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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Generative AI Engineer Associate Exam |
| Exam Number: | Databricks-Generative-AI-Engineer-Associate |
| Exam Price: | USD 200 |
| Exam Duration: | 90 minutes |
| Exam Format: | Multiple choice |
| Passing Score: | 700/1000 or 70% |
| Certificate Validity Period: | 2 years |
| Available Languages: | Brazilian Portuguese, Korean, Japanese, English |
| Real Exam Qty: | 45 |
| Recommended Training: | Generative AI Engineering with Databricks |
| Exam Registration: | Databricks Certification Registration |
| Sample Questions: | Databricks Databricks-Generative-AI-Engineer-Associate Sample Questions |
| Exam Way: | Online proctored or in-person test center |
| Pre Condition: | No formal prerequisites; recommended 6+ months hands-on experience building generative AI solutions |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/genai-engineer-associate |
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NEW QUESTION # 17
A Generative AI Engineer has been reviewing issues with their company ' s LLM-based question-answering assistant and has determined that a technique called prompt chaining could help alleviate some performance concerns. However, to suggest this to their team, they have to clearly explain how it works and how it can benefit their question-answering assistant. Which explanation do they communicate to the team?
Answer: C
Explanation:
Prompt chaining is a fundamental design pattern in LLM application development used to handle complexity.
Instead of sending a single, massive, and highly complex prompt to an LLM-which often results in reasoning errors or hallucinations-chaining breaks the logic into a sequence of smaller, targeted steps. For example, a legal assistant might first chain a step to " identify the legal jurisdiction, " followed by a step to " extract relevant statutes, " and finally a step to " summarize the findings. " This modularity improves reliability because each prompt has a narrower focus, making it easier for the model to follow instructions accurately. While it may actually increase latency (contradicting B) and cost (contradicting D) due to multiple API calls, the primary engineering benefit is the significant boost in the quality and robustness of the output.
It also allows for intermediate validation and error handling between steps, which is impossible in a single- call architecture.
NEW QUESTION # 18
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?
Answer: C
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 # 19
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: D
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 # 20
A Generative AI Engineer has been reviewing issues with their company's LLM-based question-answering assistant and has determined that a technique called prompt chaining could help alleviate some performance concerns. However, to suggest this to their team, they have to clearly explain how it works and how it can benefit their question-answering assistant. Which explanation do they communicate to the team?
Answer: C
Explanation:
Prompt chaining is a fundamental design pattern in LLM application development used to handle complexity. Instead of sending a single, massive, and highly complex prompt to an LLM-which often results in reasoning errors or hallucinations-chaining breaks the logic into a sequence of smaller, targeted steps. For example, a legal assistant might first chain a step to "identify the legal jurisdiction," followed by a step to "extract relevant statutes," and finally a step to "summarize the findings." This modularity improves reliability because each prompt has a narrower focus, making it easier for the model to follow instructions accurately. While it may actually increase latency (contradicting B) and cost (contradicting D) due to multiple API calls, the primary engineering benefit is the significant boost in the quality and robustness of the output. It also allows for intermediate validation and error handling between steps, which is impossible in a single-call architecture.
NEW QUESTION # 21
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: D
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 # 22
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Databricks-Generative-AI-Engineer-Associate Exam Duration: https://www.2pass4sure.com/Generative-AI-Engineer/Databricks-Generative-AI-Engineer-Associate-actual-exam-braindumps.html
P.S. Free 2026 Databricks Databricks-Generative-AI-Engineer-Associate dumps are available on Google Drive shared by 2Pass4sure: https://drive.google.com/open?id=14CyVZdHBTjtdsCKIwzFEtS5H14Rxg6lc