DATABRICKS-GENERATIVE-AI-ENGINEER-ASSOCIATE TEST QUESTION | AUTHENTIC DATABRICKS-GENERATIVE-AI-ENGINEER-ASSOCIATE EXAM HUB

Databricks-Generative-AI-Engineer-Associate Test Question | Authentic Databricks-Generative-AI-Engineer-Associate Exam Hub

Databricks-Generative-AI-Engineer-Associate Test Question | Authentic Databricks-Generative-AI-Engineer-Associate Exam Hub

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

TopicDetails
Topic 1
  • Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal
  • licensing requirements in this topic.
Topic 2
  • 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 3
  • 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 4
  • 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 5
  • 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.

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Databricks Certified Generative AI Engineer Associate Sample Questions (Q18-Q23):

NEW QUESTION # 18
A Generative AI Engineer I using the code below to test setting up a vector store:

Assuming they intend to use Databricks managed embeddings with the default embedding model, what should be the next logical function call?

  • A. vsc.create_direct_access_index()
  • B. vsc.similarity_search()
  • C. vsc.get_index()
  • D. vsc.create_delta_sync_index()

Answer: D

Explanation:
Context: The Generative AI Engineer is setting up a vector store using Databricks' VectorSearchClient. This is typically done to enable fast and efficient retrieval of vectorized data for tasks like similarity searches.
Explanation of Options:
* Option A: vsc.get_index(): This function would be used to retrieve an existing index, not create one, so it would not be the logical next step immediately after creating an endpoint.
* Option B: vsc.create_delta_sync_index(): After setting up a vector store endpoint, creating an index is necessary to start populating and organizing the data. The create_delta_sync_index() function specifically creates an index that synchronizes with a Delta table, allowing automatic updates as the data changes. This is likely the most appropriate choice if the engineer plans to use dynamic data that is updated over time.
* Option C: vsc.create_direct_access_index(): This function would create an index that directly accesses the data without synchronization. While also a valid approach, it's less likely to be the next logical step if the default setup (typically accommodating changes) is intended.
* Option D: vsc.similarity_search(): This function would be used to perform searches on an existing index; however, an index needs to be created and populated with data before any search can be conducted.
Given the typical workflow in setting up a vector store, the next step after creating an endpoint is to establish an index, particularly one that synchronizes with ongoing data updates, henceOption B.


NEW QUESTION # 19
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. Provide the LLM with a prompt that explicitly instructs it to generate text in the desired tone and style
  • B. Include few-shot examples in the prompt to the LLM
  • C. Use a neutralizer to normalize the tone and style of the underlying documents
  • D. Fine-tune the LLM on a dataset of desired tone and style

Answer: C

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 # 20
A Generative Al Engineer is building a system that will answer questions on currently unfolding news topics.
As such, it pulls information from a variety of sources including articles and social media posts. They are concerned about toxic posts on social media causing toxic outputs from their system.
Which guardrail will limit toxic outputs?

  • A. Log all LLM system responses and perform a batch toxicity analysis monthly.
  • B. Use only approved social media and news accounts to prevent unexpected toxic data from getting to the LLM.
  • C. Reduce the amount of context Items the system will Include in consideration for its response.
  • D. Implement rate limiting

Answer: B

Explanation:
The system answers questions on unfolding news topics using articles and social media, with a concern about toxic outputs from toxic inputs. A guardrail must limit toxicity in the LLM's responses. Let's evaluate the options.
* Option A: Use only approved social media and news accounts to prevent unexpected toxic data from getting to the LLM
* Curating input sources (e.g., verified accounts) reduces exposure to toxic content at the data ingestion stage, directly limiting toxic outputs. This is a proactive guardrail aligned with data quality control.
* Databricks Reference:"Control input data quality to mitigate unwanted LLM behavior, such as toxicity"("Building LLM Applications with Databricks," 2023).
* Option B: Implement rate limiting
* Rate limiting controls request frequency, not content quality. It prevents overload but doesn't address toxicity in social media inputs or outputs.
* Databricks Reference: Rate limiting is for performance, not safety:"Use rate limits to manage compute load"("Generative AI Cookbook").
* Option C: Reduce the amount of context items the system will include in consideration for its response
* Reducing context might limit exposure to some toxic items but risks losing relevant information, and it doesn't specifically target toxicity. It's an indirect, imprecise fix.
* Databricks Reference: Context reduction is for efficiency, not safety:"Adjust context size based on performance needs"("Databricks Generative AI Engineer Guide").
* Option D: Log all LLM system responses and perform a batch toxicity analysis monthly
* Logging and analyzing responses is reactive, identifying toxicity after it occurs rather than preventing it. Monthly analysis doesn't limit real-time toxic outputs.
* Databricks Reference: Monitoring is for auditing, not prevention:"Log outputs for post-hoc analysis, but use input filters for safety"("Building LLM-Powered Applications").
Conclusion: Option A is the most effective guardrail, proactively filtering toxic inputs from unverified sources, which aligns with Databricks' emphasis on data quality as a primary safety mechanism for LLM systems.


NEW QUESTION # 21
Which indicator should be considered to evaluate the safety of the LLM outputs when qualitatively assessing LLM responses for a translation use case?

  • A. The similarity to the previous language
  • B. The ability to generate responses in code
  • C. The accuracy and relevance of the responses
  • D. The latency of the response and the length of text generated

Answer: C

Explanation:
* Problem Context: When assessing the safety and effectiveness of LLM outputs in a translation use case, it is essential to ensure that the translations accurately and relevantly convey the intended message. The evaluation should focus on how well the LLM understands and processes different languages and contexts.
* Explanation of Options:
* Option A: The ability to generate responses in code- This is not relevant to translation quality or safety.
* Option B: The similarity to the previous language- While ensuring that translations preserve the original's intent is important, this doesn't directly address the overall quality or safety of the translation.
* Option C: The latency of the response and the length of text generated- These operational metrics are less critical in assessing the qualitative aspects of translation safety.
* Option D: The accuracy and relevance of the responses- This is crucial in translation to ensure that the translated content is true to the original in meaning and appropriateness. Accuracy and relevance directly impact the effectiveness and safety of translations, especially in sensitive or nuanced contexts.
Thus,Option Dis the most important indicator when evaluating the safety of LLM outputs in translation, focusing on the core aspects that determine the utility and trustworthiness of translated content.


NEW QUESTION # 22
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?

  • A. Assess the quality of the retrieved context
  • B. Use a different LLM to improve the generated response
  • C. Use a different semantic similarity search algorithm
  • D. Implement caching for frequently asked questions

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 # 23
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