Practice
1. In the context of dropout regularization, what happens to the dropout rate during inference?
easy
Solution
Step 1: Understand dropout behavior during inference
During inference, dropout is not applied, meaning the dropout rate is effectively zero.Step 2: Eliminate wrong options
The dropout rate is increased to improve generalization. is incorrect because increasing dropout during inference would not improve generalization. The dropout rate remains the same as during training. is incorrect because dropout is not applied during inference. The dropout rate is adjusted based on validation performance. is incorrect because dropout rate is not adjusted based on validation performance during inference.Final Answer:
Option A -> Option AQuick Check:
Dropout off during inference. [OK]
Hint: Dropout off during inference. [OK]
Common Mistakes:
2. A production ML model's performance has unexpectedly dropped. What is the most likely root cause?
medium
Solution
Step 1: Identify common causes of performance drop
Data drift is a frequent cause of unexpected performance drops.Step 2: Eliminate wrong options
The model's hyperparameters were not tuned properly. would cause issues from the start. The model architecture is outdated. is less likely to cause sudden drops. The training dataset was too small. affects initial performance, not sudden changes.Final Answer:
Option D -> Option DQuick Check:
Sudden drop? Check data distribution first [OK]
Hint: Sudden drop? Check data distribution first [OK]
Common Mistakes:
3. How would you adapt a decision tree to handle a dataset with a large number of categorical features?
hard
Solution
Step 1: Identify the best adaptation method.
Some decision tree algorithms, like C4.5, handle categorical features natively.Step 2: Eliminate wrong options.
One-hot encoding can lead to high dimensionality, label encoding can mislead the model, and PCA is not suitable for categorical data.Final Answer:
Option A -> Option AQuick Check:
Use native categorical support in trees. [OK]
Hint: Use native categorical support in trees. [OK]
Common Mistakes:
4. You are designing a recommendation system for a video platform. Which metric would best capture user satisfaction if users tend to watch only the first few recommended videos?
hard
Solution
Step 1: Understand user behavior
Users watching only the first few videos means early precision is crucial.Step 2: Choose the metric
Precision at K directly measures the quality of the top K recommendations, aligning with user behavior.Final Answer:
Option A -> Option AQuick Check:
Precision at K focuses on top results. [OK]
Hint: Precision at K focuses on top results. [OK]
Common Mistakes:
5. You are tasked with developing a system that can autonomously drive a car in a simulated environment. Which learning approach should you prioritize and why?
hard
Solution
Step 1: Identify the task requirements.
The task requires learning optimal strategies in a dynamic environment.Step 2: Eliminate wrong options.
Supervised learning, because it can learn from labeled driving data. is incorrect because labeled data alone doesn't adapt to new scenarios. Unsupervised learning, because it can discover patterns in driving data. is incorrect because pattern discovery doesn't imply strategy learning. Supervised learning, because it can classify different driving scenarios. is incorrect because classification doesn't optimize actions.Final Answer:
Option C -> Option CQuick Check:
Dynamic strategy learning -> Reinforcement learning [OK]
Hint: Dynamic strategy learning -> Reinforcement learning [OK]
Common Mistakes: