Practice
1. Given the following Python code snippet, what will be the output if the model's performance has decayed due to data drift?
import numpy as np
from sklearn.metrics import accuracy_score
y_true = np.array([1, 0, 1, 1, 0])
y_pred = np.array([0, 0, 1, 0, 0])
accuracy = accuracy_score(y_true, y_pred)
print(f'Accuracy: {accuracy:.2f}')
easy
Solution
Step 1: Calculate accuracy
Compare y_true and y_pred: only 2 out of 5 predictions are correct.Step 2: Compute accuracy
Accuracy = 2/5 = 0.40.Final Answer:
Option A -> Option AQuick Check:
Count correct predictions, divide by total [OK]
Hint: Count correct predictions, divide by total [OK]
Common Mistakes:
2. In the context of Support Vector Machines, what is the primary purpose of the margin?
easy
Solution
Step 1: Understand the role of the margin
The margin in SVM is the distance between the decision boundary and the nearest data points from either class.Step 2: Eliminate wrong options
To minimize the number of support vectors is incorrect because the number of support vectors is not minimized. To ensure the model is non-linear is incorrect because the margin itself does not ensure non-linearity. To reduce the dimensionality of the data is incorrect because the margin does not reduce dimensionality.Final Answer:
Option B -> Option BQuick Check:
Margin maximizes boundary distance [OK]
Hint: Margin maximizes boundary distance [OK]
Common Mistakes:
3. You are choosing between a decision tree and a linear regression model for a dataset with a complex, non-linear relationship. Which model should you choose and why?
medium
Solution
Step 1: Analyze the dataset characteristics
The dataset has a complex, non-linear relationship.Step 2: Choose the appropriate model
Decision trees can capture non-linear relationships, making them suitable for this dataset.Final Answer:
Option C -> Option CQuick Check:
Decision trees capture non-linear patterns. [OK]
Hint: Decision trees capture non-linear patterns. [OK]
Common Mistakes:
4. When deciding between Few-Shot and Chain-of-Thought prompting, which scenario is best suited for Chain-of-Thought?
medium
Solution
Step 1: Understand Chain-of-Thought
Chain-of-Thought is best for tasks requiring complex reasoning.Step 2: Eliminate wrong options
When the task involves simple factual recall. is incorrect as factual recall doesn't need reasoning. When the model needs to be fine-tuned with a few examples. is about Few-Shot, not Chain-of-Thought. When the task requires generating creative content. is unrelated to reasoning.Final Answer:
Option B -> Option BQuick Check:
Use Chain-of-Thought for complex reasoning tasks. [OK]
Hint: Use Chain-of-Thought for complex reasoning tasks. [OK]
Common Mistakes:
5. You need to track the lineage of a model from data ingestion to deployment using MLflow. Which approach best achieves this?
hard
Solution
Step 1: Understand MLflow's capabilities
MLflow alone does not track data lineage; integration is needed.Step 2: Evaluate options for lineage tracking
Integrating with a dedicated lineage tool provides comprehensive tracking.Final Answer:
Option B -> Option BQuick Check:
Lineage needs integration [OK]
Hint: Lineage needs integration [OK]
Common Mistakes: