The answer is to add more relevant features, as this directly addresses the root cause of a high RMSE by improving regression model performance through feature engineering. When a regression model underfits the data, as indicated by a large error metric like 50,000, the most systematic fix is to introduce new, predictive variables that capture additional patterns and relationships the current model misses. On the AWS Certified AI Practitioner AIF-C01 exam, this question tests your understanding that model improvement begins with data quality and feature richness, not with changing evaluation metrics or applying scaling alone—common traps where candidates confuse symptom management with root-cause fixes. Remember that RMSE reflects how well your features explain variance; if it’s high, your feature set is likely too sparse. Memory tip: “High RMSE? Add more keys to the lock—features unlock patterns.”
AIF-C01 Fundamentals of AI and ML Practice Question
This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 has built a regression model to predict house prices. The RMSE is 50,000 on the test set. Which action is most appropriate to improve model performance?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Add more relevant features
Option C is correct because adding relevant features can capture more patterns and improve predictive accuracy. Using a different metric (A) does not improve the model. Removing outliers (B) may help if outliers exist, but adding features is generally a more systematic improvement. Feature scaling (D) helps some algorithms but may not be the primary issue.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Remove outliers from training data
Why it's wrong here
Removing outliers can help if they are erroneous, but may not guarantee improvement and could lose information.
✗
Apply feature scaling
Why it's wrong here
Feature scaling is important for gradient-based algorithms but not a general fix for high RMSE.
✓
Add more relevant features
Why this is correct
Adding informative features can reduce bias and improve model accuracy.
Related concept
Static NAT maps one inside address to one outside address.
✗
Use a different evaluation metric
Why it's wrong here
Changing the metric does not change the model's predictions.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. 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 the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AIF-C01 NAT questions on configuration and troubleshooting.
Fundamentals of AI and ML — This question tests Fundamentals of AI and ML — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Add more relevant features — Option C is correct because adding relevant features can capture more patterns and improve predictive accuracy. Using a different metric (A) does not improve the model. Removing outliers (B) may help if outliers exist, but adding features is generally a more systematic improvement. Feature scaling (D) helps some algorithms but may not be the primary issue.
What should I do if I get this AIF-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AIF-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Question Discussion
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