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Databricks Certified Generative AI Engineer Associate - Databricks-Generative-AI-Engineer-Associate Exam Practice Test

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Total 75 questions

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Question 1

A Generative AI Engineer is testing a simple prompt template in LangChain using the code below, but is getting an error.

Assuming the API key was properly defined, what change does the Generative AI Engineer need to make to fix their chain?

A.

B.

C.

D.

Correct Answer: D

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Question 2

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

Correct Answer: C

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Question 3

A Generative AI Engineer is creating an agent-based LLM system for their favorite monster truck team. The system can answer text based questions about the monster truck team, lookup event dates via an API call, or query tables on the team's latest standings.
How could the Generative AI Engineer best design these capabilities into their system?

A. Ingest PDF documents about the monster truck team into a vector store and query it in a RAG architecture. B. Write a system prompt for the agent listing available tools and bundle it into an agent system that runs a number of calls to solve a query. C. Instruct the LLM to respond with "RAG", "API", or "TABLE" depending on the query, then use text parsing and conditional statements to resolve the query. D. Build a system prompt with all possible event dates and table information in the system prompt. Use a RAG architecture to lookup generic text questions and otherwise leverage the information in the system prompt.

Correct Answer: B

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Question 4

An AI developer team wants to fine-tune an open-weight model to have exceptional performance on a code generation use case. They are trying to choose the best model to start with. They want to minimize model hosting costs and are using Hugging Face model cards and spaces to explore models. Which TWO model attributes and metrics should the team focus on to make their selection?

A. Big Code Models Leaderboard B. Number of model parameters C. Number of model downloads last month D. Chatbot Arena Leaderboard E. MTEB Leaderboard

Correct Answer: A,B

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Question 5

All of the following are Python APIs used to query Databricks foundation models. When running in an interactive notebook, which of the following libraries does not automatically use the current session credentials?

A. OpenAI client B. MLflow Deployments SDK C. Databricks Python SDK D. REST API via requests library

Correct Answer: D

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Question 6

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. Reach out to the data curators directly before you have started using the trained model to let them know. 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. Use any available data you personally created which is completely original and you can decide what license to use.

Correct Answer: B

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Question 7

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?

A. Compare the Levenshtein distances of returned results against a representative sample of test inputs B. Compare the Recall-Onented-Understudy for Gistmg Evaluation (ROUGE) scores of returned results for a representative sample of test inputs C. Compare the Bilingual Evaluation Understudy (BLEU) scores of returned results for a representative sample of test inputs D. Compare the cosine similarities of the embeddings of returned results against those of a representative sample of test inputs

Correct Answer: D

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Question 8

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?

A. beautifulsoup B. flask C. numpy D. unstructured

Correct Answer: A

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Total 75 questions

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