This MLS-C01 practice question tests your understanding of modeling. 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 data scientist is training a classification model on a dataset with missing values in several features. The data scientist wants to use SageMaker to train the model. Which TWO approaches can the data scientist use to handle missing data within the SageMaker training pipeline? (Choose two.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use the SageMaker built-in XGBoost algorithm, which can handle missing values by default.
Option A is correct because the SageMaker built-in XGBoost algorithm has a built-in mechanism to handle missing values by default. It learns the best direction (left or right branch) to route missing values during training, so no explicit imputation is needed. This makes it a seamless choice for datasets with missing data within the SageMaker training pipeline.
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.
✓
Use the SageMaker built-in XGBoost algorithm, which can handle missing values by default.
Why this is correct
XGBoost has built-in support for missing values.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use the SageMaker BlazingText algorithm, which automatically imputes missing values.
Why it's wrong here
BlazingText is for text data and does not handle missing values.
✗
Use SageMaker Inference Pipeline to handle missing values at inference time.
Why it's wrong here
Inference pipelines are for deployment, not for training data preprocessing.
✓
Use SageMaker Processing to run a custom Python script that imputes missing values before training.
Why this is correct
SageMaker Processing allows custom preprocessing.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use SageMaker PCA algorithm, which automatically handles missing values.
Why it's wrong here
PCA does not handle missing values automatically.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume all SageMaker built-in algorithms automatically handle missing values, but only XGBoost does; BlazingText and PCA require complete data, and Inference Pipeline is for serving, not training.
Detailed technical explanation
How to think about this question
XGBoost's default handling of missing values works by learning a sparse-aware split direction during training; for each split, the algorithm assigns missing values to the branch that minimizes the loss, effectively treating missingness as a feature. SageMaker Processing runs a managed Spark or Scikit-learn script on a temporary cluster, allowing custom imputation logic (e.g., mean, median, or KNN imputation) before training. This is critical in real-world scenarios where missing data patterns are non-random and require domain-specific imputation strategies.
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 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. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use the SageMaker built-in XGBoost algorithm, which can handle missing values by default. — Option A is correct because the SageMaker built-in XGBoost algorithm has a built-in mechanism to handle missing values by default. It learns the best direction (left or right branch) to route missing values during training, so no explicit imputation is needed. This makes it a seamless choice for datasets with missing data within the SageMaker training pipeline.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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