- A
Increase the maximum number of training jobs
Why wrong: More jobs may not improve results if the search space is too broad and the tuner has already explored well.
- B
Increase the number of parallel training jobs
Why wrong: Increasing parallelism may explore more points but does not guarantee escaping a plateau; it also increases cost.
- C
Decrease the range of hyperparameters to focus on promising areas
Narrowing the search space concentrates trials in regions that previously yielded lower RMSE, potentially finding better values.
- D
Switch the objective metric to mean absolute error (MAE)
Why wrong: Changing the objective metric changes the goal, but the team wants to minimize RMSE, so this does not help.
Question 549 of 1,000
MLA-C01 ML Model Development Practice Question
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 Automatic Model Tuning to optimize a regression model. They want to minimize the root mean squared error (RMSE). The tuner has completed 20 jobs, and the RMSE has plateaued. Which action should the data scientist take to potentially improve the results?
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
✓
Decrease the range of hyperparameters to focus on promising areas
Reducing the search space can help the tuner focus on more promising regions. Increasing parallelism or max jobs may explore the same plateau, while switching to a different algorithm altogether might not be necessary.
Key principle: OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
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 maximum number of training jobs
Why it's wrong here
More jobs may not improve results if the search space is too broad and the tuner has already explored well.
- ✗
Increase the number of parallel training jobs
Why it's wrong here
Increasing parallelism may explore more points but does not guarantee escaping a plateau; it also increases cost.
- ✓
Decrease the range of hyperparameters to focus on promising areas
Why this is correct
Narrowing the search space concentrates trials in regions that previously yielded lower RMSE, potentially finding better values.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
OSPF neighbours must agree on key parameters.
- ✗
Switch the objective metric to mean absolute error (MAE)
Why it's wrong here
Changing the objective metric changes the goal, but the team wants to minimize RMSE, so this does not help.
Common exam traps
Common exam trap: OSPF can fail even when IP connectivity looks correct
OSPF neighbour formation depends on matching areas, timers, network type, authentication and passive-interface behaviour. Do not choose an answer only because the devices can ping.
Detailed technical explanation
How to think about this question
OSPF questions usually test the details that control adjacency and route selection. Read the neighbour state, area, router ID and interface configuration before deciding what is wrong.
KKey Concepts to Remember
- OSPF neighbours must agree on key parameters.
- Router ID selection can affect neighbour relationships and LSDB output.
- OSPF cost influences the preferred path.
- A route can appear in OSPF information but not become the installed route.
TExam Day Tips
- Check area mismatch first when OSPF adjacency fails.
- Review passive interfaces when a network is advertised but no neighbour forms.
- Use show ip ospf neighbor and show ip route clues carefully.
Key takeaway
OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
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.
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related MLA-C01 OSPF questions on adjacency and route selection.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
ML Model Development — This question tests ML Model Development — OSPF neighbours must agree on key parameters..
What is the correct answer to this question?
The correct answer is: Decrease the range of hyperparameters to focus on promising areas — Reducing the search space can help the tuner focus on more promising regions. Increasing parallelism or max jobs may explore the same plateau, while switching to a different algorithm altogether might not be necessary.
What should I do if I get this MLA-C01 question wrong?
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related MLA-C01 OSPF questions on adjacency and route selection.
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?
OSPF neighbours must agree on key parameters.
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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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