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Free PDF 2025 Databricks High-quality Databricks-Generative-AI-Engineer-Associate: Databricks Certified Generative AI Engineer Associate Authorized Exam Dumps

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

Topic
Details

Topic 1

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 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

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 4

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

Topic 5

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 (Q11-Q16):

NEW QUESTION # 11
A Generative Al Engineer is building a system which will answer questions on latest stock news articles.
Which will NOT help with ensuring the outputs are relevant to financial news?

A. Increase the compute to improve processing speed of questions to allow greater relevancy analysis C Implement a profanity filter to screen out offensive language
B. Incorporate manual reviews to correct any problematic outputs prior to sending to the users
C. Implement a comprehensive guardrail framework that includes policies for content filters tailored to the finance sector.

Answer: A

Explanation:
In the context of ensuring that outputs are relevant to financial news, increasing compute power (option B) does not directly improve therelevanceof the LLM-generated outputs. Here's why:
* Compute Power and Relevancy:Increasing compute power can help the model process inputs faster, but it does not inherentlyimprove therelevanceof the answers. Relevancy depends on the data sources, the retrieval method, and the filtering mechanisms in place, not on how quickly the model processes the query.
* What Actually Helps with Relevance:Other methods, like content filtering, guardrails, or manual review, can directly impact the relevance of the model's responses by ensuring the model focuses on pertinent financial content. These methods help tailor the LLM's responses to the financial domain and avoid irrelevant or harmful outputs.
* Why Other Options Are More Relevant:
* A (Comprehensive Guardrail Framework): This will ensure that the model avoids generating content that is irrelevant or inappropriate in the finance sector.
* C (Profanity Filter): While not directly related to financial relevancy, ensuring the output is clean and professional is still important in maintaining the quality of responses.
* D (Manual Review): Incorporating human oversight to catch and correct issues with the LLM's output ensures the final answers are aligned with financial content expectations.
Thus, increasing compute power does not help with ensuring the outputs are more relevant to financial news, making option B the correct answer.

NEW QUESTION # 12
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. Reduce the amount of context Items the system will Include in consideration for its response.
B. Use only approved social media and news accounts to prevent unexpected toxic data from getting to the LLM.
C. Implement rate limiting
D. Log all LLM system responses and perform a batch toxicity analysis monthly.

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 # 13
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. Include metadata columns for article date and topic to support metadata filtering.
B. Split articles by 10 day blocks and return the block closest to the query.
C. pass the query directly to the vector search index and return the best articles.
D. Create separate indexes by topic and add a classifier model to appropriately pick the best index.

Answer: A

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 # 14
A Generative AI Engineer has created a RAG application which can help employees retrieve answers from an internal knowledge base, such as Confluence pages or Google Drive. The prototype application is now working with some positive feedback from internal company testers. Now the Generative Al Engineer wants to formally evaluate the system's performance and understand where to focus their efforts to further improve the system.
How should the Generative AI Engineer evaluate the system?

A. Curate a dataset that can test the retrieval and generation components of the system separately. Use MLflow's built in evaluation metrics to perform the evaluation on the retrieval and generation components.
B. Use an LLM-as-a-judge to evaluate the quality of the final answers generated.
C. Use cosine similarity score to comprehensively evaluate the quality of the final generated answers.
D. Benchmark multiple LLMs with the same data and pick the best LLM for the job.

Answer: A

Explanation:
* Problem Context: After receiving positive feedback for the RAG application prototype, the next step is to formally evaluate the system to pinpoint areas for improvement.
* Explanation of Options:
* Option A: While cosine similarity scores are useful, they primarily measure similarity rather than the overall performance of an RAG system.
* Option B: This option provides a systematic approach to evaluation by testing both retrieval and generation components separately. This allows for targeted improvements and a clear understanding of each component's performance, using MLflow's metrics for a structured and standardized assessment.
* Option C: Benchmarking multiple LLMs does not focus on evaluating the existing system's components but rather on comparing different models.
* Option D: Using an LLM as a judge is subjective and less reliable for systematic performance evaluation.
OptionBis the most comprehensive and structured approach, facilitating precise evaluations and improvements on specific components of the RAG system.

NEW QUESTION # 15
A Generative Al Engineer would like an LLM to generate formatted JSON from emails. This will require parsing and extracting the following information: order ID, date, and sender email. Here's a sample email:

They will need to write a prompt that will extract the relevant information in JSON format with the highest level of output accuracy.
Which prompt will do that?

A. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in JSON format.
Here's an example: {"date": "April 16, 2024", "sender_email": "sarah.lee925@gmail.com", "order_id":
"RE987D"}
B. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in a human-readable format.
C. You will receive customer emails and need to extract date, sender email, and order ID. You should return the date, sender email, and order ID information in JSON format.
D. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in JSON format.

Answer: A

Explanation:
Problem Context: The goal is to parse emails to extract certain pieces of information and output this in a structured JSON format. Clarity and specificity in the prompt design will ensure higher accuracy in the LLM' s responses.
Explanation of Options:
* Option A: Provides a general guideline but lacks an example, which helps an LLM understand the exact format expected.
* Option B: Includes a clear instruction and a specific example of the output format. Providing an example is crucial as it helps set the pattern and format in which the information should be structured, leading to more accurate results.
* Option C: Does not specify that the output should be in JSON format, thus not meeting the requirement.
* Option D: While it correctly asks for JSON format, it lacks an example that would guide the LLM on how to structure the JSON correctly.
Therefore,Option Bis optimal as it not only specifies the required format but also illustrates it with an example, enhancing the likelihood of accurate extraction and formatting by the LLM.

NEW QUESTION # 16
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