- A
Precision
Why wrong: Precision may be high but recall low; alone it doesn't capture overall performance.
- B
Accuracy
Why wrong: Accuracy can be high even if the model predicts only the majority class, misleading in imbalanced datasets.
- C
Recall
Why wrong: Recall may be high but precision low; alone it's not sufficient.
- D
AUC
AUC measures ranking quality and is insensitive to class imbalance.
Question 977 of 1,000
MLA-C01 ML Model Development Practice Question
This MLA-C01 practice question tests your understanding of ml model development. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 data scientist needs to evaluate a binary classification model. The dataset is highly imbalanced (5% positive class). Which metric is MOST appropriate for assessing model performance?
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
✓
AUC
AUC (Area Under the ROC Curve) is robust to class imbalance as it evaluates the model's ability to rank positive vs negative examples. Precision, recall, and F1 can be misleading if not threshold-optimized.
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.
- ✗
Precision
Why it's wrong here
Precision may be high but recall low; alone it doesn't capture overall performance.
- ✗
Accuracy
Why it's wrong here
Accuracy can be high even if the model predicts only the majority class, misleading in imbalanced datasets.
- ✗
Recall
Why it's wrong here
Recall may be high but precision low; alone it's not sufficient.
- ✓
AUC
Why this is correct
AUC measures ranking quality and is insensitive to class imbalance.
Related concept
OSPF neighbours must agree on key parameters.
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 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. OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough. 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.
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: AUC — AUC (Area Under the ROC Curve) is robust to class imbalance as it evaluates the model's ability to rank positive vs negative examples. Precision, recall, and F1 can be misleading if not threshold-optimized.
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.
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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