- A
Reduce the early stopping tolerance
Why wrong: Tighter tolerance may cause early stopping before convergence.
- B
Increase the maximum runtime
Why wrong: Runtime does not affect model accuracy.
- C
Add feature crosses or polynomial features
Linear models benefit from feature engineering to capture non-linear relationships.
- D
Increase the number of training instances
Why wrong: More instances speed up training but do not improve model performance if underfitting.
Question 892 of 1,755
Improving Underfitting in SageMaker Linear Learner
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. 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.
A company is using SageMaker to train a linear learner algorithm. The training log shows that the algorithm converges but the final loss is still high. Which change is most likely to improve the model?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
✓
Add feature crosses or polynomial features
A high final loss despite convergence indicates that the model is underfitting — the linear decision boundary is too simple to capture the underlying patterns in the data. Adding feature crosses or polynomial features increases the model's expressiveness by introducing non-linear interactions, allowing the linear learner to fit more complex relationships and reduce the loss.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the early stopping tolerance
Why it's wrong here
Tighter tolerance may cause early stopping before convergence.
- ✗
Increase the maximum runtime
Why it's wrong here
Runtime does not affect model accuracy.
- ✓
Add feature crosses or polynomial features
Why this is correct
Linear models benefit from feature engineering to capture non-linear relationships.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the number of training instances
Why it's wrong here
More instances speed up training but do not improve model performance if underfitting.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse convergence (the optimization stopping) with a good model, overlooking that a linear model can converge to a high-loss minimum if the data is non-linear — the fix is feature engineering, not hyperparameter tuning or more data.
Detailed technical explanation
How to think about this question
Linear learner in SageMaker uses a distributed version of stochastic gradient descent (SGD) with L1/L2 regularization. When the loss plateaus at a high value, it often means the model's hypothesis space (linear combinations of original features) cannot represent the target function. Feature crosses (e.g., x1*x2) or polynomial expansions (e.g., x1^2, x2^2) implicitly map the input into a higher-dimensional space where linear separability improves, directly addressing underfitting without changing the algorithm's core optimization.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
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
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Machine Learning Implementation and Operations — study guide chapter
Learn the concepts, then practise the questions
- →
Machine Learning Implementation and Operations practice questions
Targeted practice on this topic area only
- →
All MLS-C01 questions
1,755 questions across all exam domains
- →
AWS Certified Machine Learning Specialty MLS-C01 study guide
Full concept coverage aligned to exam objectives
- →
MLS-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related MLS-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Data Engineering practice questions
Practise MLS-C01 questions linked to Data Engineering.
Machine Learning Implementation and Operations practice questions
Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.
Modeling practice questions
Practise MLS-C01 questions linked to Modeling.
Exploratory Data Analysis practice questions
Practise MLS-C01 questions linked to Exploratory Data Analysis.
MLS-C01 fundamentals practice questions
Practise MLS-C01 questions linked to MLS-C01 fundamentals.
MLS-C01 scenario practice questions
Practise MLS-C01 questions linked to MLS-C01 scenario.
MLS-C01 troubleshooting practice questions
Practise MLS-C01 questions linked to MLS-C01 troubleshooting.
Practice this exam
Start a free MLS-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Add feature crosses or polynomial features — A high final loss despite convergence indicates that the model is underfitting — the linear decision boundary is too simple to capture the underlying patterns in the data. Adding feature crosses or polynomial features increases the model's expressiveness by introducing non-linear interactions, allowing the linear learner to fit more complex relationships and reduce the loss.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
More MLS-C01 practice questions
- A company needs to transfer 10 TB of data from an on-premises data center to Amazon S3. The network bandwidth is limited…
- A company is using Amazon Kinesis Data Streams to ingest real-time clickstream data. The data is consumed by a Lambda fu…
- A team is building a data pipeline to process terabytes of log data daily using Amazon EMR. The data arrives in 5-minute…
- A data science team is building a real-time fraud detection system. Transactions are streamed via Amazon Kinesis Data St…
- A company uses Amazon SageMaker to train and deploy machine learning models. The training data is stored in Amazon S3 (P…
- A data engineering team is designing a data lake on AWS for machine learning workloads. The data includes structured, se…
Last reviewed: Jul 4, 2026
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.