Practice
1.
import torch
x = torch.tensor([-1.0, 0.0, 1.0])
output = torch.nn.functional.relu(x)
print(output)easy
Solution
Step 1: Understand ReLU behavior.
ReLU outputs zero for negative inputs and the input itself for non-negative inputs.Step 2: Apply ReLU to each element.
For -1.0, output is 0.0; for 0.0, output is 0.0; for 1.0, output is 1.0.Final Answer:
Option A -> Option AQuick Check:
ReLU zeros negatives, keeps positives. [OK]
Hint: ReLU zeros negatives, keeps positives. [OK]
Common Mistakes:
2. Given the following Python code snippet, what will be the output if the dataset contains data leakage?
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
print(accuracy_score(y_test, y_pred))easy
Solution
Step 1: Understand the impact of data leakage
Data leakage often results in overly optimistic performance metrics.Step 2: Analyze the code behavior
The code will execute without errors, but if leakage exists, the model will perform better than it should.Final Answer:
Option D -> Option DQuick Check:
Leakage inflates accuracy [OK]
Hint: Leakage inflates accuracy [OK]
Common Mistakes:
3. In the context of Word2Vec, which of the following best describes the purpose of the 'skip-gram' model?
easy
Solution
Step 1: Understand skip-gram model purpose
The skip-gram model in Word2Vec is designed to predict surrounding words given a target word.Step 2: Eliminate wrong options
Predicts the next word in a sequence given the previous words. describes a language model task, Clusters words into predefined categories. is about clustering, and Generates a sequence of words from a given context. is about sequence generation, none of which are the purpose of skip-gram.Final Answer:
Option D -> Option DQuick Check:
Skip-gram predicts context from target [OK]
Hint: Skip-gram predicts context from target [OK]
Common Mistakes:
4. Given the following Python code snippet, what will be the output?
import numpy as np
business_goals = np.array(['increase sales', 'reduce churn', 'improve satisfaction'])
ml_problems = np.array(['classification', 'regression', 'clustering'])
result = np.char.add(business_goals, ml_problems)
print(result)easy
Solution
Step 1: Understand the operation
The np.char.add function concatenates corresponding elements of two arrays element-wise.Step 2: Eliminate wrong options
['increase sales', 'reduce churn', 'improve satisfaction'] and C are incorrect because they ignore the concatenation operation. Throws an error due to incompatible types is incorrect because np.char.add handles string arrays correctly.Final Answer:
Option A -> Option AQuick Check:
np.char.add concatenates strings element-wise. [OK]
Hint: np.char.add concatenates strings element-wise. [OK]
Common Mistakes:
5. In a CI/CD pipeline for ML, when should you prefer using a canary deployment over a blue-green deployment?
medium
Solution
Step 1: Understand deployment strategies.
Canary deployment gradually rolls out changes to a small subset, while blue-green switches between two environments.Step 2: Match scenarios to strategies.
Canary is ideal for testing on a small subset (B), blue-green is for zero downtime (A) and quick rollback (D).Final Answer:
Option D -> Option DQuick Check:
Canary = gradual rollout. [OK]
Hint: Canary = gradual rollout. [OK]
Common Mistakes: