GENAI-ASSOCIATE practice questions 51–60

1. Question 51 · 92 in bank

   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?

   • AYou 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.
   • BYou 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”: “[email protected]”, “order_id”: “RE987D”}
   • CYou will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in a human-readable format.
   • DYou will receive customer emails and need to extract date, sender email, and order IReturn the extracted information in JSON format.

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   Answer: B

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2. Question 52 · 92 in bank

   A Generative AI Engineer has written scalable PySpark code to ingest unstructured PDF documents and chunk them in preparation for storing in a Databricks Vector Search index. Currently, the two columns of their dataframe include the original filename as a string and an array of text chunks from that document. What set of steps should the Generative AI Engineer perform to store the chunks in a ready-to-ingest manner for Databricks Vector Search?

   • AUse PySpark’s autoloader to apply a UDF across all chunks, formatting them in a JSON structure for Vector Search ingestion.
   • BFlatten the dataframe to one chunk per row, create a unique identifier for each row, and enable change feed on the output Delta table.
   • CUtilize the original filename as the unique identifier and save the dataframe as is.
   • DCreate a unique identifier for each document, flatten the dataframe to one chunk per row and save to an output Delta table.

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   Answer: B

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3. Question 53 · 92 in bank

   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?

   • AOpenAI client
   • BREST API via requests library
   • CMLflow Deployments SDK
   • DDatabricks Python SDK

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   Answer: B

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4. Question 54 · 92 in bank

   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()
   • Cvsc.create_direct_access_index()
   • Dvsc.similarity_search()

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   Answer: B

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5. Question 55 · 92 in bank

   A Generative AI Engineer is creating a customer support bot that should respond differently to an end user based on the sentiment in their initial message. For example, if the end user’s message was angry, the bot should try to de-escalate their negative sentiments as it solves the customer query. They want to make sure their approach follows best practices. Which approach will do this?

   • AUse an encoder-only LLM model to both detect sentiment and generate replies based upon the detected sentiment.
   • BImplement a RAG architecture for how to respond to users depending on detected sentiment.
   • CUse linear regression model to classify sentiment and feed the result to a system prompt for the LLM to respond.
   • DCreate a chain which first uses an LLM to classify sentiment, then changes system prompt for the customer interaction LLM based upon the initial customer query sentiment.

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   Answer: D

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6. Question 56 · 92 in bank

   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. Which Databricks feature should they use?

   • AAutoML
   • BVector Search
   • CInference Tables
   • DFeature Serving

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   Answer: C

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7. Question 57 · 92 in bank

   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
   • 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

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   Answer: B

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8. Question 58 · 92 in bank

   A Generative Al Engineer is tasked with improving the RAG quality by addressing its inflammatory outputs. Which action would be most effective in mitigating the problem of offensive text outputs?

   • AIncrease the frequency of upstream data updates
   • BInform the user of the expected RAG behavior
   • CRestrict access to the data sources to a limited number of users
   • DCurate upstream data properly that includes manual review before it is fed into the RAG system

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   Answer: D

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9. Question 59 · 92 in bank

   A Generative AI Engineer would like an LLM to parse and extract the following information: date, sender email, and order ID. The output should be formatted into JSON. Here’s an email sample:

   

   They need a prompt that will extract and output the required information in JSON with the highest level of output accuracy. Which prompt will do that?

   • AYou will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in a human-readable format.
   • BYou will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in JSON format.
   • CYou 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":"[email protected]", "order_id":"RE987D"}
   • DYou will receive customer emails and need to extract date, sender email, and order IYou should return the date, sender email, and order ID information in JSON format.

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   Answer: C

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10. Question 60 · 92 in bank

    What is the most suitable library for building a multi-step LLM-based workflow?

    • APandas
    • BTensorFlow
    • CPySpark
    • DLangChain

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    Answer: D

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GENAI-ASSOCIATE practice, question 51. 92 questions available.