2.1 Recommend AI-powered Data Analytic Techniques to Meet Specific Needs or Answer Specific Questions 350-201 Practice Questions (Page 2)

Questions 6–10

1. 6foundation · easy

   Why might a security team use an ensemble method instead of a single model?

   • ATo reduce the risk of overfitting and improve generalization
   • BTo eliminate the need for labeled training data
   • CTo make the model run faster
   • DTo simplify the model explanation
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2. 7expert · hard

   A data science team is building a model to detect zero-day malware. They have a dataset with a high class imbalance (very few malicious samples) and high-dimensional features. They have tried a single decision tree, but it overfits and has low recall. They want to improve recall while maintaining reasonable precision. Which approach should they recommend?

   • AUse a random forest ensemble to reduce overfitting and improve generalization
   • BUse k-means clustering to group samples and then classify based on cluster distance
   • CUse a single deep neural network with many layers
   • DUse linear regression to predict a continuous malware score
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4. 8application · medium

   A security team is developing a malware detection model. They have a dataset with many features and have trained several individual classifiers, but each one has different strengths and weaknesses. They want to improve overall accuracy and robustness by combining the predictions of multiple models. Which technique should they recommend?

   • AClustering the dataset into groups before classification
   • BEnsemble methods such as random forest or gradient boosting
   • CTime-series analysis on model performance over time
   • DRegression analysis to combine model scores into a continuous output
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5. 9foundation · easy

   What is the primary benefit of using graph analytics in cybersecurity?

   • AIt can uncover relationships and patterns in connected data, such as attack paths.
   • BIt predicts continuous values such as risk scores.
   • CIt processes unstructured text data.
   • DIt groups similar data points without labels.
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6. 10foundation · easy

   What is the primary difference between supervised and unsupervised learning?

   • ASupervised learning is faster than unsupervised learning.
   • BSupervised learning uses labeled data, while unsupervised learning uses unlabeled data.
   • CSupervised learning is used for clustering, while unsupervised learning is used for classification.
   • DSupervised learning requires more data than unsupervised learning.
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