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Databricks Databricks-Generative-AI-Engineer-Associate Exam | Databricks-Generative-AI-Engineer-Associate Certification - 365 Days Free Updates of Databricks-Generative-AI-Engineer-Associate Exam Fee

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

Topic
Details

Topic 1

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 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

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 4

Application Development: In this topic, Generative AI Engineers learn about tools needed to extract data, Langchain
similar tools, and assessing responses to identify common issues. Moreover, the topic includes questions about adjusting an LLM's response, LLM guardrails, and the best LLM based on the attributes of the application.

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Databricks Certified Generative AI Engineer Associate Sample Questions (Q14-Q19):

NEW QUESTION # 14
A Generative AI Engineer has been asked to build an LLM-based question-answering application. The application should take into account new documents that are frequently published. The engineer wants to build this application with the least cost and least development effort and have it operate at the lowest cost possible.
Which combination of chaining components and configuration meets these requirements?

A. For the application a prompt, an agent and a fine-tuned LLM are required. The agent is used by the LLM to retrieve relevant content that is inserted into the prompt which is given to the LLM to generate answers.
B. The LLM needs to be frequently with the new documents in order to provide most up-to-date answers.
C. For the application a prompt, a retriever, and an LLM are required. The retriever output is inserted into the prompt which is given to the LLM to generate answers.
D. For the question-answering application, prompt engineering and an LLM are required to generate answers.

Answer: C

Explanation:
Problem Context: The task is to build an LLM-based question-answering application that integrates new documents frequently with minimal costs and development efforts.
Explanation of Options:
* Option A: Utilizes a prompt and a retriever, with the retriever output being fed into the LLM. This setup is efficient because it dynamically updates the data pool via the retriever, allowing the LLM to provide up-to-date answers based on the latest documents without needing tofrequently retrain the model. This method offers a balance of cost-effectiveness and functionality.
* Option B: Requires frequent retraining of the LLM, which is costly and labor-intensive.
* Option C: Only involves prompt engineering and an LLM, which may not adequately handle the requirement for incorporating new documents unless it's part of an ongoing retraining or updating mechanism, which would increase costs.
* Option D: Involves an agent and a fine-tuned LLM, which could be overkill and lead to higher development and operational costs.
Option Ais the most suitable as it provides a cost-effective, minimal development approach while ensuring the application remains up-to-date with new information.

NEW QUESTION # 15
A Generative Al Engineer is deciding between using LSH (Locality Sensitive Hashing) and HNSW (Hierarchical Navigable Small World) for indexing their vector database Their top priority is semantic accuracy Which approach should the Generative Al Engineer use to evaluate these two techniques?

A. Compare the cosine similarities of the embeddings of returned results against those of a representative sample of test inputs
B. Compare the Levenshtein distances of returned results against a representative sample of test inputs
C. Compare the Bilingual Evaluation Understudy (BLEU) scores of returned results for a representative sample of test inputs
D. Compare the Recall-Onented-Understudy for Gistmg Evaluation (ROUGE) scores of returned results for a representative sample of test inputs

Answer: A

Explanation:
The task is to choose between LSH and HNSW for a vector database index, prioritizing semantic accuracy.
The evaluation must assess how well each method retrieves semantically relevant results. Let's evaluate the options.
* Option A: Compare the cosine similarities of the embeddings of returned results against those of a representative sample of test inputs
* Cosine similarity measures semantic closeness between vectors, directly assessing retrieval accuracy in a vector database. Comparing returned results' embeddings to test inputs' embeddings evaluates how well LSH or HNSW preserves semantic relationships, aligning with the priority.
* Databricks Reference:"Cosine similarity is a standard metric for evaluating vector search accuracy"("Databricks Vector Search Documentation," 2023).
* Option B: Compare the Bilingual Evaluation Understudy (BLEU) scores of returned results for a representative sample of test inputs
* BLEU evaluates text generation (e.g., translations), not vector retrieval accuracy. It's irrelevant for indexing performance.
* Databricks Reference:"BLEU applies to generative tasks, not retrieval"("Generative AI Cookbook").
* Option C: Compare the Recall-Oriented-Understudy for Gisting Evaluation (ROUGE) scores of returned results for a representative sample of test inputs
* ROUGE is for summarization evaluation, not vector search. It doesn't measure semantic accuracy in retrieval.
* Databricks Reference:"ROUGE is unsuited for vector database evaluation"("Building LLM Applications with Databricks").
* Option D: Compare the Levenshtein distances of returned results against a representative sample of test inputs
* Levenshtein distance measures string edit distance, not semantic similarity in embeddings. It's inappropriate for vector-based retrieval.
* Databricks Reference: No specific support for Levenshtein in vector search contexts.
Conclusion: Option A (cosine similarity) is the correct approach, directly evaluating semantic accuracy in vector retrieval, as recommended by Databricks for Vector Search assessments.

NEW QUESTION # 16
A team wants to serve a code generation model as an assistant for their software developers. It should support multiple programming languages. Quality is the primary objective.
Which of the Databricks Foundation Model APIs, or models available in the Marketplace, would be the best fit?

A. BGE-large
B. MPT-7b
C. Llama2-70b
D. CodeLlama-34B

Answer: D

Explanation:
For a code generation model that supports multiple programming languages and where quality is the primary objective,CodeLlama-34Bis the most suitable choice. Here's the reasoning:
* Specialization in Code Generation:CodeLlama-34B is specifically designed for code generation tasks.
This model has been trained with a focus on understanding and generating code, which makes it particularly adept at handling various programming languages and coding contexts.
* Capacity and Performance:The "34B" indicates a model size of 34 billion parameters, suggesting a high capacity for handling complex tasks and generating high-quality outputs. The large model size typically correlates with better understanding and generation capabilities in diverse scenarios.
* Suitability for Development Teams:Given that the model is optimized for code, it will be able to assist software developers more effectively than general-purpose models. It understands coding syntax, semantics, and the nuances of different programming languages.
* Why Other Options Are Less Suitable:
* A (Llama2-70b): While also a large model, it's more general-purpose and may not be as fine- tuned for code generation as CodeLlama.
* B (BGE-large): This model may not specifically focus on code generation.
* C (MPT-7b): Smaller than CodeLlama-34B and likely less capable in handling complex code generation tasks at high quality.
Therefore, for a high-quality, multi-language code generation application,CodeLlama-34B(option D) is the best fit.

NEW QUESTION # 17
A Generative Al Engineer is setting up a Databricks Vector Search that will lookup news articles by topic within 10 days of the date specified An example query might be "Tell me about monster truck news around January 5th 1992". They want to do this with the least amount of effort.
How can they set up their Vector Search index to support this use case?

A. pass the query directly to the vector search index and return the best articles.
B. Include metadata columns for article date and topic to support metadata filtering.
C. Split articles by 10 day blocks and return the block closest to the query.
D. Create separate indexes by topic and add a classifier model to appropriately pick the best index.

Answer: B

Explanation:
The task is to set up a Databricks Vector Search index for news articles, supporting queries like "monster truck news around January 5th, 1992," with minimal effort. The index must filter by topic and a 10-day date range. Let's evaluate the options.
* Option A: Split articles by 10-day blocks and return the block closest to the query
* Pre-splitting articles into 10-day blocks requires significant preprocessing and index management (e.g., one index per block). It's effort-intensive and inflexible for dynamic date ranges.
* Databricks Reference:"Static partitioning increases setup complexity; metadata filtering is preferred"("Databricks Vector Search Documentation").
* Option B: Include metadata columns for article date and topic to support metadata filtering
* Adding date and topic as metadata in the Vector Search index allows dynamic filtering (e.g., date
± 5 days, topic = "monster truck") at query time. This leverages Databricks' built-in metadata filtering, minimizing setup effort.
* Databricks Reference:"Vector Search supports metadata filtering on columns like date or category for precise retrieval with minimal preprocessing"("Vector Search Guide," 2023).
* Option C: Pass the query directly to the vector search index and return the best articles
* Passing the full query (e.g., "Tell me about monster truck news around January 5th, 1992") to Vector Search relies solely on embeddings, ignoring structured filtering for date and topic. This risks inaccurate results without explicit range logic.
* Databricks Reference:"Pure vector similarity may not handle temporal or categorical constraints effectively"("Building LLM Applications with Databricks").
* Option D: Create separate indexes by topic and add a classifier model to appropriately pick the best index
* Separate indexes per topic plus a classifier model adds significant complexity (index creation, model training, maintenance), far exceeding "least effort." It's overkill for this use case.
* Databricks Reference:"Multiple indexes increase overhead; single-index with metadata is simpler"("Databricks Vector Search Documentation").
Conclusion: Option B is the simplest and most effective solution, using metadata filtering in a single Vector Search index to handle date ranges and topics, aligning with Databricks' emphasis on efficient, low-effort setups.

NEW QUESTION # 18
A Generative AI Engineer is designing an LLM-powered live sports commentary platform. The platform provides real-time updates and LLM-generated analyses for any users who would like to have live summaries, rather than reading a series of potentially outdated news articles.
Which tool below will give the platform access to real-time data for generating game analyses based on the latest game scores?

A. Feature Serving
B. AutoML
C. Foundation Model APIs
D. DatabrickslQ

Answer: A

Explanation:
* Problem Context: The engineer is developing an LLM-powered live sports commentary platform that needs to provide real-time updates and analyses based on the latest game scores. The critical requirement here is the capability to access and integrate real-time data efficiently with the platform for immediate analysis and reporting.
* Explanation of Options:
* Option A: DatabricksIQ: While DatabricksIQ offers integration and data processing capabilities, it is more aligned with data analytics rather than real-time feature serving, which is crucial for immediate updates necessary in a live sports commentary context.
* Option B: Foundation Model APIs: These APIs facilitate interactions with pre-trained models and could be part of the solution, but on their own, they do not provide mechanisms to access real- time game scores.
* Option C: Feature Serving: This is the correct answer as feature serving specifically refers to the real-time provision of data (features) to models for prediction. This would be essential for an LLM that generates analyses based on live game data, ensuring that the commentary is current and based on the latest events in the sport.
* Option D: AutoML: This tool automates the process of applying machine learning models to real-world problems, but it does not directly provide real-time data access, which is a critical requirement for the platform.
Thus,Option C(Feature Serving) is the most suitable tool for the platform as it directly supports the real-time data needs of an LLM-powered sports commentary system, ensuring that the analyses and updates are based on the latest available information.

NEW QUESTION # 19
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