual review before it is fed into the RAG system
  • B. Inform the user of the expected RAG behavior
  • C. Increase the frequency of upstream data updates
  • D. Restrict access to the data sources to a limited number of users

Answer: A

Explanation:
Addressing offensive or inflammatory outputs in a Retrieval-Augmented Generation (RAG) system is critical for improving user experience and ensuring ethical AI deployment. Here's whyDis the most effective approach:
* Manual data curation: The root cause of offensive outputs often comes from the underlying data used to train the model or populate the retrieval system. By manually curating the upstream data and conducting thorough reviews before the data is fed into the RAG system, the engineer can filter out harmful, offensive, or inappropriate content.
* Improving data quality: Curating data ensures the system retrieves and generates responses from a high-quality, well-vetted dataset. This directly impacts the relevance and appropriateness of the outputs from the RAG system, preventing inflammatory content from being included in responses.
* Effectiveness: This strategy directly tackles the problem at its source (the data) rather than just mitigating the consequences (such as informing users or restricting access). It ensures that the system consistently provides non-offensive, relevant information.
Other options, such as increasing the frequency of data updates or informing users about behavior expectations, may not directly mitigate the generation of inflammatory outputs.


NEW QUESTION # 14
A Generative Al Engineer at an automotive company would like to build a question-answering chatbot for customers to inquire about their vehicles. They have a database containing various documents of different vehicle makes, their hardware parts, and common maintenance information.
Which of the following components will NOT be useful in building such a chatbot?

  • A. Vector database
  • B. Invite users to submit long, rather than concise, questions
  • C. Response-generating LLM
  • D. Embedding model

Answer: B

Explanation:
The task involves building a question-answering chatbot for an automotive company using a database of vehicle-related documents. The chatbot must efficiently process customer inquiries and provide accurate responses. Let's evaluate each component to determine which isnotuseful, per Databricks Generative AI Engineer principles.
* Option A: Response-generating LLM
* An LLM is essential for generating natural language responses to customer queries based on retrieved information. This is a core component of any chatbot.
* Databricks Reference:"The response-generating LLM processes retrieved context to produce coherent answers"("Building LLM Applications with Databricks," 2023).
* Option B: Invite users to submit long, rather than concise, questions
* Encouraging long questions is a user interaction design choice, not a technical component of the chatbot's architecture. Moreover, long, verbose questions can complicate intent detection and retrieval, reducing efficiency and accuracy-counter to best practices for chatbot design. Concise questions are typically preferred for clarity and performance.
* Databricks Reference: While not explicitly stated, Databricks' "Generative AI Cookbook" emphasizes efficient query processing, implying that simpler, focused inputs improve LLM performance. Inviting long questions doesn't align with this.
* Option C: Vector database
* A vector database stores embeddings of the vehicle documents, enabling fast retrieval of relevant information via semantic search. This is critical for a question-answering system with a large document corpus.
* Databricks Reference:"Vector databases enable scalable retrieval of context from large datasets"("Databricks Generative AI Engineer Guide").
* Option D: Embedding model
* An embedding model converts text (documents and queries) into vector representations for similarity search. It's a foundational component for retrieval-augmented generation (RAG) in chatbots.
* Databricks Reference:"Embedding models transform text into vectors, facilitating efficient matching of queries to documents"("Building LLM-Powered Applications").
Conclusion: Option B is not a usefulcomponentin building the chatbot. It's a user-facing suggestion rather than a technical building block, and it could even degrade performance by introducing unnecessary complexity. Options A, C, and D are all integral to a Databricks-aligned chatbot architecture.


NEW QUESTION # 15
A Generative AI Engineer is building an LLM to generate article summaries in the form of a type of poem, such as a haiku, given the article content. However, the initial output from the LLM does not match the desired tone or style.
Which approach will NOT improve the LLM's response to achieve the desired response?

  • A. Include few-shot examples in the prompt to the LLM
  • B. Use a neutralizer to normalize the tone and style of the underlying documents
  • C. Provide the LLM with a prompt that explicitly instructs it to generate text in the desired tone and style
  • D. Fine-tune the LLM on a dataset of desired tone and style

Answer: B

Explanation:
The task at hand is to improve the LLM's ability to generate poem-like article summaries with the desired tone and style. Using aneutralizerto normalize the tone and style of the underlying documents (option B) will not help improve the LLM's ability to generate the desired poetic style. Here's why:
* Neutralizing Underlying Documents:A neutralizer aims to reduce or standardize the tone of input data. However, this contradicts the goal, which is to generate text with aspecific tone and style(like haikus). Neutralizing the source documents will strip away the richness of the content, making it harder for the LLM to generate creative, stylistic outputs like poems.
* Why Other Options Improve Results:
* A (Explicit Instructions in the Prompt): Directly instructing the LLM to generate text in a specific tone and style helps align the output with the desired format (e.g., haikus). This is a common and effective technique in prompt engineering.
* C (Few-shot Examples): Providing examples of the desired output format helps the LLM understand the expected tone and structure, making it easier to generate similar outputs.
* D (Fine-tuning the LLM): Fine-tuning the model on a dataset that contains examples of the desired tone and style is a powerful way to improve the model's ability to generate outputs that match the target format.
Therefore, using a neutralizer (option B) isnotan effective method for achieving the goal of generating stylized poetic summaries.


NEW QUESTION # 16
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