Objective 2.5 Address Business Risks, Ethical Concerns, and Related Concepts in Data Exploration/feature Engineering AIP-210 Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A financial services firm is required by regulators to explain how each feature in its risk model was derived. The data science team has been iterating rapidly, creating new features from raw transaction data. What is the most effective way to ensure the firm can meet this regulatory requirement?

   • ACreate a presentation for stakeholders summarizing the model's overall accuracy and key features.
   • BMaintain a version-controlled data dictionary and transformation log that records the source, logic, and date of every feature.
   • CStore the final training dataset in a secure data lake for future reference.
   • DAsk the data scientists to write a blog post about the feature engineering process for public transparency.
   Reveal Solution Discussion Select an answer first
2. 2foundation · easy

   During data exploration, a data scientist discovers that a significant portion of a key feature contains missing values. Which business risk does this primarily introduce?

   • ARisk of model bias due to underrepresentation of certain groups
   • BRisk of privacy violations due to exposure of sensitive attributes
   • CRisk of degraded model performance due to incomplete data
   • DRisk of data quality issues that can lead to unreliable insights
   Reveal Solution Discussion Select an answer first
4. 3foundation · easy

   Which of the following is an example of documenting data provenance?

   • ACreating a backup of the data on a separate server
   • BRecording the source system and the date the data was extracted
   • CEncrypting the data at rest and in transit
   • DStoring the data in a secure cloud storage bucket
   Reveal Solution Discussion Select an answer first
5. 4foundation · easy

   What is the primary purpose of documenting data lineage?

   • ATo automate the feature engineering process
   • BTo improve model accuracy by tracking feature importance
   • CTo provide a clear audit trail of data origins and transformations
   • DTo reduce the storage cost of the data
   Reveal Solution Discussion Select an answer first
6. 5foundation · easy

   In feature engineering, what is the primary ethical concern when selecting features that are highly correlated with a protected attribute (e.g., race or gender)?

   • AThe model may inadvertently discriminate against certain groups
   • BThe features may be redundant and increase training time
   • CThe model may become too complex and difficult to interpret
   • DThe features may contain too many missing values
   Reveal Solution Discussion Select an answer first