Objective 1.4 Analyze Machine Learning System Use Cases AIP-210 Practice Questions (Page 2)

Questions 6–10

1. 6expert · hard

   A company wants to use ML to predict equipment failure. They have sensor data from machines, but the data is noisy and contains many missing values. They also have a limited budget for data cleaning. The business impact of a false alarm is low, but missing a real failure is costly. What should they prioritize?

   • ADeploy a model quickly with the noisy data to save budget
   • BInvest in data cleaning to improve data quality, even if it delays the project
   • CAbandon the ML project because data is noisy
   • DFocus on minimizing false alarms, ignoring missed failures
   Reveal Solution Discussion Select an answer first
2. 7foundation · easy

   A bank wants to predict whether a loan applicant will default on a loan, with the outcome being either 'default' or 'no default'. Which machine learning task is most appropriate?

   • ARegression
   • BClassification
   • CAnomaly detection
   • DClustering
   Reveal Solution Discussion Select an answer first
4. 8application · medium

   A government agency wants to use ML to screen job applications. They must ensure the model does not discriminate based on gender or ethnicity. Which constraint is most important to address?

   • ANumber of applicants
   • BFairness and bias mitigation
   • CModel speed
   • DCost of cloud storage
   Reveal Solution Discussion Select an answer first
5. 9application · medium

   A small business wants to use ML to predict which customers are likely to churn. They have only 200 customer records and no data science team. What is the most important feasibility consideration?

   • AThe brand of the cloud provider
   • BWhether the dataset is large enough to train a reliable model
   • CThe color scheme of the dashboard
   • DThe number of employees in the company
   Reveal Solution Discussion Select an answer first
6. 10foundation · easy

   A company wants to build an ML model to predict employee turnover, but it only has data on current employees and no historical records of employees who left. Which factor makes this use case infeasible?

   • ABusiness requirements are unclear
   • BThe problem is not a machine learning problem
   • CProblem complexity is too high
   • DLack of labeled data
   Reveal Solution Discussion Select an answer first