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Databricks Databricks-Generative-AI-Engineer-Associate Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Generative AI Engineer Associate
Exam Number:	Databricks-Generative-AI-Engineer-Associate
Real Exam Qty:	45-60
Exam Format:	Multiple select, Multiple choice
Related Certifications:	Databricks Certified Data Engineer Associate Databricks Certified Machine Learning Associate
Certificate Validity Period:	2 years
Exam Duration:	90 minutes
Available Languages:	English
Exam Price:	USD 200
Recommended Training:	Mosaic AI Documentation Databricks Academy - Generative AI Courses
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

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

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

Databricks Certified Generative AI Engineer Associate Sample Questions (Q87-Q92):

NEW QUESTION # 87
A Generative Al Engineer has successfully ingested unstructured documents and chunked them by document sections. They would like to store the chunks in a Vector Search index. The current format of the dataframe has two columns: (i) original document file name (ii) an array of text chunks for each document.
What is the most performant way to store this dataframe?

A. Split the data into train and test set, create a unique identifier for each document, then save to a Delta table
B. Flatten the dataframe to one chunk per row, create a unique identifier for each row, and save to a Delta table
C. First create a unique identifier for each document, then save to a Delta table
D. Store each chunk as an independent JSON file in Unity Catalog Volume. For each JSON file, the key is the document section name and the value is the array of text chunks for that section

Answer: B

Explanation:
* Problem Context: The engineer needs an efficient way to store chunks of unstructured documents to facilitate easy retrieval and search. The current dataframe consists of document filenames and associated text chunks.
* Explanation of Options:
* Option A: Splitting into train and test sets is more relevant for model training scenarios and not directly applicable to storage for retrieval in a Vector Search index.
* Option B: Flattening the dataframe such that each row contains a single chunk with a unique identifier is the most performant for storage and retrieval. This structure aligns well with how data is indexed and queried in vector search applications, making it easier to retrieve specific chunks efficiently.
* Option C: Creating a unique identifier for each document only does not address the need to access individual chunks efficiently, which is critical in a Vector Search application.
* Option D: Storing each chunk as an independent JSON file creates unnecessary overhead and complexity in managing and querying large volumes of files.
OptionBis the most efficient and practical approach, allowing for streamlined indexing and retrieval processes in a Delta table environment, fitting the requirements of a Vector Search index.

NEW QUESTION # 88
A Generative Al Engineer has built an LLM-based system that will automatically translate user text between two languages. They now want to benchmark multiple LLM's on this task and pick the best one. They have an evaluation set with known high quality translation examples. They want to evaluate each LLM using the evaluation set with a performant metric.
Which metric should they choose for this evaluation?

A. ROUGE metric
B. NDCG metric
C. RECALL metric
D. BLEU metric

Answer: D

Explanation:
The task is to benchmark LLMs for text translation using an evaluation set with known high-quality examples, requiring a performant metric. Let's evaluate the options.
Option A: ROUGE metric
ROUGE (Recall-Oriented Understudy for Gisting Evaluation) measures overlap between generated and reference texts, primarily for summarization. It's less suited for translation, where precision and word order matter more.
Databricks Reference: "ROUGE is commonly used for summarization, not translation evaluation" ("Generative AI Cookbook," 2023).
Option B: BLEU metric
BLEU (Bilingual Evaluation Understudy) evaluates translation quality by comparing n-gram overlap with reference translations, accounting for precision and brevity. It's widely used, performant, and appropriate for this task.
Databricks Reference: "BLEU is a standard metric for evaluating machine translation, balancing accuracy and efficiency" ("Building LLM Applications with Databricks").
Option C: NDCG metric
NDCG (Normalized Discounted Cumulative Gain) assesses ranking quality, not text generation. It's irrelevant for translation evaluation.
Databricks Reference: "NDCG is suited for ranking tasks, not generative output scoring" ("Databricks Generative AI Engineer Guide").
Option D: RECALL metric
Recall measures retrieved relevant items but doesn't evaluate translation quality (e.g., fluency, correctness). It's incomplete for this use case.
Databricks Reference: No specific extract, but recall alone lacks the granularity of BLEU for text generation tasks.
Conclusion: Option B (BLEU) is the best metric for translation evaluation, offering a performant and standard approach, as endorsed by Databricks' guidance on generative tasks.

NEW QUESTION # 89
A Generative AI Engineer is deploying an agent using Mosaic AI Model Serving. The agent needs to access various Databricks resources, including Vector Search, Databricks SQL, and Functions. They need to find the easiest and best-practice way to authenticate the deployed agent to access these resources.
What approach should they choose?

A. Embed authentication credentials within the agent's code to access the required resources.
B. Define resource dependencies while logging the agent and deploy it with the Agent Framework.
C. Set appropriate permissions on the Model Serving endpoint for the agent, as these permissions will be used when connecting to other resources.
D. Log the authentication token while logging the agent; this token will be automatically used for authentication.

Answer: B

NEW QUESTION # 90
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. Create separate indexes by topic and add a classifier model to appropriately pick the best index.
C. Split articles by 10 day blocks and return the block closest to the query.
D. Include metadata columns for article date and topic to support metadata filtering.

Answer: D

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 # 91
A Generative AI Engineer is testing a simple prompt template in LangChain using the code below, but is getting an error.

Assuming the API key was properly defined, what change does the Generative AI Engineer need to make to fix their chain?

Answer: A

Explanation:
To fix the error in the LangChain code provided for using a simple prompt template, the correct approach is Option C. Here ' s a detailed breakdown of why Option C is the right choice and how it addresses the issue:
* Proper Initialization : In Option C, the LLMChain is correctly initialized with the LLM instance specified as OpenAI(), which likely represents a language model (like GPT) from OpenAI. This is crucial as it specifies which model to use for generating responses.
* Correct Use of Classes and Methods :
* The PromptTemplate is defined with the correct format, specifying that adjective is a variable within the template. This allows dynamic insertion of values into the template when generating text.
* The prompt variable is properly linked with the PromptTemplate, and the final template string is passed correctly.
* The LLMChain correctly references the prompt and the initialized OpenAI() instance, ensuring that the template and the model are properly linked for generating output.
Why Other Options Are Incorrect:
* Option A : Misuses the parameter passing in generate method by incorrectly structuring the dictionary.
* Option B : Incorrectly uses prompt.format method which does not exist in the context of LLMChain and PromptTemplate configuration, resulting in potential errors.
* Option D : Incorrect order and setup in the initialization parameters for LLMChain, which would likely lead to a failure in recognizing the correct configuration for prompt and LLM usage.
Thus, Option C is correct because it ensures that the LangChain components are correctly set up and integrated, adhering to proper syntax and logical flow required by LangChain ' s architecture. This setup avoids common pitfalls such as type errors or method misuses, which are evident in other options.

NEW QUESTION # 92
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