AI Project Life Cycle, MLOps, and DataOps CAIPM Practice Questions (Page 2)

Questions 6–10

1. 6foundation · easy

   How do MLOps and DataOps work together to streamline the AI project life cycle?

   • AMLOps is only used for deep learning, while DataOps is only for traditional machine learning
   • BDataOps ensures reliable data pipelines, while MLOps manages the model development and deployment lifecycle
   • CMLOps handles data ingestion, while DataOps handles model training
   • DDataOps replaces MLOps in the deployment phase
   Reveal Solution Discussion Select an answer first
2. 7application · medium

   A company is in the early stages of an AI project to predict equipment failure. The team has defined the problem and collected historical sensor data. Before building the model, the team wants to ensure that the data is suitable for the task. Which activity should they perform in the data preparation phase?

   • ADefine the business metrics
   • BClean the data and handle missing values
   • CSelect the ML algorithm
   • DDeploy the model to production
   Reveal Solution Discussion Select an answer first
4. 8application · medium

   A government agency is building a data pipeline that ingests data from multiple departments. The pipeline must ensure data quality and provide clear ownership for each data set. The agency wants to adopt DataOps principles. Which combination of practices should they implement?

   • AA single data engineer responsible for all data sets and no documentation.
   • BCentralized data storage without access controls and no monitoring.
   • CAutomated data quality tests, clear data ownership, and cross-team collaboration.
   • DManual data validation, siloed team responsibilities, and weekly status meetings.
   Reveal Solution Discussion Select an answer first
5. 9foundation · easy

   In the AI project life cycle, which phase is primarily concerned with ensuring the model remains accurate and relevant after it has been deployed?

   • AProblem definition
   • BMaintenance
   • CDeployment
   • DData preparation
   Reveal Solution Discussion Select an answer first
6. 10expert · hard

   A company has a model in production that is retrained weekly. The retraining pipeline is automated, but the team notices that the model's performance sometimes degrades after retraining. The data pipeline and model code are versioned. What is the MOST LIKELY cause of this issue?

   • AThe CI/CD pipeline is not triggering retraining.
   • BThe model is not being deployed to the correct environment.
   • CThe model code is not versioned properly.
   • DData drift or changes in the underlying data distribution.
   Reveal Solution Discussion Select an answer first