GENERATIVE-AI-ENGINEER-ASSOCIATE — questions

Page 2 of 5 · 91 total questions.

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 22

Topic 1 · Question 22

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?

AAssess the quality of the retrieved context (correct answer)
BImplement caching for frequently asked questions
CUse a different LLM to improve the generated response
DUse a different semantic similarity search algorithm

Reveal answer & explanation

Correct answer: A

The correct answer is A. Option A: Assess the quality of the retrieved context

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 23

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Topic 1 · Question 23

A Generative AI Engineer is developing a chatbot designed to assist users with insurance-related queries. The chatbot is built on a large language model (LLM) and is conversational. However, to maintain the chatbot’s focus and to comply with company policy, it must not provide responses to questions about politics. Instead, when presented with political inquiries, the chatbot should respond with a standard message: “Sorry, I cannot answer that. I am a chatbot that can only answer questions around insurance.” Which framework type should be implemented to solve this?

ASafety Guardrail
BSecurity Guardrail
CContextual Guardrail (correct answer)
DCompliance Guardrail

Reveal answer & explanation

Correct answer: C

The correct answer is C. Option C: Contextual Guardrail

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 24

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Topic 1 · Question 24

A Generative AI Engineer I using the code below to test setting up a vector store: Assuming they intend to use Databricks managed embeddings with the default embedding model, what should be the next logical function call?

Avsc.get_index()
Bvsc.create_delta_sync_index() (correct answer)
Cvsc.create_direct_access_index()
Dvsc.similarity_search()

Reveal answer & explanation

Correct answer: B

The correct answer is B. Option B: vsc.create_delta_sync_index()

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 25

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Topic 1 · Question 25

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?

AUse spark.conf.set ()
BPass variables using the Databricks Feature Store API
CAdd credentials using environment variables (correct answer)
DPass the secrets in plain text

Reveal answer & explanation

Correct answer: C

The correct answer is C. Option C: Add credentials using environment variables

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 26

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Topic 1 · Question 26

A Generative AI Engineer wants to build an LLM-based solution to help a restaurant improve its online customer experience with bookings by automatically handling common customer inquiries. The goal of the solution is to minimize escalations to human intervention and phone calls while maintaining a personalized interaction. To design the solution, the Generative AI Engineer needs to define the input data to the LLM and the task it should perform. Which input/output pair will support their goal?

AInput: Online chat logs; Output: Group the chat logs by users, followed by summarizing each user’s interactions
BInput: Online chat logs; Output: Buttons that represent choices for booking details (correct answer)
CInput: Customer reviews; Output: Classify review sentiment
DInput: Online chat logs; Output: Cancellation options

Reveal answer & explanation

Correct answer: B

The correct answer is B. Option B: Input: Online chat logs; Output: Buttons that represent choices for booking details

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 27

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Topic 1 · Question 27

What is an effective method to preprocess prompts using custom code before sending them to an LLM?

ADirectly modify the LLM’s internal architecture to include preprocessing steps
BIt is better not to introduce custom code to preprocess prompts as the LLM has not been trained with examples of the preprocessed prompts
CRather than preprocessing prompts, it’s more effective to postprocess the LLM outputs to align the outputs to desired outcomes
DWrite a MLflow PyFunc model that has a separate function to process the prompts (correct answer)

Reveal answer & explanation

Correct answer: D

The correct answer is D. Option D: Write a MLflow PyFunc model that has a separate function to process the prompts

Explanation

MLflow tracks experiments, packages models, manages model versions, and supports governed deployment workflows.

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 28

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Topic 1 · Question 28

A Generative AI Engineer is developing an LLM application that users can use to generate personalized birthday poems based on their names. Which technique would be most effective in safeguarding the application, given the potential for malicious user inputs?

AImplement a safety filter that detects any harmful inputs and ask the LLM to respond that it is unable to assist (correct answer)
BReduce the time that the users can interact with the LLM
CAsk the LLM to remind the user that the input is malicious but continue the conversation with the user
DIncrease the amount of compute that powers the LLM to process input faster

Reveal answer & explanation

Correct answer: A

The correct answer is A. Option A: Implement a safety filter that detects any harmful inputs and ask the LLM to respond that it is unable to assist

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 29

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Topic 1 · Question 29

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

AThe ability to generate responses in code
BThe similarity to the previous language
CThe latency of the response and the length of text generated
DThe accuracy and relevance of the responses (correct answer)

Reveal answer & explanation

Correct answer: D

The correct answer is D. Option D: The accuracy and relevance of the responses

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 30

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Topic 1 · Question 30

A Generative AI Engineer is developing a patient-facing healthcare-focused chatbot. If the patient’s question is not a medical emergency, the chatbot should solicit more information from the patient to pass to the doctor’s office and suggest a few relevant pre-approved medical articles for reading. If the patient’s question is urgent, direct the patient to calling their local emergency services. Given the following user input: “I have been experiencing severe headaches and dizziness for the past two days.” Which response is most appropriate for the chatbot to generate?

AHere are a few relevant articles for your browsing. Let me know if you have questions after reading them.
BPlease call your local emergency services. (correct answer)
CHeadaches can be tough. Hope you feel better soon!
DPlease provide your age, recent activities, and any other symptoms you have noticed along with your headaches and dizziness.

Reveal answer & explanation

Correct answer: B

The correct answer is B. Option B: Please call your local emergency services.

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 31

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Topic 1 · Question 31 · Select all that apply

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.)

AUse a smaller embedding model to generate embeddings
BReduce the maximum output tokens of the new model
CDecrease the chunk size of embedded documents (correct answer)
DReduce the number of records retrieved from the vector database (correct answer)
ERetrain the response generating model using ALiBi

Reveal answer & explanation

Correct answer: C, D

The correct answer is C, D. Option C: Decrease the chunk size of embedded documents Option D: Reduce the number of records retrieved from the vector database

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 32

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Topic 1 · Question 32

A Generative Al Engineer is building a system which will answer questions on latest stock news articles. Which will NOT help with ensuring the outputs are relevant to financial news?

AImplement a comprehensive guardrail framework that includes policies for content filters tailored to the finance sector.
BIncrease the compute to improve processing speed of questions to allow greater relevancy analysis (correct answer)
CImplement a profanity filter to screen out offensive language.
DIncorporate manual reviews to correct any problematic outputs prior to sending to the users

Reveal answer & explanation

Correct answer: B

The correct answer is B. Option B: Increase the compute to improve processing speed of questions to allow greater relevancy analysis

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 33

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Topic 1 · Question 33

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?

AKeep all articles because the RAG application needs to understand non-company content to avoid answering questions about them.
BInclude in the system prompt that any information it sees will be about SnoPenAI, even if no data filtering is performed.
CInclude in the system prompt that the application is not supposed to answer any questions unrelated to SnoPen AI. (correct answer)
DConsolidate all SnoPen AI related documents into a single chunk in the vector database.

Reveal answer & explanation

Correct answer: C

The correct answer is C. Option C: Include in the system prompt that the application is not supposed to answer any questions unrelated to SnoPen AI.

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 34

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Topic 1 · Question 34

A Generative Al Engineer has successfully ingested unstructured documents and chunked them by document sections. They would like to store the chunks in a Vector Search index. The current format of the dataframe has two columns: (i) original document file name (ii) an array of text chunks for each document. What is the most performant way to store this dataframe?

ASplit the data into train and test set, create a unique identifier for each document, then save to a Delta table
BFlatten the dataframe to one chunk per row, create a unique identifier for each row, and save to a Delta table (correct answer)
CFirst create a unique identifier for each document, then save to a Delta table
DStore each chunk as an independent JSON file in Unity Catalog Volume. For each JSON file, the key is the document section name and the value is the array of text chunks for that section

Reveal answer & explanation

Correct answer: B

The correct answer is B. Option B: Flatten the dataframe to one chunk per row, create a unique identifier for each row, and save to a Delta table

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 35

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Topic 1 · Question 35

A Generative AI Engineer has created a RAG application which can help employees retrieve answers from an internal knowledge base, such as Confluence pages or Google Drive. The prototype application is now working with some positive feedback from internal company testers. Now the Generative Al Engineer wants to formally evaluate the system’s performance and understand where to focus their efforts to further improve the system. How should the Generative AI Engineer evaluate the system?

AUse cosine similarity score to comprehensively evaluate the quality of the final generated answers.
BCurate a dataset that can test the retrieval and generation components of the system separately. Use MLflow’s built in evaluation metrics to perform the evaluation on the retrieval and generation components. (correct answer)
CBenchmark multiple LLMs with the same data and pick the best LLM for the job.
DUse an LLM-as-a-judge to evaluate the quality of the final answers generated.

Reveal answer & explanation

Correct answer: B

The correct answer is B. Option B: Curate a dataset that can test the retrieval and generation components of the system separately. Use MLflow’s built in evaluation metrics to perform the evaluation on the retrieval and generation components.

Explanation

MLflow tracks experiments, packages models, manages model versions, and supports governed deployment workflows.

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 36

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Topic 1 · Question 36

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?

ALog 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
BLog the model using MLflow during training, directly register the model to Unity Catalog using the MLflow API, and start a serving endpoint (correct answer)
CSave the model along with its dependencies in a local directory, build the Docker image, and run the Docker container
DWrap the LLM’s prediction function into a Flask application and serve using Gunicorn

Reveal answer & explanation

Correct answer: B

The correct answer is B. Option B: Log the model using MLflow during training, directly register the model to Unity Catalog using the MLflow API, and start a serving endpoint

Explanation

Unity Catalog provides centralized governance, fine-grained permissions, lineage, discovery, and auditing across Databricks data and AI assets. MLflow tracks experiments, packages models, manages model versions, and supports governed deployment workflows.

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 37

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Topic 1 · Question 37

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?

ASwitch to using External Models instead
BDeploy the model using pay-per-token throughput as it comes with cost guarantees (correct answer)
CChange to a model with a fewer number of parameters in order to reduce hardware constraint issues
DThrottle the incoming batch of requests manually to avoid rate limiting issues

Reveal answer & explanation

Correct answer: B

The correct answer is B. Option B: Deploy the model using pay-per-token throughput as it comes with cost guarantees This option delivers the requirement at the lowest cost.

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 38

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Topic 1 · Question 38

A Generative AI Engineer is building an LLM to generate article summaries in the form of a type of poem, such as a haiku, given the article content. However, the initial output from the LLM does not match the desired tone or style. Which approach will NOT improve the LLM’s response to achieve the desired response?

AProvide the LLM with a prompt that explicitly instructs it to generate text in the desired tone and style
BUse a neutralizer to normalize the tone and style of the underlying documents (correct answer)
CInclude few-shot examples in the prompt to the LLM
DFine-tune the LLM on a dataset of desired tone and style

Reveal answer & explanation

Correct answer: B

The correct answer is B. Option B: Use a neutralizer to normalize the tone and style of the underlying documents

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 39

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Topic 1 · Question 39

A Generative AI Engineer is creating an LLM-powered application that will need access to up-to-date news articles and stock prices. The design requires the use of stock prices which are stored in Delta tables and finding the latest relevant news articles by searching the internet. How should the Generative AI Engineer architect their LLM system?

AUse an LLM to summarize the latest news articles and lookup stock tickers from the summaries to find stock prices.
BQuery the Delta table for volatile stock prices and use an LLM to generate a search query to investigate potential causes of the stock volatility.
CDownload and store news articles and stock price information in a vector store. Use a RAG architecture to retrieve and generate at runtime.
DCreate an agent with tools for SQL querying of Delta tables and web searching, provide retrieved values to an LLM for generation of response. (correct answer)

Reveal answer & explanation

Correct answer: D

The correct answer is D. Option D: Create an agent with tools for SQL querying of Delta tables and web searching, provide retrieved values to an LLM for generation of response.

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 40

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Topic 1 · Question 40

A Generative AI Engineer is designing a chatbot for a gaming company that aims to engage users on its platform while its users play online video games. Which metric would help them increase user engagement and retention for their platform?

ARandomness
BDiversity of responses (correct answer)
CLack of relevance
DRepetition of responses

Reveal answer & explanation

Correct answer: B

The correct answer is B. Option B: Diversity of responses

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 41

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Topic 1 · Question 41

A company has a typical RAG-enabled, customer-facing chatbot on its website. Select the correct sequence of components a user's questions will go through before the final output is returned. Use the diagram above for reference.

A1.embedding model, 2.vector search, 3.context-augmented prompt, 4.response-generating LLM (correct answer)
B1.context-augmented prompt, 2.vector search, 3.embedding model, 4.response-generating LLM
C1.response-generating LLM, 2.vector search, 3.context-augmented prompt, 4.embedding model
D1.response-generating LLM, 2.context-augmented prompt, 3.vector search, 4.embedding model

Reveal answer & explanation

Correct answer: A

The correct answer is A. Option A: 1.embedding model, 2.vector search, 3.context-augmented prompt, 4.response-generating LLM

Explanation

Databricks Vector Search builds and serves similarity indexes for retrieval-augmented generation applications. An embedding maps content into a numeric vector so semantically similar items can be retrieved by distance.

Showing questions 21–40 of 91 · Page 2 of 5