Databricks-Generative-AI-Engineer-Associate Passguide & New Databricks-Generative-AI-Engineer-Associate Test Questions

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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	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 3	Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal licensing requirements in this topic.
Topic 4	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 5	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 (Q78-Q83):

NEW QUESTION # 78
A Generative AI Engineer developed an LLM application using the provisioned throughput Foundation Model API. Now that the application is ready to be deployed, they realize their volume of requests are not sufficiently high enough to create their own provisioned throughput endpoint. They want to choose a strategy that ensures the best cost-effectiveness for their application.
What strategy should the Generative AI Engineer use?

A. Deploy the model using pay-per-token throughput as it comes with cost guarantees
B. Change to a model with a fewer number of parameters in order to reduce hardware constraint issues
C. Throttle the incoming batch of requests manually to avoid rate limiting issues
D. Switch to using External Models instead

Answer: A

Explanation:
* Problem Context: The engineer needs a cost-effective deployment strategy for an LLM application with relatively low request volume.
* Explanation of Options:
* Option A: Switching to external models may not provide the required control or integration necessary for specific application needs.
* Option B: Using a pay-per-token model is cost-effective, especially for applications with variable or low request volumes, as it aligns costs directly with usage.
* Option C: Changing to a model with fewer parameters could reduce costs, but might also impact the performance and capabilities of the application.
* Option D: Manually throttling requests is a less efficient and potentially error-prone strategy for managing costs.
OptionBis ideal, offering flexibility and cost control, aligning expenses directly with the application's usage patterns.

NEW QUESTION # 79
A generative AI engineer is deploying an AI agent authored with MLflow's ChatAgent interface for a retail company's customer support system on Databricks. The agent must handle thousands of inquiries daily, and the engineer needs to track its performance and quality in real-time to ensure it meets service-level agreements. Which metrics are automatically captured by default and made available for monitoring when the agent is deployed using the Mosaic AI Agent Framework?

A. No metrics are automatically captured
B. Operational metrics like request volume, latency, and errors
C. Both operational and quality metrics
D. Quality metrics like correctness and guideline adherence

Answer: B

Explanation:
When deploying an agent via the Mosaic AI Agent Framework (which leverages Databricks Model Serving), operational metrics are captured automatically by default. These include system-level telemetry such as the number of requests per second (volume), the time taken for the model to respond (latency), and the rate of 4xx/5xx HTTP errors. These are essential for monitoring Service Level Agreements (SLAs). However, Quality metrics (B), such as correctness, groundedness, or adherence to custom guidelines, cannot be determined "automatically" by the serving infrastructure because they require either human feedback or an LLM-as-a-judge evaluation (using Databricks Agent Evaluation). While Databricks makes it easy to generate quality metrics using the mlflow.evaluate API or the inference table, they are not "default operational metrics" that appear without additional evaluation configuration.

NEW QUESTION # 80
What is the most suitable library for building a multi-step LLM-based workflow?

A. PySpark
B. LangChain
C. Pandas
D. TensorFlow

Answer: B

Explanation:
* Problem Context: The Generative AI Engineer needs a tool to build amulti-step LLM-based workflow. This type of workflow often involves chaining multiple steps together, such as query generation, retrieval of information, response generation, and post-processing, with LLMs integrated at several points.
* Explanation of Options:
* Option A: Pandas: Pandas is a powerful data manipulation library for structured data analysis, but it is not designed for managing or orchestrating multi-step workflows, especially those involving LLMs.
* Option B: TensorFlow: TensorFlow is primarily used for training and deploying machine learning models, especially deep learning models. It is not designed for orchestrating multi-step tasks in LLM-based workflows.
* Option C: PySpark: PySpark is a distributed computing framework used for large-scale data processing. While useful for handling big data, it is not specialized for chaining LLM-based operations.
* Option D: LangChain: LangChain is a purpose-built framework designed specifically for orchestrating multi-step workflowswith large language models (LLMs). It enables developers to easily chain different tasks, such as retrieving documents, summarizing information, and generating responses, all in a structured flow. This makes it the best tool for building complex LLM-based workflows.
Thus,LangChainis the most suitable library for creating multi-step LLM-based workflows.

NEW QUESTION # 81
When developing an LLM application, it's crucial to ensure that the data used for training the model complies with licensing requirements to avoid legal risks.
Which action is NOT appropriate to avoid legal risks?

A. Use any available data you personally created which is completely original and you can decide what license to use.
B. Reach out to the data curators directly after you have started using the trained model to let them know.
C. Only use data explicitly labeled with an open license and ensure the license terms are followed.
D. Reach out to the data curators directly before you have started using the trained model to let them know.

Answer: B

Explanation:
* Problem Context : When using data to train a model, it's essential to ensure compliance with licensing to avoid legal risks. Legal issues can arise from using data without permission, especially when it comes from third-party sources.
* Explanation of Options :
* Option A : Reaching out to data curators before using the data is an appropriate action. This allows you to ensure you have permission or understand the licensing terms before starting to use the data in your model.
* Option B : Using original data that you personally created is always a safe option. Since you have full ownership over the data, there are no legal risks, as you control the licensing.
* Option C : Using data that is explicitly labeled with an open license and adhering to the license terms is a correct and recommended approach. This ensures compliance with legal requirements.
* Option D : Reaching out to the data curators after you have already started using the trained model is not appropriate . If you've already used the data without understanding its licensing terms, you may have already violated the terms of use, which could lead to legal complications.
It's essential to clarify the licensing terms before using the data, not after.
Thus, Option D is not appropriate because it could expose you to legal risks by using the data without first obtaining the proper licensing permissions.

NEW QUESTION # 82
A Generative Al Engineer has already trained an LLM on Databricks and it is now ready to be deployed.
Which of the following steps correctly outlines the easiest process for deploying a model on Databricks?

A. Log the model as a pickle object, upload the object to Unity Catalog Volume, register it to Unity Catalog using MLflow, and start a serving endpoint
B. Save the model along with its dependencies in a local directory, build the Docker image, and run the Docker container
C. Wrap the LLM's prediction function into a Flask application and serve using Gunicorn
D. Log the model using MLflow during training, directly register the model to Unity Catalog using the MLflow API, and start a serving endpoint

Answer: D

Explanation:
* Problem Context: The goal is to deploy a trained LLM on Databricks in the simplest and most integrated manner.
* Explanation of Options:
* Option A: This method involves unnecessary steps like logging the model as a pickle object, which is not the most efficient path in a Databricks environment.
* Option B: Logging the model with MLflow during training and then using MLflow's API to register and start serving the model is straightforward and leverages Databricks' built-in functionalities for seamless model deployment.
* Option C: Building and running a Docker container is a complex and less integrated approach within the Databricks ecosystem.
* Option D: Using Flask and Gunicorn is a more manual approach and less integrated compared to the native capabilities of Databricks and MLflow.
OptionBprovides the most straightforward and efficient process, utilizing Databricks' ecosystem to its full advantage for deploying models.

NEW QUESTION # 83
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