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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	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 (Q61-Q66):

NEW QUESTION # 61
A Generative AI Engineer is tasked with deploying an application that takes advantage of a custom MLflow Pyfunc model to return some interim results.
How should they configure the endpoint to pass the secrets and credentials?

A. Pass variables using the Databricks Feature Store API
B. Add credentials using environment variables
C. Pass the secrets in plain text
D. Use spark.conf.set ()

Answer: B

Explanation:
Context: Deploying an application that uses an MLflow Pyfunc model involves managing sensitive information such as secrets and credentials securely.
Explanation of Options:
* Option A: Use spark.conf.set(): While this method can pass configurations within Spark jobs, using it for secrets is not recommended because it may expose them in logs or Spark UI.
* Option B: Pass variables using the Databricks Feature Store API: The Feature Store API is designed for managing features for machine learning, not for handling secrets or credentials.
* Option C: Add credentials using environment variables: This is a common practice for managing credentials in a secure manner, as environment variables can be accessed securely by applications without exposing them in the codebase.
* Option D: Pass the secrets in plain text: This is highly insecure and not recommended, as it exposes sensitive information directly in the code.
Therefore,Option Cis the best method for securely passing secrets and credentials to an application, protecting them from exposure.

NEW QUESTION # 62
A Generative AI Engineer just deployed an LLM application at a digital marketing company that assists with answering customer service inquiries.
Which metric should they monitor for their customer service LLM application in production?

A. Energy usage per query
B. Number of customer inquiries processed per unit of time
C. Final perplexity scores for the training of the model
D. HuggingFace Leaderboard values for the base LLM

Answer: B

Explanation:
When deploying an LLM application for customer service inquiries, the primary focus is on measuring the operational efficiency and quality of the responses. Here's whyAis the correct metric:
* Number of customer inquiries processed per unit of time: This metric tracks the throughput of the customer service system, reflecting how many customer inquiries the LLM application can handle in a given time period (e.g., per minute or hour). High throughput is crucial in customer service applications where quick response times are essential to user satisfaction and business efficiency.
* Real-time performance monitoring: Monitoring the number of queries processed is an important part of ensuring that the model is performing well under load, especially during peak traffic times. It also helps ensure the system scales properly to meet demand.
Why other options are not ideal:
* B. Energy usage per query: While energy efficiency is a consideration, it is not the primary concern for a customer-facing application where user experience (i.e., fast and accurate responses) is critical.
* C. Final perplexity scores for the training of the model: Perplexity is a metric for model training, but it doesn't reflect the real-time operational performance of an LLM in production.
* D. HuggingFace Leaderboard values for the base LLM: The HuggingFace Leaderboard is more relevant during model selection and benchmarking. However, it is not a direct measure of the model's performance in a specific customer service application in production.
Focusing on throughput (inquiries processed per unit time) ensures that the LLM application is meeting business needs for fast and efficient customer service responses.

NEW QUESTION # 63
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. You should return the date, sender email, and order ID information in JSON format.
C. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in a human-readable 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 # 64
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 using MLflow during training, directly register the model to Unity Catalog using the MLflow API, and start a serving endpoint
B. 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
C. Wrap the LLM's prediction function into a Flask application and serve using Gunicorn
D. Save the model along with its dependencies in a local directory, build the Docker image, and run the Docker container

Answer: A

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 # 65
A Generative Al Engineer is building a production-ready LLM system which replies directly to customers.
The solution makes use of the Foundation Model API via provisioned throughput. They are concerned that the LLM could potentially respond in a toxic or otherwise unsafe way. They also wish to perform this with the least amount of effort.
Which approach will do this?

A. Ask users to report unsafe responses
B. Host Llama Guard on Foundation Model API and use it to detect unsafe responses
C. Add a regex expression on inputs and outputs to detect unsafe responses.
D. Add some LLM calls to their chain to detect unsafe content before returning text

Answer: B

Explanation:
The task is to prevent toxic or unsafe responses in an LLM system using the Foundation Model API with minimal effort. Let's assess the options.
* Option A: Host Llama Guard on Foundation Model API and use it to detect unsafe responses
* Llama Guard is a safety-focused model designed to detect toxic or unsafe content. Hosting it via the Foundation Model API (a Databricks service) integrates seamlessly with the existing system, requiring minimal setup (just deployment and a check step), and leverages provisioned throughput for performance.
* Databricks Reference:"Foundation Model API supports hosting safety models like Llama Guard to filter outputs efficiently"("Foundation Model API Documentation," 2023).
* Option B: Add some LLM calls to their chain to detect unsafe content before returning text
* Using additional LLM calls (e.g., prompting an LLM to classify toxicity) increases latency, complexity, and effort (crafting prompts, chaining logic), and lacks the specificity of a dedicated safety model.
* Databricks Reference:"Ad-hoc LLM checks are less efficient than purpose-built safety solutions" ("Building LLM Applications with Databricks").
* Option C: Add a regex expression on inputs and outputs to detect unsafe responses
* Regex can catch simple patterns (e.g., profanity) but fails for nuanced toxicity (e.g., sarcasm, context-dependent harm), requiring significant manual effort to maintain and update rules.
* Databricks Reference:"Regex-based filtering is limited for complex safety needs"("Generative AI Cookbook").
* Option D: Ask users to report unsafe responses
* User reporting is reactive, not preventive, and places burden on users rather than the system. It doesn't limit unsafe outputs proactively and requires additional effort for feedback handling.
* Databricks Reference:"Proactive guardrails are preferred over user-driven monitoring" ("Databricks Generative AI Engineer Guide").
Conclusion: Option A (Llama Guard on Foundation Model API) is the least-effort, most effective approach, leveraging Databricks' infrastructure for seamless safety integration.

NEW QUESTION # 66
......

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Latest Databricks Databricks-Generative-AI-Engineer-Associate Labs - Databricks-Generative-AI-Engineer-Associate Free Download

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

Practice Databricks-Generative-AI-Engineer-Associate Online, Pdf Databricks-Generative-AI-Engineer-Associate Free

Databricks Certified Generative AI Engineer Associate Sample Questions (Q61-Q66):