← MLS-C01·Amazon Web Services

Question 405 of 1,755

Modeling →hardMultiple ChoiceObjective-mapped

Quick Answer

The correct approach is to use a gradient boosting model with ordinal encoding for categorical features and deploy SageMaker’s built-in XGBoost with SHAP. This combination directly addresses the challenge of handling high cardinality categorical features with XGBoost and SHAP because ordinal encoding maps each category to a unique integer without expanding the feature space, keeping the model lightweight for real-time inference. XGBoost’s tree-based structure naturally handles ordinal-encoded high-cardinality inputs, while SHAP provides consistent, model-agnostic explanations for individual predictions, satisfying regulatory compliance. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of trade-offs between encoding strategies and inference latency—common traps include choosing one-hot encoding (which explodes dimensionality and slows predictions) or using linear models that struggle with high cardinality. Remember the memory tip: “Ordinal for trees, SHAP for compliance, SageMaker for speed.”

MLS-C01 Modeling Practice Question

This MLS-C01 practice question tests your understanding of modeling. 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 bank is building a credit risk model using a large dataset with 500 features and 2 million samples. The dataset contains many categorical features with high cardinality (e.g., zip code, occupation). The model must be deployed on SageMaker and provide real-time predictions with low latency. They also need to explain individual predictions for regulatory compliance. Which approach is most appropriate?

Question 1hardmultiple choice

Read the full NAT/PAT explanation →

A
Use a linear model with target encoding for categorical features and deploy with SageMaker's built-in linear learner algorithm
Why wrong: Linear models may not capture complex interactions.
B
Use a deep neural network with embedding layers for categorical features and use SageMaker's built-in Debugger for explanations
Why wrong: Deep networks may be slower and Debugger is for training, not inference explanations.
C
Use XGBoost with one-hot encoding for categorical features and deploy with SageMaker's built-in SHAP explainer
Why wrong: One-hot encoding on high cardinality features creates too many features, increasing latency.
D
Use a gradient boosting model with ordinal encoding for categorical features and use SageMaker's built-in XGBoost with SHAP
Ordinal encoding handles high cardinality without explosion; XGBoost captures interactions; SHAP provides explanations.

Full breakdown with real-world context →

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

✓

Use a gradient boosting model with ordinal encoding for categorical features and use SageMaker's built-in XGBoost with SHAP

XGBoost with ordinal encoding and SHAP balances performance, latency, and explainability.

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.

✗
Use a linear model with target encoding for categorical features and deploy with SageMaker's built-in linear learner algorithm
Why it's wrong here
Linear models may not capture complex interactions.
✗
Use a deep neural network with embedding layers for categorical features and use SageMaker's built-in Debugger for explanations
Why it's wrong here
Deep networks may be slower and Debugger is for training, not inference explanations.
✗
Use XGBoost with one-hot encoding for categorical features and deploy with SageMaker's built-in SHAP explainer
Why it's wrong here
One-hot encoding on high cardinality features creates too many features, increasing latency.
✓
Use a gradient boosting model with ordinal encoding for categorical features and use SageMaker's built-in XGBoost with SHAP
Why this is correct
Ordinal encoding handles high cardinality without explosion; XGBoost captures interactions; SHAP provides explanations.
Related concept
Static NAT maps one inside address to one outside address.

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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.

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 MLS-C01 NAT questions on configuration and troubleshooting.

Related MLS-C01 practice-question pages

Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.

Data Engineering practice questions

Practise MLS-C01 questions linked to Data Engineering.

Machine Learning Implementation and Operations practice questions

Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.

Modeling practice questions

Practise MLS-C01 questions linked to Modeling.

Exploratory Data Analysis practice questions

Practise MLS-C01 questions linked to Exploratory Data Analysis.

MLS-C01 fundamentals practice questions

Practise MLS-C01 questions linked to MLS-C01 fundamentals.

MLS-C01 scenario practice questions

Practise MLS-C01 questions linked to MLS-C01 scenario.

MLS-C01 troubleshooting practice questions

Practise MLS-C01 questions linked to MLS-C01 troubleshooting.

Practice this exam

Start a free MLS-C01 practice session

Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.

10 questions 20 questions 30 questions 50 questions Timed 30

MLS-C01 practice-test guide →Study guide →Browse all practice tests

FAQ

Questions learners often ask

What does this MLS-C01 question test?

Modeling — This question tests Modeling — Static NAT maps one inside address to one outside address..

What is the correct answer to this question?

The correct answer is: Use a gradient boosting model with ordinal encoding for categorical features and use SageMaker's built-in XGBoost with SHAP — XGBoost with ordinal encoding and SHAP balances performance, latency, and explainability.

What should I do if I get this MLS-C01 question wrong?

What is the key concept behind this question?

Static NAT maps one inside address to one outside address.

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 →

How Courseiva writes practice questions · Editorial policy

Keep practising

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.

Loading comments…

This MLS-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 MLS-C01 exam.