This MLA-C01 practice question tests your understanding of ml model development. 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 neural network for image classification. The training job is taking too long. The team wants to reduce training time without sacrificing model accuracy. Which approach should they recommend?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a GPU-based instance such as ml.p3.2xlarge
Option B is correct because GPU-based instances like ml.p3.2xlarge are specifically designed for parallel processing of matrix operations, which are fundamental to neural network training. By offloading compute-intensive tensor operations to GPU cores, training time can be significantly reduced without altering the model architecture or data, thus preserving accuracy.
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 batch size to the maximum possible
Why it's wrong here
Very large batch sizes can degrade model accuracy and may not fit in memory.
✓
Use a GPU-based instance such as ml.p3.2xlarge
Why this is correct
GPUs accelerate matrix operations in neural networks, reducing training time.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a learning rate scheduler that reduces the learning rate over time
Why it's wrong here
Schedulers help convergence but do not directly reduce training time.
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that any change to hyperparameters or architecture can reduce training time without side effects, but the trap here is that candidates confuse 'reducing training time' with 'improving convergence speed'—only hardware acceleration (GPU) directly reduces wall-clock time without risking accuracy degradation.
Detailed technical explanation
How to think about this question
Under the hood, GPU instances like ml.p3.2xlarge leverage NVIDIA V100 Tensor Cores that perform mixed-precision matrix multiplications (FP16/FP32) via CUDA cores, achieving up to 125 TFLOPS for deep learning workloads. In practice, for image classification tasks using CNNs, switching from a CPU-based instance (e.g., ml.c5.xlarge) to a GPU instance can reduce training time by 10x–50x, depending on batch size and model depth, while maintaining identical accuracy because the underlying algorithm (e.g., stochastic gradient descent) remains unchanged.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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.
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a GPU-based instance such as ml.p3.2xlarge — Option B is correct because GPU-based instances like ml.p3.2xlarge are specifically designed for parallel processing of matrix operations, which are fundamental to neural network training. By offloading compute-intensive tensor operations to GPU cores, training time can be significantly reduced without altering the model architecture or data, thus preserving accuracy.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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 →
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.
This MLA-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 MLA-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.