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Free and Premium Databricks Databricks-Generative-AI-Engineer-Associate Dumps Questions Answers

Databricks Certified Generative AI Engineer Associate Questions and Answers

Question 1

A Generative Al Engineer is developing a RAG application and would like to experiment with different embedding models to improve the application performance.

Which strategy for picking an embedding model should they choose?

Options:

A.

Pick an embedding model trained on related domain knowledge

B.

Pick the most recent and most performant open LLM released at the time

C.

pick the embedding model ranked highest on the Massive Text Embedding Benchmark (MTEB) leaderboard hosted by HuggingFace

D.

Pick an embedding model with multilingual support to support potential multilingual user questions

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Question 2

A Generative Al Engineer is creating an LLM-based application. The documents for its retriever have been chunked to a maximum of 512 tokens each. The Generative Al Engineer knows that cost and latency are more important than quality for this application. They have several context length levels to choose from.

Which will fulfill their need?

Options:

A.

context length 514; smallest model is 0.44GB and embedding dimension 768

B.

context length 2048: smallest model is 11GB and embedding dimension 2560

C.

context length 32768: smallest model is 14GB and embedding dimension 4096

D.

context length 512: smallest model is 0.13GB and embedding dimension 384

Question 3

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?

Options:

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.

Question 4

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?

Options:

A.

Switch to using External Models instead

B.

Deploy the model using pay-per-token throughput as it comes with cost guarantees

C.

Change to a model with a fewer number of parameters in order to reduce hardware constraint issues

D.

Throttle the incoming batch of requests manually to avoid rate limiting issues

Question 5

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.)

Options:

A.

Use a smaller embedding model to generate

B.

Reduce the maximum output tokens of the new model

C.

Decrease the chunk size of embedded documents

D.

Reduce the number of records retrieved from the vector database

E.

Retrain the response generating model using ALiBi

Question 6

A Generative Al Engineer is building a RAG application that answers questions about internal documents for the company SnoPen AI.

The source documents may contain a significant amount of irrelevant content, such as advertisements, sports news, or entertainment news, or content about other companies.

Which approach is advisable when building a RAG application to achieve this goal of filtering irrelevant information?

Options:

A.

Keep all articles because the RAG application needs to understand non-company content to avoid answering questions about them.

B.

Include in the system prompt that any information it sees will be about SnoPenAI, even if no data filtering is performed.

C.

Include in the system prompt that the application is not supposed to answer any questions unrelated to SnoPen Al.

D.

Consolidate all SnoPen AI related documents into a single chunk in the vector database.

Question 7

Which indicator should be considered to evaluate the safety of the LLM outputs when qualitatively assessing LLM responses for a translation use case?

Options:

A.

The ability to generate responses in code

B.

The similarity to the previous language

C.

The latency of the response and the length of text generated

D.

The accuracy and relevance of the responses

Question 8

A Generative Al Engineer is deciding between using LSH (Locality Sensitive Hashing) and HNSW (Hierarchical Navigable Small World) for indexing their vector database Their top priority is semantic accuracy

Which approach should the Generative Al Engineer use to evaluate these two techniques?

Options:

A.

Compare the cosine similarities of the embeddings of returned results against those of a representative sample of test inputs

B.

Compare the Bilingual Evaluation Understudy (BLEU) scores of returned results for a representative sample of test inputs

C.

Compare the Recall-Onented-Understudy for Gistmg Evaluation (ROUGE) scores of returned results for a representative sample of test inputs

D.

Compare the Levenshtein distances of returned results against a representative sample of test inputs

Question 9

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?

Options:

A.

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.

B.

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”}

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.

Question 10

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?

Options:

A.

Assess the quality of the retrieved context

B.

Implement caching for frequently asked questions

C.

Use a different LLM to improve the generated response

D.

Use a different semantic similarity search algorithm

Question 11

A Generative Al Engineer has created a RAG application to look up answers to questions about a series of fantasy novels that are being asked on the author’s web forum. The fantasy novel texts are chunked and embedded into a vector store with metadata (page number, chapter number, book title), retrieved with the user’s query, and provided to an LLM for response generation. The Generative AI Engineer used their intuition to pick the chunking strategy and associated configurations but now wants to more methodically choose the best values.

Which TWO strategies should the Generative AI Engineer take to optimize their chunking strategy and parameters? (Choose two.)

Options:

A.

Change embedding models and compare performance.

B.

Add a classifier for user queries that predicts which book will best contain the answer. Use this to filter retrieval.

C.

Choose an appropriate evaluation metric (such as recall or NDCG) and experiment with changes in the chunking strategy, such as splitting chunks by paragraphs or chapters.

Choose the strategy that gives the best performance metric.

D.

Pass known questions and best answers to an LLM and instruct the LLM to provide the best token count. Use a summary statistic (mean, median, etc.) of the best token counts to choose chunk size.

E.

Create an LLM-as-a-judge metric to evaluate how well previous questions are answered by the most appropriate chunk. Optimize the chunking parameters based upon the values of the metric.

Question 12

A Generative AI Engineer is building a Generative AI system that suggests the best matched employee team member to newly scoped projects. The team member is selected from a very large team. The match should be based upon project date availability and how well their employee profile matches the project scope. Both the employee profile and project scope are unstructured text.

How should the Generative Al Engineer architect their system?

Options:

A.

Create a tool for finding available team members given project dates. Embed all project scopes into a vector store, perform a retrieval using team member profiles to find the best team member.

B.

Create a tool for finding team member availability given project dates, and another tool that uses an LLM to extract keywords from project scopes. Iterate through available team members’ profiles and perform keyword matching to find the best available team member.

C.

Create a tool to find available team members given project dates. Create a second tool that can calculate a similarity score for a combination of team member profile and the project scope. Iterate through the team members and rank by best score to select a team member.

D.

Create a tool for finding available team members given project dates. Embed team profiles into a vector store and use the project scope and filtering to perform retrieval to find the available best matched team members.

Question 13

A Generative AI Engineer is building a RAG application that will rely on context retrieved from source documents that are currently in PDF format. These PDFs can contain both text and images. They want to develop a solution using the least amount of lines of code.

Which Python package should be used to extract the text from the source documents?

Options:

A.

flask

B.

beautifulsoup

C.

unstructured

D.

numpy

Question 14

A Generative Al Engineer wants their (inetuned LLMs in their prod Databncks workspace available for testing in their dev workspace as well. All of their workspaces are Unity Catalog enabled and they are currently logging their models into the Model Registry in MLflow.

What is the most cost-effective and secure option for the Generative Al Engineer to accomplish their gAi?

Options:

A.

Use an external model registry which can be accessed from all workspaces

B.

Setup a script to export the model from prod and import it to dev.

C.

Setup a duplicate training pipeline in dev, so that an identical model is available in dev.

D.

Use MLflow to log the model directly into Unity Catalog, and enable READ access in the dev workspace to the model.

Question 15

A Generative Al Engineer needs to design an LLM pipeline to conduct multi-stage reasoning that leverages external tools. To be effective at this, the LLM will need to plan and adapt actions while performing complex reasoning tasks.

Which approach will do this?

Options:

A.

Tram the LLM to generate a single, comprehensive response without interacting with any external tools, relying solely on its pre-trained knowledge.

B.

Implement a framework like ReAct which allows the LLM to generate reasoning traces and perform task-specific actions that leverage external tools if necessary.

C.

Encourage the LLM to make multiple API calls in sequence without planning or structuring the calls, allowing the LLM to decide when and how to use external tools spontaneously.

D.

Use a Chain-of-Thought (CoT) prompting technique to guide the LLM through a series of reasoning steps, then manually input the results from external tools for the final answer.

Question 16

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?

Options:

A.

Add guardrails to filter outputs from the LLM before it is shown to the user

B.

Fine-tune the model on your data, hoping it will learn what is appropriate and not

C.

Limit the data available based on the user’s access level

D.

Use a strong system prompt to ensure the model aligns with your needs.

Question 17

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?

Options:

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.

Log the model using MLflow during training, directly register the model to Unity Catalog using the MLflow API, and start a serving endpoint

C.

Save the model along with its dependencies in a local directory, build the Docker image, and run the Docker container

D.

Wrap the LLM’s prediction function into a Flask application and serve using Gunicorn

Question 18

A Generative Al Engineer is ready to deploy an LLM application written using Foundation Model APIs. They want to follow security best practices for production scenarios

Which authentication method should they choose?

Options:

A.

Use an access token belonging to service principals

B.

Use a frequently rotated access token belonging to either a workspace user or a service principal

C.

Use OAuth machine-to-machine authentication

D.

Use an access token belonging to any workspace user