- A
Random search
Why wrong: Random search is more efficient than grid search but still does not use past results to guide search.
- B
Bayesian optimization
Bayesian optimization uses past evaluations to focus on promising regions, reducing training time.
- C
Grid search
Why wrong: Grid search exhaustively evaluates all combinations, which is time-consuming.
- D
Manual tuning
Why wrong: Manual tuning relies on trial and error, often less efficient than automated methods.
Question 772 of 1,000
mediumMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A team is using Amazon SageMaker to train a…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 team is using Amazon SageMaker to train a neural network. They want to minimize training time while effectively exploring the hyperparameter space. Which approach should they use?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
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
✓
Bayesian optimization
Bayesian optimization is the correct approach because it builds a probabilistic model of the objective function and uses it to select the most promising hyperparameters to evaluate next, balancing exploration and exploitation. This method converges to optimal hyperparameters in fewer iterations than random or grid search, significantly reducing training time for expensive neural network models.
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.
- ✗
Random search
Why it's wrong here
Random search is more efficient than grid search but still does not use past results to guide search.
- ✓
Bayesian optimization
Why this is correct
Bayesian optimization uses past evaluations to focus on promising regions, reducing training time.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Grid search
Why it's wrong here
Grid search exhaustively evaluates all combinations, which is time-consuming.
- ✗
Manual tuning
Why it's wrong here
Manual tuning relies on trial and error, often less efficient than automated methods.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that random search is always the best for hyperparameter tuning, but the question explicitly asks to minimize training time, which favors Bayesian optimization's efficient use of prior evaluations.
Detailed technical explanation
How to think about this question
Bayesian optimization in SageMaker uses a Gaussian process or tree-structured Parzen estimator (TPE) to model the objective function. It selects the next hyperparameter set by maximizing an acquisition function (e.g., Expected Improvement), which quantifies the potential gain over the current best result. In practice, this approach can reduce the number of training jobs by 50-80% compared to grid search, especially when training deep neural networks on large datasets.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Bayesian optimization — Bayesian optimization is the correct approach because it builds a probabilistic model of the objective function and uses it to select the most promising hyperparameters to evaluate next, balancing exploration and exploitation. This method converges to optimal hyperparameters in fewer iterations than random or grid search, significantly reducing training time for expensive neural network models.
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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
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.
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