GENAI-ASSOCIATE practice questions 11–20

1. Question 11 · 92 in bank

   A Generative AI Engineer is deploying a customer-facing, fine-tuned LLM on their public website. Given the large investment the company put into fine tuning this model, and the proprietary nature of the tuning data, they are concerned about model inversion attacks. Which of the following Databricks AI Security Framework (DASF) risk mitigation strategies are most relevant to this use case?

   • AImplement AI guardrails to allow users to configure and enforce compliance
   • BLeverage Databricks access control lists (ACLs) to configure permissions for accessing models
   • CUse secure model features with Databricks Feature Store
   • DApply attribute-based access controls (ABAC) to limit unauthorized access

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   Answer: B

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2. Question 12 · 92 in bank

   Select 2

   A Generative Al Engineer is responsible for developing a chatbot to enable their company’s internal HelpDesk Call Center team to more quickly find related tickets and provide resolution. While creating the GenAI application work breakdown tasks for this project, they realize they need to start planning which data sources (either Unity Catalog volume or Delta table) they could choose for this application. They have collected several candidate data sources for consideration: call_rep_history: a Delta table with primary keys representative_id, call_id. This table is maintained to calculate representatives’ call resolution from fields call_duration and call start_time. transcript Volume: a Unity Catalog Volume of all recordings as a *.wav files, but also a text transcript as *.txt files. call_cust_history: a Delta table with primary keys customer_id, cal1_id. This table is maintained to calculate how much internal customers use the HelpDesk to make sure that the charge back model is consistent with actual service use. call_detail: a Delta table that includes a snapshot of all call details updated hourly. It includes root_cause and resolution fields, but those fields may be empty for calls that are still active. maintenance_schedule – a Delta table that includes a listing of both HelpDesk application outages as well as planned upcoming maintenance downtimes. They need sources that could add context to best identify ticket root cause and resolution. Which TWO sources do that? (Choose two.)

   • Acall_cust_history
   • Bmaintenance_schedule
   • Ccall_rep_history
   • Dcall_detail
   • Etranscript Volume

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   Answer: DE

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3. Question 13 · 92 in bank

   A Generative AI Engineer has been reviewing issues with their company's LLM based question-answering assistant and has determined that a technique called prompt chaining could help alleviate some performance concerns. However, to suggest this to their team, they have to clearly explain how it works and how it can benefit their question-answering assistant. Which explanation do they communicate to the team?

   • AIt allows you to break down complex tasks into multiple independent subtasks. This enables the assistant to generate more comprehensive and accurate responses.
   • BIt allows you to reduce the latency of your applications. By having multiple chains participating in the response as a chain, you increase the rate at which the response is generated.
   • CIt allows you to decrease the effort involved in crafting a prompt. Chains make it possible to reuse prompt text across multiple different use cases.
   • DIt reduces the average cost of a typical request. Chains make more efficient use of the tokens produced to generate higher quality responses with fewer tokens.

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   Answer: A

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4. Question 14 · 92 in bank

   A Generative AI 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?

   • APick an embedding model with multilingual support to support potential multilingual user questions
   • BPick the most recent and most performant open LLM released at the time
   • CPick an embedding model trained on related domain knowledge
   • DPick the embedding model ranked highest on the Massive Text Embedding Benchmark (MTEB) leaderboard hosted by HuggingFace

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   Answer: C

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5. Question 15 · 92 in bank

   A Generative Al Engineer is tasked with developing an application that is based on an open source large language model (LLM). They need a foundation LLM with a large context window. Which model fits this need?

   • ADistilBERT
   • BMPT-30B
   • CLlama2-70B
   • DDBRX

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   Answer: D

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6. Question 16 · 92 in bank

   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
   • CImplement a profanity filter to screen out offensive language.
   • DIncorporate manual reviews to correct any problematic outputs prior to sending to the users

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   Answer: B

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7. Question 17 · 92 in bank

   Select 2

   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 Huggingface model cards and spaces to explore models. Which TWO model attributes and metrics should the team focus on to make their selection? (Choose two.)

   • ABig Code Models Leaderboard
   • BNumber of model parameters
   • CMTEB Leaderboard
   • DChatbot Arena Leaderboard
   • ENumber of model downloads last month

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   Answer: AB

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8. Question 18 · 92 in bank

   A Generative AI 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?

   • AUse OAuth machine-to-machine authentication
   • BUse an access token belonging to service principals
   • CUse an access token belonging to any workspace user
   • DUse a frequently rotated access token belonging to either a workspace user or a service principal

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   Answer: B

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9. Question 19 · 92 in bank

   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?

   • AIngest PDF documents about the monster truck team into a vector store and query it in a RAG architecture.
   • BWrite 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.
   • CInstruct the LLM to respond with “RAG”, “API”, or “TABLE” depending on the query, then use text parsing and conditional statements to resolve the query.
   • DBuild 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.

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   Answer: B

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10. Question 20 · 92 in bank

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

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    Answer: C

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GENAI-ASSOCIATE practice, question 11. 92 questions available.