- A
Increase the learning rate significantly
Why wrong: High learning rate may cause divergence.
- B
Use the 'mode' hyperparameter set to 'batch_skipgram' instead of 'supervised'
Why wrong: This changes to unsupervised, not solving classification.
- C
Use SageMaker Managed Spot Training
Spot instances reduce cost, allowing more resources for same budget.
- D
Use a larger instance type, such as ml.m4.4xlarge
Why wrong: Larger instance increases cost.
Question 1,435 of 1,755
Reduce Training Time in SageMaker
This MLS-C01 practice question tests your understanding of modeling. 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 text classification model using a built-in BlazingText algorithm. The dataset has 500,000 documents, each labeled with one of 10 categories. The training time is taking longer than expected. The data scientist wants to speed up training without increasing cost. The training job is using a single ml.m4.xlarge instance. The code uses default hyperparameters. Which change is MOST likely to reduce training time? A. Use a larger instance type, such as ml.m4.4xlarge. B. Increase the learning rate significantly. C. Use SageMaker Managed Spot Training. D. Use the 'mode' hyperparameter set to 'batch_skipgram' instead of 'supervised'. The company has a fixed budget and wants to minimize cost while reducing training time. Which option should the data scientist choose?
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.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
✓
Use SageMaker Managed Spot Training
Option C is the best because SageMaker Managed Spot Training uses Spot Instances which are significantly cheaper than On-Demand instances. This cost saving allows the data scientist to launch multiple training instances in parallel within the same fixed budget, thereby reducing training time. While other options either increase cost, risk poor convergence, or change the task to unsupervised, Spot Training provides a cost-effective way to accelerate training without altering the algorithm or instance type.
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.
- ✗
Increase the learning rate significantly
Why it's wrong here
High learning rate may cause divergence.
- ✗
Use the 'mode' hyperparameter set to 'batch_skipgram' instead of 'supervised'
Why it's wrong here
This changes to unsupervised, not solving classification.
- ✓
Use SageMaker Managed Spot Training
Why this is correct
Spot instances reduce cost, allowing more resources for same budget.
Clue confirmation
The clue words "most likely", "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a larger instance type, such as ml.m4.4xlarge
Why it's wrong here
Larger instance increases cost.
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
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use SageMaker Managed Spot Training — Option C is the best because SageMaker Managed Spot Training uses Spot Instances which are significantly cheaper than On-Demand instances. This cost saving allows the data scientist to launch multiple training instances in parallel within the same fixed budget, thereby reducing training time. While other options either increase cost, risk poor convergence, or change the task to unsupervised, Spot Training provides a cost-effective way to accelerate training without altering the algorithm or instance type.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Are there clue words in this question I should notice?
Yes — watch for: "most likely", "minimum / minimize". 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 →
Same concept, more angles
1 more ways this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company is using SageMaker to train a TensorFlow model for image classification. The training is slow on a single GPU instance. Which TWO strategies can reduce training time? (Choose TWO.)
hard- A.Increase the image size
- ✓ B.Use SageMaker Pipe Mode for data ingestion
- C.Increase the number of training epochs
- ✓ D.Use distributed training with multiple GPUs
- E.Decrease the batch size
Why B: Options B and D are correct. SageMaker Pipe Mode streams training data directly from S3, reducing data download time. Distributed training with multiple GPUs parallelizes the computation, speeding up training. Option A (increase image size) increases computational load and slows training. Option C (increase epochs) adds more iterations, increasing time. Option E (decrease batch size) can reduce GPU utilization efficiency, often slowing training.
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Last reviewed: Jun 20, 2026
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