Question # 1 A Generative AI Engineer has been asked to build an LLM-based question-answering application. The application should take into account new documents that are frequently published. The engineer wants to build this application with the least cost and least development effort and have it operate at the lowest cost possible. Which combination of chaining components and configuration meets these requirements? A. For the application a prompt, a retriever, and an LLM are required. The retriever output is inserted into the prompt which is given to the LLM to generate answers.B. The LLM needs to be frequently with the new documents in order to provide most up-to-date answers.C. For the question-answering application, prompt engineering and an LLM are required to generate answers.D. For the application a prompt, an agent and a fine-tuned LLM are required. The agent is used by the LLM to retrieve relevant content that is inserted into the prompt which is given to the LLM to generate answers.
Click for Answer
A. For the application a prompt, a retriever, and an LLM are required. The retriever output is inserted into the prompt which is given to the LLM to generate answers.
Answer Description Explanation: Explanation of Options:
Option A: Utilizes a prompt and a retriever, with the retriever output being fed into the LLM. This setup is efficient because it dynamically updates the data pool via the retriever, allowing the LLM to provide up-to-date answers based on the latest documents without needing tofrequently retrain the model. This method offers a balance of cost-effectiveness and functionality.
Option B: Requires frequent retraining of the LLM, which is costly and labor-intensive.
Option C: Only involves prompt engineering and an LLM, which may not adequately handle the requirement for incorporating new documents unless it’s part of an ongoing retraining or updating mechanism, which would increase costs.
Option D: Involves an agent and a fine-tuned LLM, which could be overkill and lead to higher development and operational costs.
Question # 2 A Generative AI Engineer received the following business requirements for an external chatbot.
The chatbot needs to know what types of questions the user asks and routes to appropriate models to answer the questions. For example, the user might ask about upcoming event details. Another user might ask about purchasing tickets for a particular event. What is an ideal workflow for such a chatbot? A. The chatbot should only look at previous event informationB. There should be two different chatbots handling different types of user queries.C. The chatbot should be implemented as a multi-step LLM workflow. First, identify the type of question asked, then route the question to the appropriate model. If it’s an upcoming event question, send the query to a text-to-SQL model. If it’s about ticket purchasing, the customer should be redirected to a payment platform.D. The chatbot should only process payments
Click for Answer
C. The chatbot should be implemented as a multi-step LLM workflow. First, identify the type of question asked, then route the question to the appropriate model. If it’s an upcoming event question, send the query to a text-to-SQL model. If it’s about ticket purchasing, the customer should be redirected to a payment platform.
Answer Description Explanation:
Explanation of Options:
Question # 3 A Generative AI Engineer has a provisioned throughput model serving endpoint as part of a RAG application and would like to monitor the serving endpoint’s incoming requests and outgoing responses. The current approach is to include a micro-service in between the endpoint and the user interface to write logs to a remote server. Which Databricks feature should they use instead which will perform the same task? A. Vector SearchB. LakeviewC. DBSQLD. Inference Tables
Click for Answer
D. Inference Tables
Answer Description Explanation: Problem Context: The goal is to monitor theserving endpointfor incoming requests and outgoing responses in aprovisioned throughput model serving endpointwithin aRetrieval-Augmented Generation (RAG) application. The current approach involves using a microservice to log requests and responses to a remote server, but the Generative AI Engineer is looking for a more streamlined solution within Databricks.
Explanation of Options:
Question # 4 A Generative Al Engineer has developed an LLM application to answer questions about internal company policies. The Generative AI Engineer must ensure that the application doesn’t hallucinate or leak confidential data. Which approach should NOT be used to mitigate hallucination or confidential data leakage? A. Add guardrails to filter outputs from the LLM before it is shown to the userB. Fine-tune the model on your data, hoping it will learn what is appropriate and notC. Limit the data available based on the user’s access levelD. Use a strong system prompt to ensure the model aligns with your needs.
Click for Answer
B. Fine-tune the model on your data, hoping it will learn what is appropriate and not
Answer Description Explanation:
Risk of Data Leakage: Fine-tuning on sensitive or confidential data does not guarantee that the model will not inadvertently include or reference this data in its outputs. There’s a risk of overfitting to the specific data details, which might lead to unintended leakage.
Hallucination: Fine-tuning does not necessarily mitigate the model's tendency to hallucinate; in fact, it might exacerbate it if the training data is not comprehensive or representative of all potential queries.
Better Approaches:
Question # 5 What is an effective method to preprocess prompts using custom code before sending them to an LLM? A. Directly modify the LLM’s internal architecture to include preprocessing stepsB. It is better not to introduce custom code to preprocess prompts as the LLM has not been trained with examples of the preprocessed promptsC. Rather than preprocessing prompts, it’s more effective to postprocess the LLM outputs to align the outputs to desired outcomesD. Write a MLflow PyFunc model that has a separate function to process the prompts
Click for Answer
D. Write a MLflow PyFunc model that has a separate function to process the prompts
Answer Description Explanation:
MLflow PyFunc Models: MLflow is a widely used platform for managing the machine learning lifecycle, including experimentation, reproducibility, and deployment. APyFuncmodel is a generic Python function model that can implement custom logic, which includes preprocessing prompts.
Preprocessing Prompts: Preprocessing could include various tasks like cleaning up the user input, formatting it according to specific rules, or augmenting it with additional context before passing it to the LLM. Writing this preprocessing as part of a PyFunc model allows the custom code to be managed, tested, and deployed easily.
Modular and Reusable: By separating the preprocessing logic into a PyFunc model, the system becomes modular, making it easier to maintain and update without needing to modify the core LLM or retrain it.
Why Other Options Are Less Suitable:
A (Modify LLM’s Internal Architecture): Directly modifying the LLM's architecture is highly impractical and can disrupt the model’s performance. LLMs are typically treated as black-box models for tasks like prompt processing.
B (Avoid Custom Code): While it’s true that LLMs haven't been explicitly trained with preprocessed prompts, preprocessing can still improve clarity and alignment with desired input formats without confusing the model.
C (Postprocessing Outputs): While postprocessing the output can be useful, it doesn't address the need for clean and well-formatted inputs, which directly affect the quality of the model's responses.
Question # 6 Generative AI Engineer at an electronics company just deployed a RAG application for customers to ask questions about products that the company carries. However, they received feedback that the RAG response often returns information about an irrelevant product. What can the engineer do to improve the relevance of the RAG’s response? A. Assess the quality of the retrieved contextB. Implement caching for frequently asked questionsC. Use a different LLM to improve the generated responseD. Use a different semantic similarity search algorithm
Click for Answer
A. Assess the quality of the retrieved context
Answer Description Explanation:
Context Relevance: The RAG model generates answers based on retrieved documents or context. If the retrieved information is about an irrelevant product, it suggests that the retrieval step is failing to select the right context. The Generative AI Engineer must first assess the quality of what is being retrieved and ensure it is pertinent to the query.
Vector Search and Embedding Similarity: RAG typically uses vector search for retrieval, where embeddings of the query are matched against embeddings of product descriptions. Assessing thesemantic similarity searchprocess ensures that the closest matches are actually relevant to the query.
Fine-tuning the Retrieval Process: By improving theretrieval quality, such as tuning the embeddings or adjusting the retrieval strategy, the system can return more accurate and relevant product information.
Why Other Options Are Less Suitable:
Question # 7 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. Use spark.conf.set ()B. Pass variables using the Databricks Feature Store APIC. Add credentials using environment variablesD. Pass the secrets in plain text
Click for Answer
C. Add credentials using environment variables
Answer Description 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.
Question # 8 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. Switch to using External Models insteadB. Deploy the model using pay-per-token throughput as it comes with cost guaranteesC. Change to a model with a fewer number of parameters in order to reduce hardware constraint issuesD. Throttle the incoming batch of requests manually to avoid rate limiting issues
Click for Answer
B. Deploy the model using pay-per-token throughput as it comes with cost guarantees
Answer Description Explanation:
Explanation of Options:
Up-to-Date
We always provide up-to-date Databricks-Generative-AI-Engineer-Associate exam dumps to our clients. Keep checking website for updates and download.
Excellence
Quality and excellence of our Databricks Certified Generative AI Engineer Associate practice questions are above customers expectations. Contact live chat to know more.
Success
Your SUCCESS is assured with the Databricks-Generative-AI-Engineer-Associate exam questions of passin1day.com. Just Buy, Prepare and PASS!
Quality
All our braindumps are verified with their correct answers. Download Generative AI Engineer Practice tests in a printable PDF format.
Basic
$80
Any 3 Exams of Your Choice
3 Exams PDF + Online Test Engine
Buy Now
Premium
$100
Any 4 Exams of Your Choice
4 Exams PDF + Online Test Engine
Buy Now
Gold
$125
Any 5 Exams of Your Choice
5 Exams PDF + Online Test Engine
Buy Now
Passin1Day has a big success story in last 12 years with a long list of satisfied customers.
Databricks-Generative-AI-Engineer-Associate Dumps
We have recently updated Databricks Databricks-Generative-AI-Engineer-Associate dumps study guide. You can use our Generative AI Engineer braindumps and pass your exam in just 24 hours. Our Databricks Certified Generative AI Engineer Associate real exam contains latest questions. We are providing Databricks Databricks-Generative-AI-Engineer-Associate dumps with updates for 3 months. You can purchase in advance and start studying. Whenever Databricks update Databricks Certified Generative AI Engineer Associate exam, we also update our file with new questions. Passin1day is here to provide real Databricks-Generative-AI-Engineer-Associate exam questions to people who find it difficult to pass exam
What Our Customers Say
Jeff Brown
Thanks you so much passin1day.com team for all the help that you have provided me in my Databricks exam. I will use your dumps for next certification as well.
Mareena Frederick
You guys are awesome. Even 1 day is too much. I prepared my exam in just 3 hours with your Databricks-Generative-AI-Engineer-Associate exam dumps and passed it in first attempt :)
Ralph Donald
I am the fully satisfied customer of passin1day.com. I have passed my exam using your Databricks Certified Generative AI Engineer Associate braindumps in first attempt. You guys are the secret behind my success ;)
Lilly Solomon
I was so depressed when I get failed in my Cisco exam but thanks GOD you guys exist and helped me in passing my exams. I am nothing without you.
