Hands-On Exercise: Exploratory Testing and Exploratory Data Analysis (EDA) CT-AI Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   What is the main purpose of exploratory data analysis (EDA) in the context of AI datasets?

   • ATo summarize and visualize the data to understand its structure, patterns, and anomalies.
   • BTo replace the need for formal statistical hypothesis testing.
   • CTo confirm that the dataset is perfectly clean and ready for modeling.
   • DTo automatically generate the final machine learning model.
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2. 17application · medium

   A test manager is planning the testing strategy for a new AI-based recommendation system. The team has limited time and wants to uncover unexpected user-facing issues quickly. Which approach best aligns with the principles of exploratory testing?

   • AHave a skilled tester interact with the system using realistic user scenarios, simultaneously learning the system and designing new test cases based on observations.
   • BAsk the developers to perform unit testing on the model's code to ensure it is error-free.
   • CAutomate a large suite of regression tests to ensure existing functionality is not broken.
   • DCreate a comprehensive test plan with pre-defined test cases derived from the requirements document.
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4. 18foundation · easy

   Which scenario best illustrates the integration of exploratory testing and EDA?

   • AA tester runs a regression test suite and then a data scientist cleans the data.
   • BA data scientist creates a histogram of feature distributions and then trains a model.
   • CA tester discovers that the AI system misclassifies images with low lighting, then checks the training data and finds that few low-light images exist.
   • DA data scientist uses a data visualization tool to create charts for a report.
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5. 19application · medium

   A data scientist is starting EDA on a dataset for a new project. The dataset has 100 features and 10,000 rows. What is the most efficient first step to get a high-level understanding of the data?

   • APlot a scatter plot of every pair of features to see all relationships.
   • BRemove all features with low variance to reduce the dataset size.
   • CGenerate summary statistics (mean, median, standard deviation) for all numerical features and check for missing values.
   • DTrain a preliminary model to see which features are most important.
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6. 20foundation · easy

   Why is exploratory testing particularly valuable for AI-based systems?

   • AIt guarantees that all possible inputs are tested.
   • BIt is the only method that can test AI models.
   • CIt can replace the need for any scripted testing.
   • DIt helps uncover unexpected behaviors that are hard to predict from specifications.
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