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

Topic	Details
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	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 4	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.

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

NEW QUESTION # 18
A Generative Al Engineer is tasked with developing an application that is based on an open source large language model (LLM). They need a foundation LLM with a large context window.
Which model fits this need?

A. MPT-30B
B. Llama2-70B
C. DBRX
D. DistilBERT

Answer: B

Explanation:
* Problem Context: The engineer needs an open-source LLM with a large context window to develop an application.
* Explanation of Options:
* Option A: DistilBERT: While an efficient and smaller version of BERT, DistilBERT does not provide a particularly large context window.
* Option B: MPT-30B: This model, while large, is not specified as being particularly notable for its context window capabilities.
* Option C: Llama2-70B: Known for its large model size and extensive capabilities, including a large context window. It is also available as an open-source model, making it ideal for applications requiring extensive contextual understanding.
* Option D: DBRX: This is not a recognized standard model in the context of large language models with extensive context windows.
Thus,Option C(Llama2-70B) is the best fit as it meets the criteria of having a large context window and being available for open-source use, suitable for developing robust language understanding applications.

NEW QUESTION # 19
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 a human-readable format.
B. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in JSON 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.
Here's an example: {"date": "April 16, 2024", "sender_email": "sarah.lee925@gmail.com", "order_id": "RE987D"}

Answer: D

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 B is 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 # 20
After changing the response generating LLM in a RAG pipeline from GPT-4 to a model with a shorter context length that the company self-hosts, the Generative AI Engineer is getting the following error:

What TWO solutions should the Generative AI Engineer implement without changing the response generating model? (Choose two.)

A. Retrain the response generating model using ALiBi
B. Decrease the chunk size of embedded documents
C. Reduce the number of records retrieved from the vector database
D. Reduce the maximum output tokens of the new model
E. Use a smaller embedding model to generate

Answer: B,C

Explanation:
* Problem Context: After switching to a model with a shorter context length, the error message indicating that the prompt token count has exceeded the limit suggests that the input to the model is too large.
* Explanation of Options:
* Option A: Use a smaller embedding model to generate- This wouldn't necessarily address the issue of prompt size exceeding the model's token limit.
* Option B: Reduce the maximum output tokens of the new model- This option affects the output length, not the size of the input being too large.
* Option C: Decrease the chunk size of embedded documents- This would help reduce the size of each document chunk fed into the model, ensuring that the input remains within the model's context length limitations.
* Option D: Reduce the number of records retrieved from the vector database- By retrieving fewer records, the total input size to the model can be managed more effectively, keeping it within the allowable token limits.
* Option E: Retrain the response generating model using ALiBi- Retraining the model is contrary to the stipulation not to change the response generating model.
OptionsCandDare the most effective solutions to manage the model's shorter context length without changing the model itself, by adjusting the input size both in terms of individual document size and total documents retrieved.

NEW QUESTION # 21
A Generative AI Engineer has deployed a RAG application to production. Its Vector Search index is built from a Delta table that receives incremental updates every hour from an upstream ETL pipeline. The team wants the index to automatically reflect source-table changes without manual re-indexing and without changing the endpoint used by downstream applications.
Which approach should the engineer use?

A. Schedule a job to rebuild the index every hour after the ETL pipeline finishes.
B. Update the pipeline mode to continuous mode rather than triggered mode.
C. Use a Delta Sync index so changes in the source Delta table are automatically synchronized to the index.
D. Replace the current index with a Direct Vector Access index and push updates through a custom streaming job.

Answer: C

Explanation:
D identifies the index type designed to synchronize with a source Delta table. Delta Sync supports incremental maintenance, avoiding a custom process that repeatedly rebuilds the complete index or manually pushes every changed vector. A Direct Vector Access index transfers update responsibility to the application.
However, this question has a configuration ambiguity: Delta Sync also has a synchronization mode.
Continuous mode processes arriving changes automatically, while triggered mode requires an explicit synchronization request, which can be scheduled after ETL. If the existing index is already a triggered Delta Sync index, B is the relevant configuration change. D is therefore the intended architectural answer, with continuous synchronization or an automated trigger needed to deliver the requested operational behavior.
Databricks API documentation

NEW QUESTION # 22
A Generative AI Engineer is managing prompt templates using MLflow v3.x for a document summarization pipeline. A regulatory audit requires the team to demonstrate exactly which prompt version was used to generate outputs on a specific date three months ago, including the exact prompt text and any variables used at that time.
Which combination of MLflow v3.x capabilities allows the engineer to satisfy this audit requirement?

A. MLflow Prompt Registry version history and logged runs that reference the prompt name and version used during inference.
B. MLflow autologging and Delta Lake time travel on the inference table.
C. MLflow Model Registry webhooks and a downstream audit log stored in an external database.
D. MLflow experiment tags and an automatically scripted changelog stored in a Databricks notebook.

Answer: A

Explanation:
The Prompt Registry preserves identifiable prompt versions, allowing the team to recover the exact template associated with a historical execution. Linking inference activity to the prompt's name and version establishes which registered template the application actually used. MLflow also supports lineage between prompt versions, application versions, and execution traces. However, satisfying the entire audit requires retaining the actual runtime variable values or the fully rendered prompt in the relevant execution records. Registry history alone preserves the template, not every value substituted into it. Therefore, D is the best option, provided the logging captures those inputs. Model webhooks, experiment tags, or table history alone do not provide the same explicit prompt-version lineage and complete historical reconstruction. Databricks documentation

NEW QUESTION # 23
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