Artificial Intelligence FC0-U71 Practice Questions (Page 6)

Questions 26–30

1. 26application · medium

   A data scientist is training a model to recognize spam emails. She has a dataset of 10,000 emails, each labeled 'spam' or 'not spam'. Which type of machine learning is she using?

   • ASupervised learning, because the model learns from labeled examples.
   • BUnsupervised learning, because the model finds patterns without labels.
   • CSemi-supervised learning, because the model uses a small amount of labeled data.
   • DReinforcement learning, because the model receives rewards for correct classifications.
   Reveal Solution Discussion Select an answer first
2. 27foundation · easy

   What is the role of machine learning within the broader field of artificial intelligence?

   • AIt is a separate field unrelated to AI.
   • BIt is a programming language used to develop AI systems.
   • CIt is a type of hardware used to run AI applications.
   • DIt is a subset of AI that focuses on enabling systems to learn from data.
   Reveal Solution Discussion Select an answer first
4. 28application · medium

   A healthcare AI startup is developing a model to predict patient readmission. They have access to a large dataset of patient records. What is the most important ethical consideration when using this data?

   • AEnsuring patient data is de-identified and used with proper consent.
   • BEnsuring the model is fast enough to make real-time predictions.
   • CEnsuring the model is trained on as much data as possible, regardless of source.
   • DEnsuring the model is deployed on a cloud platform.
   Reveal Solution Discussion Select an answer first
5. 29expert · hard

   A company is developing an AI system to screen job applicants. The team discovers that the training data is biased against a certain demographic group. They have two options: (1) remove the sensitive attribute from the data, or (2) rebalance the training data to be more representative. Which approach is more likely to address the bias effectively, and why?

   • ARemoving the attribute, because it directly prevents the model from using the demographic information.
   • BNeither approach can reduce bias, because AI systems are inherently unbiased.
   • CRebalancing the data, because simply removing the attribute may not eliminate bias if other features correlate with the demographic group.
   • DBoth are equally effective, because bias is solely caused by the presence of the sensitive attribute.
   Reveal Solution Discussion Select an answer first
6. 30foundation · easy

   What is the impact of using low-quality or incomplete data to train an AI model?

   • AThe model will run faster.
   • BThe model's predictions may be inaccurate or biased.
   • CThe model will be more secure.
   • DThe model will require less storage space.
   Reveal Solution Discussion Select an answer first