Describe AI MLOps/LLMOps Ecosystems and General Use CERTIFIED-AI-EXPERT Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A legal firm is implementing an LLM-based system to summarize case documents. They need to ensure the system does not generate incorrect legal citations. Which LLMOps practice is most important to mitigate this risk?

   • ARelying on the model's training data to ensure all citations are correct.
   • BLimiting the system to only summarize documents without generating citations.
   • CUsing a larger language model to improve the accuracy of generated citations.
   • DImplementing a validation step that checks generated citations against a database of known legal references.
   Reveal Solution Discussion Select an answer first
2. 2application · medium

   A gaming company is using MLOps to manage a model that predicts player churn. They need to deploy the model to multiple regions with low latency. Which infrastructure choice is most appropriate?

   • ADeploying the model to edge locations or multiple cloud regions to reduce latency for players.
   • BUsing a large batch processing job that runs predictions once a day.
   • CStoring the model on a shared network drive and having each game server load it locally.
   • DDeploying the model in a single centralized data center to simplify management.
   Reveal Solution Discussion Select an answer first
4. 3expert · hard

   A manufacturing company is using MLOps to predict equipment failures. The models are trained on sensor data from machines. The company has a strict requirement that any model update must be approved by a human expert before deployment. Which MLOps practice best accommodates this requirement?

   • AImplementing a CI/CD pipeline that pauses for manual approval before deploying the model to production.
   • BHaving data scientists manually deploy the model after training, without using a pipeline.
   • CAutomatically deploying all model updates without human intervention to speed up the process.
   • DUsing a shadow deployment where the new model runs in parallel but is never used for decisions.
   Reveal Solution Discussion Select an answer first
5. 4application · medium

   An e-commerce company wants to use LLMOps to automate customer support responses. They have a large dataset of past support tickets and want to fine-tune a model to handle common queries. Which step is most critical in the LLMOps lifecycle for this use case?

   • ASetting up a monitoring system only after the model is deployed and users report issues.
   • BCurating and cleaning the support ticket dataset to ensure high-quality training data.
   • CChoosing the largest possible model to maximize accuracy, regardless of cost.
   • DDeploying the model immediately after fine-tuning to get user feedback quickly.
   Reveal Solution Discussion Select an answer first
6. 5foundation · easy

   Which type of infrastructure is commonly used to run containerized ML workloads in an MLOps ecosystem?

   • ALoad balancer
   • BObject storage
   • CRelational database
   • DKubernetes cluster
   Reveal Solution Discussion Select an answer first