- A
L1 Regularization: Encourages sparsity by setting some coefficients to zero
L1 regularization (Lasso) adds a penalty proportional to the absolute value of coefficients, driving some to zero.
- B
L2 Regularization: Shrinks coefficients to prevent overfitting
L2 regularization (Ridge) adds a penalty proportional to the square of coefficients, shrinking them without forcing zero.
- C
Dropout: Randomly drops neurons during training to prevent co-adaptation
Dropout is a stochastic regularization technique that randomly deactivates a fraction of neurons each iteration.
- D
Early Stopping: Halts training when validation performance stops improving
Early stopping monitors validation metrics and stops training when no improvement is seen for a set number of epochs.
- E
L1 Regularization: Shrinks coefficients uniformly
Why wrong: Incorrect — L1 does not shrink uniformly; it can zero out coefficients. This description matches L2 regularization.
- F
Dropout: Adds penalty to the loss function
Why wrong: Incorrect — dropout does not modify the loss function; it modifies the network architecture during training. Penalty-based methods are L1/L2.
Regularization Techniques
This PMLE practice question tests your understanding of collaborating to manage data and models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: l1 Regularization. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Match each regularization technique to its effect.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
L1 Regularization: Encourages sparsity by setting some coefficients to zero
This question matches regularization techniques to their effects. L1 regularization (option A) encourages sparsity by setting some coefficients to zero, which is correct. Option E incorrectly states L1 shrinks coefficients uniformly; that is L2 regularization. L2 regularization (option B) shrinks coefficients to prevent overfitting, which is correct. Dropout (option C) randomly drops neurons during training to prevent co-adaptation, which is correct. Option F incorrectly states dropout adds penalty to the loss function; that describes regularization like L1/L2. Early stopping (option D) halts training when validation performance stops improving, which is correct.
Key principle: L1 Regularization
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
L1 Regularization: Encourages sparsity by setting some coefficients to zero
Why this is correct
L1 regularization (Lasso) adds a penalty proportional to the absolute value of coefficients, driving some to zero.
Related concept
L1 Regularization
- ✓
L2 Regularization: Shrinks coefficients to prevent overfitting
Why this is correct
L2 regularization (Ridge) adds a penalty proportional to the square of coefficients, shrinking them without forcing zero.
Related concept
L1 Regularization
- ✓
Dropout: Randomly drops neurons during training to prevent co-adaptation
Why this is correct
Dropout is a stochastic regularization technique that randomly deactivates a fraction of neurons each iteration.
Related concept
L1 Regularization
- ✓
Early Stopping: Halts training when validation performance stops improving
Why this is correct
Early stopping monitors validation metrics and stops training when no improvement is seen for a set number of epochs.
Related concept
L1 Regularization
- ✗
L1 Regularization: Shrinks coefficients uniformly
Why it's wrong here
Incorrect — L1 does not shrink uniformly; it can zero out coefficients. This description matches L2 regularization.
- ✗
Dropout: Adds penalty to the loss function
Why it's wrong here
Incorrect — dropout does not modify the loss function; it modifies the network architecture during training. Penalty-based methods are L1/L2.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- L1 Regularization
- L2 Regularization
- Dropout
- Early Stopping
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
L1 Regularization
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. L1 Regularization Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Review l1 Regularization, then practise related PMLE questions on the same topic to reinforce the concept.
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Collaborating to manage data and models — study guide chapter
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FAQ
Questions learners often ask
What does this PMLE question test?
Collaborating to manage data and models — This question tests Collaborating to manage data and models — L1 Regularization.
What is the correct answer to this question?
The correct answer is: L1 Regularization: Encourages sparsity by setting some coefficients to zero — This question matches regularization techniques to their effects. L1 regularization (option A) encourages sparsity by setting some coefficients to zero, which is correct. Option E incorrectly states L1 shrinks coefficients uniformly; that is L2 regularization. L2 regularization (option B) shrinks coefficients to prevent overfitting, which is correct. Dropout (option C) randomly drops neurons during training to prevent co-adaptation, which is correct. Option F incorrectly states dropout adds penalty to the loss function; that describes regularization like L1/L2. Early stopping (option D) halts training when validation performance stops improving, which is correct.
What should I do if I get this PMLE question wrong?
Review l1 Regularization, then practise related PMLE questions on the same topic to reinforce the concept.
What is the key concept behind this question?
L1 Regularization
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Last reviewed: Jun 11, 2026
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